From 4974b9b07f19d023bca8c719db743d29ea3d09fc Mon Sep 17 00:00:00 2001
From: hdolinh <hdolinh@users.noreply.github.com>
Date: Mon, 21 Aug 2023 01:30:03 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20NCEAS/re?=
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---
 2023-08-usgs/images/logic-model-template.png | Bin 0 -> 202847 bytes
 2023-08-usgs/search.json                     |  40 ++---
 2023-08-usgs/session_01.html                 |  24 +--
 2023-08-usgs/session_02.html                 | 163 ++++++++++++-------
 4 files changed, 138 insertions(+), 89 deletions(-)
 create mode 100644 2023-08-usgs/images/logic-model-template.png

diff --git a/2023-08-usgs/images/logic-model-template.png b/2023-08-usgs/images/logic-model-template.png
new file mode 100644
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diff --git a/2023-08-usgs/search.json b/2023-08-usgs/search.json
index 6bc1fd3c..9228a050 100644
--- a/2023-08-usgs/search.json
+++ b/2023-08-usgs/search.json
@@ -53,42 +53,42 @@
     "href": "session_01.html#what-is-synthesis-science",
     "title": "1  Successful Synthesis Science",
     "section": "1.1 What is Synthesis Science?",
-    "text": "1.1 What is Synthesis Science?\n\n\n\nGraphic by Judith Sitters\n\n\n\n\n\n\n\n\nSynthesis Science in Ecology\n\n\n\nSynthesis science is the process of distilling existing data, ideas, theories, or methods drawn from many sources, across multiple fields of inquiry, to accelerate the generation of new scientific knowledge, often at a broad scale.\n\n\nTo break that down further, think of it this way: synthesis science utilizes existing data and knowledge to generate new inights. It’s like putting together puzzle pieces from different sources to create a bigger picture.\nSynthesis science is fundamentally different than other types of academic research in that it does not involve collecting new or original data to conduct research or to answer a research question.\nSince the ecological and environmental sciences are complex and often interdisciplinary, synthesis approaches are ideal for “understanding complexity across scales, leveraging data from various disciplines, facilitating discovery of general patterns in natural systems, and informing policy” (Halpern et al. 2023) in the ecological and environmental sciences.\n\n1.1.1 The Value and Impact of Synthesis Research\n\n\n\nGraphic adapted from Engingeering Ideas newsletter on Substack\n\n\nData is everywhere and information is more accessible than ever, but how can we leverage both data and knowledge to further our understanding of a topic and turn those insights into meaningful action? This is where synthesis research shines.\nThere are many studies that value the importance of synthesis for varying reasons including:\n\nElevating scientific research as evidence that is influential in policy-making (Dicks, Walsh, and Sutherland 2014)\nAddressing the challenge of “information overload” (Hampton and Parker 2011)\nDelivering research products that enhance scientific knowledge in decision-making (Hampton and Parker 2011)\nProviding critical knowledge to solve environmental issues (Carpenter et al. 2009)\nInitiating new collaborations and new insights across disciplines and non-academic sectors (Hampton and Parker 2011)\n\nAs synthesis science and research synthesis approaches have been gaining prominenance in the ecological and environmental fields, researchers have sought to understand the impacts of synthesis. In particular, Wyborn et al. (2018) identified the following impacts of research synthesis:\n\nKnowledge is used to change understanding of a system or phenomena\nKnowledge changes understanding of the interconnections between actors, sectors or systems\nKnowledge is used to support a particular view of a problem\nKnowledge is used as the prime source of information to inform policy change or reform\nKnowledge use is dependent on the capacity to understand or apply knowledge in a given context"
+    "text": "1.1 What is Synthesis Science?\n\n\n\nGraphic by Judith Sitters\n\n\n\n\n\n\n\n\nSynthesis Science in Ecological and Environmental Sciences\n\n\n\nSynthesis science is the process of distilling existing data, ideas, theories, or methods drawn from many sources, across multiple fields of inquiry, to accelerate the generation of new scientific knowledge, often at a broad scale.\n\n\nTo break that down further, think of it this way: synthesis science utilizes existing data and knowledge to generate new inights. It’s like putting together puzzle pieces from different sources to create a bigger picture.\nSynthesis science is fundamentally different than other types of academic research in that it does not involve collecting new or original data to conduct research or to answer a research question.\nSince the ecological and environmental sciences are complex and often interdisciplinary, synthesis approaches are ideal for “understanding complexity across scales, leveraging data from various disciplines, facilitating discovery of general patterns in natural systems, and informing policy” (Halpern et al. 2023) in the ecological and environmental sciences.\n\n1.1.1 The Value and Impact of Synthesis Research\n\n\n\nGraphic adapted from Engingeering Ideas newsletter on Substack\n\n\nData is everywhere and information is more accessible than ever, but how can we leverage both data and knowledge to further our understanding of a topic and turn those insights into meaningful action? This is where synthesis research shines.\nThere are many studies that value the importance of synthesis for varying reasons including:\n\nElevating scientific research as evidence that is influential in policy-making (Dicks, Walsh, and Sutherland 2014)\nAddressing the challenge of “information overload” (Hampton and Parker 2011)\nDelivering research products that enhance scientific knowledge in decision-making (Hampton and Parker 2011)\nProviding critical knowledge to solve environmental issues (Carpenter et al. 2009)\nInitiating new collaborations and new insights across disciplines and non-academic sectors (Hampton and Parker 2011)\n\nAs synthesis science and research synthesis approaches have been gaining prominenance in the ecological and environmental fields, researchers have sought to understand the impacts of synthesis. In particular, Wyborn et al. (2018) identified the following types of impacts from research synthesis:\n\nKnowledge is used to change understanding of a system or phenomena\nKnowledge changes understanding of the interconnections between actors, sectors or systems\nKnowledge is used to support a particular view of a problem\nKnowledge is used as the prime source of information to inform policy change or reform\nKnowledge use is dependent on the capacity to understand or apply knowledge in a given context"
   },
   {
     "objectID": "session_01.html#research-synthesis-approaches",
     "href": "session_01.html#research-synthesis-approaches",
     "title": "1  Successful Synthesis Science",
     "section": "1.2 Research Synthesis Approaches",
-    "text": "1.2 Research Synthesis Approaches\nIn general, research synthesis approaches include utilizing existing research on a particular topic with the intention of integrating all the finding together.\nThere are multiple different types of approaches such as narrative reviews, vote counting (this quantitative method has been discredited, but you may find examples of it in your research), case studies, and more. However, systematic reviews and meta-analysis are considered by some to be the “gold standard” of research synthesis and are often used at NCEAS, which is why we’re highlighting them here.\n\n\n\n\n\n\n\nSystematic Review\nA form of synthesis to study a specific question or problem by following clear steps: finding and choosing studies, checking them carefully, and gathering information from them to create a reliable review.\nNote: Not all systematic reviews contain meta-analysis\n\n\nMeta-Analysis\nA form of synthesis that uses statistical and qunatitative methods to integrate and compare (quantitatively) results from a large number of individual studies.\n\n\n\n\n1.2.1 What about data?\nData is essential to synthesis in ecological and environmental research. Synthesizing data can involve combining and analyzing data from multiple sources to address research questions, identify patterns, and draw meaningful conclusions. This process might involve integrating data from field studies, experiments, observations, and other sources to gain a comprehensive understanding of ecological and environmental phenomena."
+    "text": "1.2 Research Synthesis Approaches\nIn general, research synthesis approaches include utilizing existing research on a particular topic with the intention of integrating all the finding together.\nThere are multiple different types of approaches such as narrative reviews, vote counting (this quantitative method has been discredited, but you may find examples of it in your research), case studies, and more. However, systematic reviews and meta-analysis are considered by some to be the “gold standard” of research synthesis and are often used at NCEAS, which is why we’re highlighting them here.\n\n\n\n\n\n\n\nSystematic Review\nA form of synthesis to study a specific question or problem by following clear steps: finding and choosing studies, checking them carefully, and gathering information from them to create a reliable review.\nNote: Not all systematic reviews contain meta-analysis\n\n\nMeta-Analysis\nA form of synthesis that uses statistical and qunatitative methods to integrate and compare (quantitatively) results from a large number of individual studies.\n\n\n\n\n1.2.1 What about data?\nData is essential to synthesis in ecological and environmental research. Synthesizing data can involve combining and analyzing data from multiple sources to address research questions, to conduct a systematic review or meta-analysis, to identify patterns, and draw meaningful conclusions that don’t appear in siloed approaches.\nAt NCEAS, our approach tends to include combining multiple datasets from various sources, then we leverage specific expertise from diverse disciplines and researchers, and together we use those two elements to unleash new insights to answer large-scale questions.\nWe often see this approach with working groups at NCEAS. Working groups are teams that include researchers, agencies and organizational practitioners, and more who come together (and typically at NCEAS, a neutral ground) for focused and intensive working sessions around their research questions."
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-    "text": "1.3 NCEAS and Synthesis Science\n\n\n\n\n\nIn 1995, the National Science Foundation (NSF) responded to the increasing calls by environmental researchers and professionals for interdisciplinary and transdisciplinary explanations, methods to connect science to applications, and ways to advance research technologies by creating NCEAS, the first synthesis center.\nThere have been multiple other synthesis centers that been established since, by each one is founded by the overall goal to use synthesis to “advance basic science, organize ecological information for decision makers concerned with pressing national issues, and make cost-effective use of the nation’s extant and accumulating database” (Hampton and Parker 2011).\nSynthesis centers are specifically designed to catalyze collaboration leading to breakthrough ideas, and one way NCEAS (among other synthesis centers) achieve this are through working groups. Since its inception, NCEAS has hosted hundreds of working groups and has supported them in varying ways including: structuring group size or composition, facilitation of working sessions, supplying computing and informatics support, offering training opporunities for data-focused team members, providing a neutral space for meetings and co-working, and more. In short, NCEAS lowers the “activation energy” for working group members so that they can focus on the cross-pollination of ideas, creative thinking, and emergent research (Rodrigo et al. 2013)."
+    "text": "1.3 NCEAS and Synthesis Science\n\n\n\n\n\nIn 1995, the National Science Foundation (NSF) responded to the increasing calls by environmental researchers and professionals for interdisciplinary and transdisciplinary explanations, methods to connect science to applications, and ways to advance research technologies by creating NCEAS, the first synthesis center.\nThere are multiple other synthesis centers that have been established since, but each one has a foundational goal to use synthesis to “advance basic science, organize ecological information for decision makers concerned with pressing national issues, and make cost-effective use of the nation’s extant and accumulating database” (Hampton and Parker 2011).\nSynthesis centers are specifically designed to catalyze collaboration leading to breakthrough ideas, and one way NCEAS (among other synthesis centers) achieve this are through working groups. Since its inception, NCEAS has hosted hundreds of working groups and has supported them in varying ways including: structuring group size or composition, facilitation of working sessions, supplying computing and informatics support, offering training opporunities for data-focused team members, providing a neutral space for meetings and co-working, and more. In short, NCEAS lowers the “activation energy” for working group members so that they can focus on the cross-pollination of ideas, creative thinking, and emergent research (Rodrigo et al. 2013)."
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-    "text": "1.4 Notable Working Groups at NCEAS\n\n1.4.1 Ocean Health Index (OHI)\n\n\n\n\n\nThe Ocean Health Index brought together a diverse group of experts including marine scientists, economists, policy experts, and more. This collaborative effort aimed to comprehensively assess the health of oceans worldwide by integrating data from various sources, including physical, biological, and socioeconomic factors.\nThe success of the Ocean Health Index highlights several key aspects of effective synthesis science in working groups:\n\nInterdisciplinary Collaboration: The working group included experts from different disciplines, fostering a holistic understanding of ocean health that goes beyond individual scientific perspectives.\nData Integration: The Index integrated data from multiple sources and scales, ranging from local to global, to provide a comprehensive view of ocean health.\nStandardized Metrics: The working group developed standardized metrics that allowed for consistent assessment and comparison of ocean health across regions and over time.\nPolicy Relevance: The synthesis findings were designed to inform policy and management decisions, demonstrating the practical applications of synthesis science in addressing real-world challenges.\nCommunication and Outreach: The Index effectively communicated its findings to a wide range of stakeholders, raising awareness about ocean health and fostering engagement in conservation efforts.\n\n\n\n1.4.2 State of Alaska’s Salmon and People (SASAP)\n\n\n\n\n\nThe SASAP project was a partnership between NCEAS and Anchorage-based Nautilus Impact Investing to create an equitable decision-making platform for salmon management through information synthesis, collaboration, and stakeholder engagement.\nThe project sought to address the need for accessible, up-to-date, and complete information about salmon and the factors shaping their future. It convened eight collaborative working groups consisting of leading experts at the University of Alaska and other universities, local indigenous leaders, and specialists across resource sectors – an intentional integration of western scientific perspectives and indigenous knowledge."
+    "section": "1.4 Working Groups at NCEAS",
+    "text": "1.4 Working Groups at NCEAS\n\n1.4.1 Ocean Health Index (OHI)\n\n\n\n\n\nThe Ocean Health Index brought together a diverse group of experts including marine scientists, economists, policy experts, and more. This collaborative effort aimed to comprehensively assess the health of oceans worldwide by integrating data from various sources, including physical, biological, and socioeconomic factors.\nThe success of the Ocean Health Index highlights several key aspects of effective synthesis science in working groups:\n\nInterdisciplinary Collaboration: The working group included experts from different disciplines, fostering a holistic understanding of ocean health that goes beyond individual scientific perspectives.\nData Integration: The Index integrated data from multiple sources and scales, ranging from local to global, to provide a comprehensive view of ocean health.\nStandardized Metrics: The working group developed standardized metrics that allowed for consistent assessment and comparison of ocean health across regions and over time.\nPolicy Relevance: The synthesis findings were designed to inform policy and management decisions, demonstrating the practical applications of synthesis science in addressing real-world challenges.\nCommunication and Outreach: The Index effectively communicated its findings to a wide range of stakeholders, raising awareness about ocean health and fostering engagement in conservation efforts.\n\n\n\n1.4.2 State of Alaska’s Salmon and People (SASAP)\n\n\n\n\n\nThe SASAP project was a partnership between NCEAS and Anchorage-based Nautilus Impact Investing to create an equitable decision-making platform for salmon management through information synthesis, collaboration, and stakeholder engagement.\nThe project sought to address the need for accessible, up-to-date, and complete information about salmon and the factors shaping their future. It convened eight collaborative working groups consisting of leading experts at the University of Alaska and other universities, local indigenous leaders, and specialists across resource sectors – an intentional integration of western scientific perspectives and indigenous knowledge."
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-    "section": "1.5 Priorities of Synthesis Here and Now",
-    "text": "1.5 Priorities of Synthesis Here and Now\nIn February 2021, NCEAS hosted a virtual workshop where 127 ecologists, environmental and natural scientists convened to discuss the future priorities of synthesis science. Participants across career stages, institutions, backgrounds, and geographies were in attendance and together they identified seven research topics, and two pressing needs for conducting research synthesis practices.\nLater in Jnauary 2023, dozens of scientists authored and published Priorities for synthesis research in ecology and environmental science the results of the workshop. We’ve summarized these results in the tables below.\n\n\n\n\n\n\n\nResearch Topic\nDescription\n\n\n\n\nDiversity, Equity, Inclusion, and Justice (DEIJ)\nEnvironmental justice shows the link between nature and fairness for people, especially those who are disadvantaged. Unequal distribution of nature due to factors like racism impacts cities and ecosystems. Integrating DEIJ into research can improve knowledge and involve underrepresented groups, while engaging diverse communities can make research more impactful and inclusive.\n\n\nHuman and Natural systems\nEnvironmental synthesis has given us valuable insights into environmental change, but it needs to also look at how human actions and decisions affect the environment. For example, decisions about things like population growth and greenhouse gas policies impact species and nature. Bringing together information from different fields is challenging, especially when considering different scales and practices. Synthesis science can help us better understand how humans and nature are connected, but it takes time and effort to combine ideas from different disciplines.\n\n\nActionable and Use-inspired science\nSynthesis science in ecology and environmental science is great for helping decision makers. Methods like systematic reviews and predictive models can simplify research for environmental policy. To be more effective, scientists need to work closely with practitioners and use different types of knowledge. Communication, teamwork, and using various knowledge sources can improve how decisions are made and carried out based on scientific findings.\n\n\nScale\nUnderstanding ecosystems across different sizes and time periods is hard for social-ecological research. Synthesis helps by combining information and methods from different scales, and connecting these findings to policy decisions. The challenges come from not having enough long-term data, difficulty mixing different information for analysis, and lack of skills and cooperation across scales. To overcome these challenges, more efforts are needed to collect, organize, and combine data over long periods and big areas.\n\n\nGenerality\nScience looks for common rules to explain how things work, but sometimes it’s hard to find them. Synthesis science can help find these general rules and figure out when they apply. Two problems are not having enough similar studies to compare and bias in what gets studied and reported. To fix this, scientists can share their work openly, use the same rules for reporting, and make sure all types of results are published. Working together across different areas can also help find these general rules.\n\n\nComplexity and Resilience\nEcosystems are very complicated, especially when considering things like society and economics. This complexity can make systems strong or weak. Using this complex view helps solve problems like water and food. But putting all this information together (synthesis) is hard because it depends on the situation, and finding general rules is tough. To make it work better, more scientists need to learn to work together across different areas and share their data. The goal is to understand how complexity affects ecosystems and find ways to help both people and the environment.\n\n\nPredictability\nThis time of big changes in the environment makes it important to predict how ecosystems will be in the future and how they’ll affect people. Predicting things in the short term (like months or years) is common in some fields but not in ecology. Some people worry that the predictions might not be perfect, but even imperfect predictions can help us learn and make better decisions. To make predictions better in ecology, scientists can work together, use standardized methods and data, and talk to the people who use the predictions. This will help us manage natural resources more wisely.\n\n\n\n\n\n\n\n\n\n\nPressing Need\nDescription\n\n\n\n\nNeed for increased participant diversity and inclusive research practices\nSynthesis science is most effective when it combines data, perspectives, and insights from different regions and ecosystems. By including diverse participants and creating inclusive research environments, we can better address important questions and find fair solutions to challenges. Ignoring diversity and inclusion can lead to biased syntheses and unfair outcomes. The large scope of synthesis science calls for involving people, ideas, and data from various backgrounds and languages. Efforts should be made to ensure representation, provide support, and promote open dialogue to increase diversity and inclusivity in synthesis research, ultimately fostering better understanding and more equitable outcomes.\n\n\nIncreased and improved data flow, access, and skill-building\nThe amount of data for synthesis science is increasing, and there are efforts to organize and combine data from various sources to answer ecological questions. Funding is needed to manage and store data properly. It’s important to think about ethical issues when using different datasets, especially if some people can’t share their data openly. Some data may be hard to access due to language or quality issues, and including diverse types of data, like traditional knowledge, can be challenging. Tools like machine learning can help make sense of different data sources. To improve data synthesis, global collaboration among diverse groups is essential.\n\n\n\n\n\n\n\nCarpenter, Stephen R., E. Virginia Armbrust, Peter W. Arzberger, F. Stuart Chapin, James J. Elser, Edward J. Hackett, Anthony R. Ives, et al. 2009. “Accelerate Synthesis in Ecology and Environmental Sciences.” BioScience 59 (8): 699–701. https://doi.org/10.1525/bio.2009.59.8.11.\n\n\nDicks, Lynn V., Jessica C. Walsh, and William J. Sutherland. 2014. “Organising Evidence for Environmental Management Decisions: A ‘4S’ Hierarchy.” Trends in Ecology & Evolution 29 (11): 607–13. https://doi.org/10.1016/j.tree.2014.09.004.\n\n\nHalpern, Benjamin S., Carl Boettiger, Michael C. Dietze, Jessica A. Gephart, Patrick Gonzalez, Nancy B. Grimm, Peter M. Groffman, et al. 2023. “Priorities for Synthesis Research in Ecology and Environmental Science.” Ecosphere 14 (1). https://doi.org/10.1002/ecs2.4342.\n\n\nHampton, Stephanie E., and John N. Parker. 2011. “Collaboration and Productivity in Scientific Synthesis.” BioScience 61 (11): 900–910. https://doi.org/10.1525/bio.2011.61.11.9.\n\n\nRodrigo, Allen, Susan Alberts, Karen Cranston, Joel Kingsolver, Hilmar Lapp, Craig McClain, Robin Smith, Todd Vision, Jory Weintraub, and Brian Wiegmann. 2013. “Science Incubators: Synthesis Centers and Their Role in the Research Ecosystem.” PLoS Biology 11 (1): e1001468. https://doi.org/10.1371/journal.pbio.1001468.\n\n\nWyborn, Carina, Elena Louder, Jerry Harrison, Jensen Montambault, Jasper Montana, Melanie Ryan, Angela Bednarek, et al. 2018. “Understanding the Impacts of Research Synthesis.” Environmental Science & Policy 86 (August): 72–84. https://doi.org/10.1016/j.envsci.2018.04.013."
+    "section": "1.5 Priorities of Synthesis Here, Now, and Beyond",
+    "text": "1.5 Priorities of Synthesis Here, Now, and Beyond\nIn February 2021, NCEAS hosted a virtual workshop where 127 ecologists, environmental and natural scientists convened to discuss the future priorities of synthesis science. Participants across career stages, institutions, backgrounds, and geographies were in attendance and together they identified seven research topics, and two pressing needs for conducting research synthesis practices.\nLater in January 2023, dozens of scientists authored and published Priorities for synthesis research in ecology and environmental science the results of the workshop. We’ve summarized these results in the tables below.\n\n\n\n\n\n\n\nResearch Topic\nDescription\n\n\n\n\nDiversity, Equity, Inclusion, and Justice (DEIJ)\nEnvironmental justice shows the link between nature and fairness for people, especially those who are disadvantaged. Unequal distribution of nature due to factors like racism impacts cities and ecosystems. Integrating DEIJ into research can improve knowledge and involve underrepresented groups, while engaging diverse communities can make research more impactful and inclusive.\n\n\nHuman and Natural systems\nEnvironmental synthesis has given us valuable insights into environmental change, but it needs to also look at how human actions and decisions affect the environment. For example, decisions about things like population growth and greenhouse gas policies impact species and nature. Bringing together information from different fields is challenging, especially when considering different scales and practices. Synthesis science can help us better understand how humans and nature are connected, but it takes time and effort to combine ideas from different disciplines.\n\n\nActionable and Use-inspired science\nSynthesis science in ecology and environmental science is great for helping decision makers. Methods like systematic reviews and predictive models can simplify research for environmental policy. To be more effective, scientists need to work closely with practitioners and use different types of knowledge. Communication, teamwork, and using various knowledge sources can improve how decisions are made and carried out based on scientific findings.\n\n\nScale\nUnderstanding ecosystems across different sizes and time periods is hard for social-ecological research. Synthesis helps by combining information and methods from different scales, and connecting these findings to policy decisions. The challenges come from not having enough long-term data, difficulty mixing different information for analysis, and lack of skills and cooperation across scales. To overcome these challenges, more efforts are needed to collect, organize, and combine data over long periods and big areas.\n\n\nGenerality\nScience looks for common rules to explain how things work, but sometimes it’s hard to find them. Synthesis science can help find these general rules and figure out when they apply. Two problems are not having enough similar studies to compare and bias in what gets studied and reported. To fix this, scientists can share their work openly, use the same rules for reporting, and make sure all types of results are published. Working together across different areas can also help find these general rules.\n\n\nComplexity and Resilience\nEcosystems are very complicated, especially when considering things like society and economics. This complexity can make systems strong or weak. Using this complex view helps solve problems like water and food. But putting all this information together (synthesis) is hard because it depends on the situation, and finding general rules is tough. To make it work better, more scientists need to learn to work together across different areas and share their data. The goal is to understand how complexity affects ecosystems and find ways to help both people and the environment.\n\n\nPredictability\nThis time of big changes in the environment makes it important to predict how ecosystems will be in the future and how they’ll affect people. Predicting things in the short term (like months or years) is common in some fields but not in ecology. Some people worry that the predictions might not be perfect, but even imperfect predictions can help us learn and make better decisions. To make predictions better in ecology, scientists can work together, use standardized methods and data, and talk to the people who use the predictions. This will help us manage natural resources more wisely.\n\n\n\n\n\n\n\n\n\n\nPressing Need\nDescription\n\n\n\n\nNeed for increased participant diversity and inclusive research practices\nSynthesis science is most effective when it combines data, perspectives, and insights from different regions and ecosystems. By including diverse participants and creating inclusive research environments, we can better address important questions and find fair solutions to challenges. Ignoring diversity and inclusion can lead to biased syntheses and unfair outcomes. The large scope of synthesis science calls for involving people, ideas, and data from various backgrounds and languages. Efforts should be made to ensure representation, provide support, and promote open dialogue to increase diversity and inclusivity in synthesis research, ultimately fostering better understanding and more equitable outcomes.\n\n\nIncreased and improved data flow, access, and skill-building\nThe amount of data for synthesis science is increasing, and there are efforts to organize and combine data from various sources to answer ecological questions. Funding is needed to manage and store data properly. It’s important to think about ethical issues when using different datasets, especially if some people can’t share their data openly. Some data may be hard to access due to language or quality issues, and including diverse types of data, like traditional knowledge, can be challenging. Tools like machine learning can help make sense of different data sources. To improve data synthesis, global collaboration among diverse groups is essential.\n\n\n\n\n\n\n\nCarpenter, Stephen R., E. Virginia Armbrust, Peter W. Arzberger, F. Stuart Chapin, James J. Elser, Edward J. Hackett, Anthony R. Ives, et al. 2009. “Accelerate Synthesis in Ecology and Environmental Sciences.” BioScience 59 (8): 699–701. https://doi.org/10.1525/bio.2009.59.8.11.\n\n\nDicks, Lynn V., Jessica C. Walsh, and William J. Sutherland. 2014. “Organising Evidence for Environmental Management Decisions: A ‘4S’ Hierarchy.” Trends in Ecology & Evolution 29 (11): 607–13. https://doi.org/10.1016/j.tree.2014.09.004.\n\n\nHalpern, Benjamin S., Carl Boettiger, Michael C. Dietze, Jessica A. Gephart, Patrick Gonzalez, Nancy B. Grimm, Peter M. Groffman, et al. 2023. “Priorities for Synthesis Research in Ecology and Environmental Science.” Ecosphere 14 (1). https://doi.org/10.1002/ecs2.4342.\n\n\nHampton, Stephanie E., and John N. Parker. 2011. “Collaboration and Productivity in Scientific Synthesis.” BioScience 61 (11): 900–910. https://doi.org/10.1525/bio.2011.61.11.9.\n\n\nRodrigo, Allen, Susan Alberts, Karen Cranston, Joel Kingsolver, Hilmar Lapp, Craig McClain, Robin Smith, Todd Vision, Jory Weintraub, and Brian Wiegmann. 2013. “Science Incubators: Synthesis Centers and Their Role in the Research Ecosystem.” PLoS Biology 11 (1): e1001468. https://doi.org/10.1371/journal.pbio.1001468.\n\n\nWyborn, Carina, Elena Louder, Jerry Harrison, Jensen Montambault, Jasper Montana, Melanie Ryan, Angela Bednarek, et al. 2018. “Understanding the Impacts of Research Synthesis.” Environmental Science & Policy 86 (August): 72–84. https://doi.org/10.1016/j.envsci.2018.04.013."
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-    "text": "Learning Objectives\n\nProvide an overview of Logic Models\nApply the principles of Logic Models to synthesis development\nRefine synthesis group challenges"
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     "text": "2.1 Logic Models\nLogic models are a planning tool that are designed to support program development by depicting the flow of resources and processes leading to a desired result. They are also used for outcomes-based evaluation of a program and are often requested as part of an evaluation planning process by funders or stakeholders.\nA simplified logic models comprise three main parts: Inputs, Outputs and Outcomes.\n\nInputs reflect what is invested, outputs are what is done and outcomes are the results of the program.\nIn a more detailed logic model, outputs and outcomes are further broken down. Outputs are often represented as ‘Activities’ and ‘Participants’. By including participation (or participants), the logic model is explicitly considering the intended audience, or stakeholders, impacted by the program. Engagement of this audience is an output. In the case of outcomes, these can be split into short, medium and long-term outcomes. Sometimes this last category may be labeled ‘Impact’\n\nDefining the inputs, outputs and outcomes early in a planning process enables teams to visualize the workflow from activity to results and can help mitigate potential challenges. Logic models can be thought of as having an ‘if this then that’ structure where inputs -&gt; outputs -&gt; outcomes.\n\nIn the example below we have constructed a simple logic model for a hypothetical project where training materials are being developed for a group of educators to implement at their respective institutions.\n\nLinkages are not always sequential and can be within categories, bi-directional and/or include feedback loops. Detailing this complexity of relationships, or theory of action, can be time consuming but is a valuable part of the thought process for project planning. In exploring all relationships, logic modeling also allows for assessing program feasibility.\n\nThe above graphics include two sections within Outputs - Activities and Participants - and this is quite common. There is variation in logic model templates, including versions with a third type of output - “Products’. Sometimes description of these products is contained within the Activities section - for example, ‘develop curricula’, ‘produce a report’ - however calling these out explicitly is beneficial for teams focused on product development.\nProgram development (and logic modeling) occurs in response to a given ‘Situation’ or need, and exploring this is the first step in modeling. The situation defines the objective, or problem, that the program is designed to solve hence some logic models may omit the left-hand situation column but be framed with Problem and Solution statements. Finally, comprehensive logic modeling takes into consideration assumptions that are made with respect to the resources available, the people involved, or the way the program will work and also recognizes that there are external factors that can impact the program’s success.\n\nIn summary:\nLogic models support program development and evaluation and comprise three primary steps in the workflow:\n\nInputs: Resources, contributions, and investments required for a program;\nOutputs: Activities conducted, participants reached, and products produced; and\nOutcomes: Results or expected changes arising from the program structured as short-, medium- and long-term."
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-    "text": "2.2 Logic models for synthesis development\nLogic models are one tool for program development and have sufficient flexibility for a variety of situations, including planning for a research collaboration. While some logic model categories may feel less relevant (can we scale up to a long-term outcome from a published synthesis?), the process of articulating the research objective, proposed outcome, associated resources and activities has value. Below are examples of questions that a typical logic model (LM) will ask, and how these might be reframed for a research collaboration (RC).\nObjective/Problem Statement\nLM: What is the problem? Why is this a problem? Who does this impact?\nRC: What is the current state of knowledge? What gaps exists in understanding? Why is more information / synthesis important?\nInputs\nLM: What resources are needed for the program? Personnel, money, time, equipment, partnerships ..\nRC: What is needed to undertake the synthesis research? For personnel, think in terms of the roles that are needed - data manager, statistician, writer, editor etc. Consider the time frame. DATA - what data are needed and what already exists?\nOutputs - Activities\nLM: What will be done? Development, design, workshops, conferences, counseling, outreach..\nRC: What activities are needed to conduct the research? This could be high level or it could be broken down into details such as the types of statistical approaches.\nOutputs - Participants\nLM: Who will we reach? Clients, Participants, Customers..\nRC: Who is the target audience? Who will be impacted by this work? Who is positioned to leverage this work?\nOutputs - Products\nLM: What will you create? Publications, websites, media communications …\nRC: What research products are planned / expected? Consider this in relation to the intended audience. Is a peer-reviewed publication, report or white paper most appropriate? How will derived data be handled? Will documentation, workflows, or code be published?\nShort-term Outcomes\nLM: What short-term outcomes are anticipated among participants. These can include changes in awareness, knowledge, skills, attitudes, opinions and intent.\nRC: Will this work represent a significant contribution to current understanding?\nMedium-term Outcomes\nLM: What medium-term outcomes are predicted among participants? These might include changes in behaviors, decision-making and actions.\nRC: Will this work promote increased research activity or open new avenues of inquiry?\nLong-term Outcomes\nLM: What long-term benefits, or impacts, are expected? Changes in social, economic, civic, and environmental conditions?\nRC: Will this work result in local, regional or national policy change? What will be the long-term impact of increased investment in the ecosystem?\n\n\n\n\n\n\nBreakout: Synthesis planning with logic models\n\n\n\nBreakout groups will focus on refining ideas for synthesis topcis using the logic modeling tools described in this section. The goal for this session is to develop one or more high-level logic models that:\n\nSummarize the synthesis challenge\nDefine the inputs needed to approach the synthesis\nDefine the outputs, including activities and products that would would be needed to address the issue\nDefine the short term outcomes and longer-term impacts of the work\n\nOften it is helpful to start with a brainstorming activity to list activities and products that might be used to address the synthesis challenge, then connect those in terms of outcomes and impacts, and then circle back to the resource and data inputs needed to feed the logic model. Thinking of the whole model as a workflow can help conceptualize the dependencies among steps.\nTools:\n\nPowerpoint logic model template\nMermaid flowcharts embedded in Quarto documents\n\n\n\n\n\nflowchart LR\n    INPUTS --&gt; ACTIVITIES --&gt; OUTPUTS --&gt; OUTCOMES/IMPACTS\n\n    Scenario{{Accelerate synthesis via data science training}}\n\n    R1[Instructor] & R2[Classroom space] & R3[Projector] --&gt; B{Data Science Workshop}\n    B --&gt; C(Workshop Curriculum)\n    B --&gt; D(Presentations and Practice)\n    \n    C & D --&gt; E[/Improved Delta management/] & F[/Increased analytic efficiency/]\n\n\n\n\n\nSource\n```{mermaid}\nflowchart LR\n    INPUTS --&gt; ACTIVITIES --&gt; OUTPUTS --&gt; OUTCOMES/IMPACTS\n\n    Scenario{{Accelerate synthesis via data science training}}\n\n    R1[Instructor] & R2[Classroom space] & R3[Projector] --&gt; B{Data Science Workshop}\n    B --&gt; C(Workshop Curriculum)\n    B --&gt; D(Presentations and Practice)\n    \n    C & D --&gt; E[/Improved Delta management/] & F[/Increased analytic efficiency/]\n```"
+    "section": "2.2 Logic Models for Synthesis Research",
+    "text": "2.2 Logic Models for Synthesis Research\nLogic models are one tool for program development and have sufficient flexibility for a variety of situations, including planning for synthesis research. While some logic model categories may feel less relevant, the process of articulating a research objective, associated resources and activities and proposed outcome(s) has value.\nBelow is a table of logic model elements and example questions for each element framed for both a typical logic model and synthesis research.\n\n\n\n\n\n\n\n\nLogic Model Element\nLogic Model Framing\nSynthesis Research Framing\n\n\n\n\nProblem Statement\nWhat is the problem? Why is this a problem? Who does this impact?\nWhat is the current state of knowledge? What gaps exists in understanding? Why is more information / synthesis important?\n\n\nInputs\nWhat resources are needed for the program (e.g. personnel, money, time, equipment, partnerships, etc.)?\nWhat is needed to undertake the synthesis research? For personnel, think in terms of the roles that are needed (e.g. data manager, statistician, writer, editor etc.) Consider the time frame. Condsider what data are needed and what already exists?\n\n\nOutputs - Activities\nWhat will be done (e.g. development, design, workshops, conferences, counseling, outreach, etc.)?\nWhat activities are needed to conduct the research?\nThis could be high level or it could be broken down into details such as the types of statistical approaches.\n\n\nOutputs - Participants\nWho will we reach (e.g. clients, participants, customers, etc.)?\nWho is the target audience? Who will be impacted by this work? Who is positioned to leverage this work?\n\n\nOutputs - Products\nWhat will you create (e.g. publications, websites, media communications, etc.)?\nWhat research products are planned or expected? Consider this in relation to the intended audience. Is a peer-reviewed publication, report or white paper most appropriate? How will derived data be handled? Will documentation, workflows, or code be published?\n\n\nOutcomes - Short-term\nWhat short-term outcomes are anticipated among participants?\nThese might include changes in awareness, knowledge, skills, attitudes, opinions or intent.\nWill this work represent a significant contribution to current understanding?\n\n\nOutcomes - Medium-term\nWhat medium-term outcomes are predicted among participants?\nThese might include changes in behaviors, decision-making and actions.\nWill this work promote increased research activity or open new avenues of inquiry?\n\n\nOutcomes - Long-term\nWhat long-term benefits, or impacts, are expected?\nThis might include changes in social, economic, civic, or environmental conditions.\nWill this work result in local, regional or national policy change? What will be the long-term impact of increased investment in the ecosystem?"
   },
   {
-    "objectID": "session_02.html#resources",
-    "href": "session_02.html#resources",
+    "objectID": "session_02.html#activity-synthesis-planning-with-logic-models",
+    "href": "session_02.html#activity-synthesis-planning-with-logic-models",
     "title": "2  Logic Models",
-    "section": "2.3 Resources",
-    "text": "2.3 Resources\n\nLogic model template (ppt) on Sharepoint\n\nSame Logic model template on Google Drive"
+    "section": "2.3 Activity: Synthesis Planning with Logic Models",
+    "text": "2.3 Activity: Synthesis Planning with Logic Models\n\n\n\n\n\n\nSetup\n\n\n\nBreakout into your research synthesis groups to refine your ideas for potential synthesis topics. Use either the Logic Model Template or a Mermaid Flowchart to create your logic model.\nThe goal for this activity is to develop one or more high-level logic models that:\n\nSummarize the synthesis research question\nDefine the inputs needed to approach the synthesis research question\nDefine the outputs including the activities, participants, and products needed to address the research question\nDefine at least the short-term outcomes and long-term outcomes of the research\n\nOften it is helpful to start by working backwards. Consider what outcomes are you trying to achieve, what outputs would help you get to those outcomes, and finally what inputs do you need to create those outputs.\n\n\n\nLogic Model Template\nUse our Logic Model Template to create your Logic Model. When you access the link, you must create your own copy to start editing.\n\n\n\nLogic Model Template\n\n\n\n\nMermaid Flowchart\nUse a Mermaid Flowchart to create your Logic Model. These is a tool that is embedded in Quarto documents.\n\n\n\n\nflowchart LR\n    INPUTS --&gt; ACTIVITIES --&gt; OUTPUTS --&gt; OUTCOMES/IMPACTS\n\n    Scenario{{Accelerate synthesis via data science training}}\n\n    R1[Instructor] & R2[Classroom space] & R3[Projector] --&gt; B{Data Science Workshop}\n    B --&gt; C(Workshop Curriculum)\n    B --&gt; D(Presentations and Practice)\n    \n    C & D --&gt; E[/Improved Delta management/] & F[/Increased analytic efficiency/]\n\n\n\n\n\nSource Code for Mermaid Flowhart Example (above):\n\n```{mermaid}\n\nflowchart LR\n    INPUTS --&gt; ACTIVITIES --&gt; OUTPUTS --&gt; OUTCOMES/IMPACTS\n\n    Scenario{{Accelerate synthesis via data science training}}\n\n    R1[Instructor] & R2[Classroom space] & R3[Projector] --&gt; B{Data Science Workshop}\n    B --&gt; C(Workshop Curriculum)\n    B --&gt; D(Presentations and Practice)\n    \n    C & D --&gt; E[/Improved Delta management/] & F[/Increased analytic efficiency/]\n```"
   },
   {
     "objectID": "session_03.html#learning-objectives",
diff --git a/2023-08-usgs/session_01.html b/2023-08-usgs/session_01.html
index a6b0a457..8a2a1adc 100644
--- a/2023-08-usgs/session_01.html
+++ b/2023-08-usgs/session_01.html
@@ -185,12 +185,12 @@ <h2 id="toc-title">Table of contents</h2>
   <li><a href="#what-about-data" id="toc-what-about-data" class="nav-link" data-scroll-target="#what-about-data"><span class="header-section-number">1.2.1</span> What about data?</a></li>
   </ul></li>
   <li><a href="#nceas-and-synthesis-science" id="toc-nceas-and-synthesis-science" class="nav-link" data-scroll-target="#nceas-and-synthesis-science"><span class="header-section-number">1.3</span> NCEAS and Synthesis Science</a></li>
-  <li><a href="#notable-working-groups-at-nceas" id="toc-notable-working-groups-at-nceas" class="nav-link" data-scroll-target="#notable-working-groups-at-nceas"><span class="header-section-number">1.4</span> Notable Working Groups at NCEAS</a>
+  <li><a href="#working-groups-at-nceas" id="toc-working-groups-at-nceas" class="nav-link" data-scroll-target="#working-groups-at-nceas"><span class="header-section-number">1.4</span> Working Groups at NCEAS</a>
   <ul class="collapse">
   <li><a href="#ocean-health-index-ohi" id="toc-ocean-health-index-ohi" class="nav-link" data-scroll-target="#ocean-health-index-ohi"><span class="header-section-number">1.4.1</span> Ocean Health Index (OHI)</a></li>
   <li><a href="#state-of-alaskas-salmon-and-people-sasap" id="toc-state-of-alaskas-salmon-and-people-sasap" class="nav-link" data-scroll-target="#state-of-alaskas-salmon-and-people-sasap"><span class="header-section-number">1.4.2</span> State of Alaska’s Salmon and People (SASAP)</a></li>
   </ul></li>
-  <li><a href="#priorities-of-synthesis-here-and-now" id="toc-priorities-of-synthesis-here-and-now" class="nav-link" data-scroll-target="#priorities-of-synthesis-here-and-now"><span class="header-section-number">1.5</span> Priorities of Synthesis Here and Now</a></li>
+  <li><a href="#priorities-of-synthesis-here-now-and-beyond" id="toc-priorities-of-synthesis-here-now-and-beyond" class="nav-link" data-scroll-target="#priorities-of-synthesis-here-now-and-beyond"><span class="header-section-number">1.5</span> Priorities of Synthesis Here, Now, and Beyond</a></li>
   </ul>
 </nav>
     </div>
@@ -222,7 +222,7 @@ <h2 data-number="1.1" class="anchored" data-anchor-id="what-is-synthesis-science
 <i class="callout-icon no-icon"></i>
 </div>
 <div class="callout-title-container flex-fill">
-Synthesis Science in Ecology
+Synthesis Science in Ecological and Environmental Sciences
 </div>
 </div>
 <div class="callout-body-container callout-body">
@@ -249,7 +249,7 @@ <h3 data-number="1.1.1" class="anchored" data-anchor-id="the-value-and-impact-of
 <li>Providing critical knowledge to solve environmental issues <span class="citation" data-cites="carpenter2009">(<a href="#ref-carpenter2009" role="doc-biblioref">Carpenter et al. 2009</a>)</span></li>
 <li>Initiating new collaborations and new insights across disciplines and non-academic sectors <span class="citation" data-cites="hampton2011">(<a href="#ref-hampton2011" role="doc-biblioref">Hampton and Parker 2011</a>)</span></li>
 </ul>
-<p>As synthesis science and research synthesis approaches have been gaining prominenance in the ecological and environmental fields, researchers have sought to understand the <strong>impacts of synthesis</strong>. In particular, <span class="citation" data-cites="wyborn2018">Wyborn et al. (<a href="#ref-wyborn2018" role="doc-biblioref">2018</a>)</span> identified the following impacts of research synthesis:</p>
+<p>As synthesis science and research synthesis approaches have been gaining prominenance in the ecological and environmental fields, researchers have sought to understand the <strong>impacts of synthesis</strong>. In particular, <span class="citation" data-cites="wyborn2018">Wyborn et al. (<a href="#ref-wyborn2018" role="doc-biblioref">2018</a>)</span> identified the following types of impacts from research synthesis:</p>
 <ul>
 <li>Knowledge is used to change understanding of a system or phenomena</li>
 <li>Knowledge changes understanding of the interconnections between actors, sectors or systems</li>
@@ -282,7 +282,9 @@ <h2 data-number="1.2" class="anchored" data-anchor-id="research-synthesis-approa
 </table>
 <section id="what-about-data" class="level3" data-number="1.2.1">
 <h3 data-number="1.2.1" class="anchored" data-anchor-id="what-about-data"><span class="header-section-number">1.2.1</span> What about data?</h3>
-<p>Data is essential to synthesis in ecological and environmental research. Synthesizing data can involve combining and analyzing data from multiple sources to address research questions, identify patterns, and draw meaningful conclusions. This process might involve integrating data from field studies, experiments, observations, and other sources to gain a comprehensive understanding of ecological and environmental phenomena.</p>
+<p>Data is essential to synthesis in ecological and environmental research. Synthesizing data can involve combining and analyzing data from multiple sources to address research questions, to conduct a systematic review or meta-analysis, to identify patterns, and draw meaningful conclusions that don’t appear in siloed approaches.</p>
+<p>At NCEAS, our approach tends to include <strong>combining multiple datasets</strong> from various sources, then we <strong>leverage specific expertise from diverse disciplines and researchers</strong>, and together we use those two elements to <strong>unleash new insights to answer large-scale questions</strong>.</p>
+<p>We often see this approach with <a href="https://www.nceas.ucsb.edu/working-group-model">working groups at NCEAS</a>. Working groups are teams that include researchers, agencies and organizational practitioners, and more who come together (and typically at NCEAS, a neutral ground) for focused and intensive working sessions around their research questions.</p>
 </section>
 </section>
 <section id="nceas-and-synthesis-science" class="level2" data-number="1.3">
@@ -293,11 +295,11 @@ <h2 data-number="1.3" class="anchored" data-anchor-id="nceas-and-synthesis-scien
 </figure>
 </div>
 <p>In 1995, the National Science Foundation (NSF) responded to the increasing calls by environmental researchers and professionals for interdisciplinary and transdisciplinary explanations, methods to connect science to applications, and ways to advance research technologies by creating NCEAS, the first synthesis center.</p>
-<p>There have been multiple other synthesis centers that been established since, by each one is founded by the overall goal to use synthesis to “advance basic science, organize ecological information for decision makers concerned with pressing national issues, and make cost-effective use of the nation’s extant and accumulating database” <span class="citation" data-cites="hampton2011">(<a href="#ref-hampton2011" role="doc-biblioref">Hampton and Parker 2011</a>)</span>.</p>
+<p>There are multiple other synthesis centers that have been established since, but each one has a foundational goal to use synthesis to “advance basic science, organize ecological information for decision makers concerned with pressing national issues, and make cost-effective use of the nation’s extant and accumulating database” <span class="citation" data-cites="hampton2011">(<a href="#ref-hampton2011" role="doc-biblioref">Hampton and Parker 2011</a>)</span>.</p>
 <p>Synthesis centers are specifically designed to catalyze collaboration leading to breakthrough ideas, and one way NCEAS (among other synthesis centers) achieve this are through <strong>working groups</strong>. Since its inception, NCEAS has hosted hundreds of working groups and has supported them in varying ways including: structuring group size or composition, facilitation of working sessions, supplying computing and informatics support, offering training opporunities for data-focused team members, providing a neutral space for meetings and co-working, and more. In short, NCEAS lowers the “activation energy” for working group members so that they can focus on the cross-pollination of ideas, creative thinking, and emergent research <span class="citation" data-cites="rodrigo2013">(<a href="#ref-rodrigo2013" role="doc-biblioref">Rodrigo et al. 2013</a>)</span>.</p>
 </section>
-<section id="notable-working-groups-at-nceas" class="level2" data-number="1.4">
-<h2 data-number="1.4" class="anchored" data-anchor-id="notable-working-groups-at-nceas"><span class="header-section-number">1.4</span> Notable Working Groups at NCEAS</h2>
+<section id="working-groups-at-nceas" class="level2" data-number="1.4">
+<h2 data-number="1.4" class="anchored" data-anchor-id="working-groups-at-nceas"><span class="header-section-number">1.4</span> Working Groups at NCEAS</h2>
 <section id="ocean-health-index-ohi" class="level3" data-number="1.4.1">
 <h3 data-number="1.4.1" class="anchored" data-anchor-id="ocean-health-index-ohi"><span class="header-section-number">1.4.1</span> Ocean Health Index (OHI)</h3>
 <div class="quarto-figure quarto-figure-left">
@@ -326,10 +328,10 @@ <h3 data-number="1.4.2" class="anchored" data-anchor-id="state-of-alaskas-salmon
 <p>The project sought to address the need for accessible, up-to-date, and complete information about salmon and the factors shaping their future. It convened eight collaborative working groups consisting of leading experts at the University of Alaska and other universities, local indigenous leaders, and specialists across resource sectors – an intentional integration of western scientific perspectives and indigenous knowledge.</p>
 </section>
 </section>
-<section id="priorities-of-synthesis-here-and-now" class="level2" data-number="1.5">
-<h2 data-number="1.5" class="anchored" data-anchor-id="priorities-of-synthesis-here-and-now"><span class="header-section-number">1.5</span> Priorities of Synthesis Here and Now</h2>
+<section id="priorities-of-synthesis-here-now-and-beyond" class="level2" data-number="1.5">
+<h2 data-number="1.5" class="anchored" data-anchor-id="priorities-of-synthesis-here-now-and-beyond"><span class="header-section-number">1.5</span> Priorities of Synthesis Here, Now, and Beyond</h2>
 <p>In February 2021, NCEAS hosted a virtual workshop where 127 ecologists, environmental and natural scientists convened to discuss the future priorities of synthesis science. Participants across career stages, institutions, backgrounds, and geographies were in attendance and together they identified seven research topics, and two pressing needs for conducting research synthesis practices.</p>
-<p>Later in Jnauary 2023, dozens of scientists authored and published <a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4342#ecs24342-bib-0004">Priorities for synthesis research in ecology and environmental science</a> the results of the workshop. We’ve summarized these results in the tables below.</p>
+<p>Later in January 2023, dozens of scientists authored and published <a href="https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.4342#ecs24342-bib-0004">Priorities for synthesis research in ecology and environmental science</a> the results of the workshop. We’ve summarized these results in the tables below.</p>
 <table class="table">
 <colgroup>
 <col style="width: 25%">
diff --git a/2023-08-usgs/session_02.html b/2023-08-usgs/session_02.html
index dae84409..4e545f5e 100644
--- a/2023-08-usgs/session_02.html
+++ b/2023-08-usgs/session_02.html
@@ -161,8 +161,12 @@ <h2 id="toc-title">Table of contents</h2>
   <ul>
   <li><a href="#learning-objectives" id="toc-learning-objectives" class="nav-link active" data-scroll-target="#learning-objectives">Learning Objectives</a></li>
   <li><a href="#logic-models" id="toc-logic-models" class="nav-link" data-scroll-target="#logic-models"><span class="header-section-number">2.1</span> Logic Models</a></li>
-  <li><a href="#logic-models-for-synthesis-development" id="toc-logic-models-for-synthesis-development" class="nav-link" data-scroll-target="#logic-models-for-synthesis-development"><span class="header-section-number">2.2</span> Logic models for synthesis development</a></li>
-  <li><a href="#resources" id="toc-resources" class="nav-link" data-scroll-target="#resources"><span class="header-section-number">2.3</span> Resources</a></li>
+  <li><a href="#logic-models-for-synthesis-research" id="toc-logic-models-for-synthesis-research" class="nav-link" data-scroll-target="#logic-models-for-synthesis-research"><span class="header-section-number">2.2</span> Logic Models for Synthesis Research</a></li>
+  <li><a href="#activity-synthesis-planning-with-logic-models" id="toc-activity-synthesis-planning-with-logic-models" class="nav-link" data-scroll-target="#activity-synthesis-planning-with-logic-models"><span class="header-section-number">2.3</span> Activity: Synthesis Planning with Logic Models</a>
+  <ul class="collapse">
+  <li><a href="#logic-model-template" id="toc-logic-model-template" class="nav-link" data-scroll-target="#logic-model-template">Logic Model Template</a></li>
+  <li><a href="#mermaid-flowchart" id="toc-mermaid-flowchart" class="nav-link" data-scroll-target="#mermaid-flowchart">Mermaid Flowchart</a></li>
+  </ul></li>
   </ul>
 </nav>
     </div>
@@ -175,9 +179,9 @@ <h2 id="toc-title">Table of contents</h2>
 <section id="learning-objectives" class="level2 unnumbered">
 <h2 class="unnumbered anchored" data-anchor-id="learning-objectives">Learning Objectives</h2>
 <ul>
-<li>Provide an overview of Logic Models</li>
+<li>Become familiar with Logic Models</li>
 <li>Apply the principles of Logic Models to synthesis development</li>
-<li>Refine synthesis group challenges</li>
+<li>Refine synthesis group research question</li>
 </ul>
 </section>
 <section id="logic-models" class="level2" data-number="2.1">
@@ -205,56 +209,107 @@ <h2 data-number="2.1" class="anchored" data-anchor-id="logic-models"><span class
 <li><strong>Outcomes:</strong> Results or expected changes arising from the program structured as short-, medium- and long-term.</li>
 </ul>
 </section>
-<section id="logic-models-for-synthesis-development" class="level2" data-number="2.2">
-<h2 data-number="2.2" class="anchored" data-anchor-id="logic-models-for-synthesis-development"><span class="header-section-number">2.2</span> Logic models for synthesis development</h2>
-<p>Logic models are one tool for program development and have sufficient flexibility for a variety of situations, including planning for a research collaboration. While some logic model categories may feel less relevant (can we scale up to a long-term outcome from a published synthesis?), the process of articulating the research objective, proposed outcome, associated resources and activities has value. Below are examples of questions that a typical logic model (LM) will ask, and how these might be reframed for a research collaboration (RC).</p>
-<p><strong>Objective/Problem Statement</strong></p>
-<p>LM: What is the problem? Why is this a problem? Who does this impact?</p>
-<p>RC: What is the current state of knowledge? What gaps exists in understanding? Why is more information / synthesis important?</p>
-<p><strong>Inputs</strong></p>
-<p>LM: What resources are needed for the program? Personnel, money, time, equipment, partnerships ..</p>
-<p>RC: What is needed to undertake the synthesis research? For personnel, think in terms of the roles that are needed - data manager, statistician, writer, editor etc. Consider the time frame. DATA - what data are needed and what already exists?</p>
-<p><strong>Outputs - Activities</strong></p>
-<p>LM: What will be done? Development, design, workshops, conferences, counseling, outreach..</p>
-<p>RC: What activities are needed to conduct the research? This could be high level or it could be broken down into details such as the types of statistical approaches.</p>
-<p><strong>Outputs - Participants</strong></p>
-<p>LM: Who will we reach? Clients, Participants, Customers..</p>
-<p>RC: Who is the target audience? Who will be impacted by this work? Who is positioned to leverage this work?</p>
-<p><strong>Outputs - Products</strong></p>
-<p>LM: What will you create? Publications, websites, media communications …</p>
-<p>RC: What research products are planned / expected? Consider this in relation to the intended audience. Is a peer-reviewed publication, report or white paper most appropriate? How will derived data be handled? Will documentation, workflows, or code be published?</p>
-<p><strong>Short-term Outcomes</strong></p>
-<p>LM: What short-term outcomes are anticipated among participants. These can include changes in awareness, knowledge, skills, attitudes, opinions and intent.</p>
-<p>RC: Will this work represent a significant contribution to current understanding?</p>
-<p><strong>Medium-term Outcomes</strong></p>
-<p>LM: What medium-term outcomes are predicted among participants? These might include changes in behaviors, decision-making and actions.</p>
-<p>RC: Will this work promote increased research activity or open new avenues of inquiry?</p>
-<p><strong>Long-term Outcomes</strong></p>
-<p>LM: What long-term benefits, or impacts, are expected? Changes in social, economic, civic, and environmental conditions?</p>
-<p>RC: Will this work result in local, regional or national policy change? What will be the long-term impact of increased investment in the ecosystem?</p>
-<div class="callout callout-style-default callout-tip callout-titled" title="Breakout: Synthesis planning with logic models">
+<section id="logic-models-for-synthesis-research" class="level2" data-number="2.2">
+<h2 data-number="2.2" class="anchored" data-anchor-id="logic-models-for-synthesis-research"><span class="header-section-number">2.2</span> Logic Models for Synthesis Research</h2>
+<p>Logic models are one tool for program development and have sufficient flexibility for a variety of situations, including planning for synthesis research. While some logic model categories may feel less relevant, the process of articulating a research objective, associated resources and activities and proposed outcome(s) has value.</p>
+<p>Below is a table of logic model elements and example questions for each element framed for both a typical logic model and synthesis research.</p>
+<table class="table">
+<colgroup>
+<col style="width: 25%">
+<col style="width: 38%">
+<col style="width: 38%">
+</colgroup>
+<thead>
+<tr class="header">
+<th>Logic Model Element</th>
+<th>Logic Model Framing</th>
+<th>Synthesis Research Framing</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td><strong>Problem Statement</strong></td>
+<td>What is the problem? Why is this a problem? Who does this impact?</td>
+<td>What is the current state of knowledge? What gaps exists in understanding? Why is more information / synthesis important?</td>
+</tr>
+<tr class="even">
+<td><strong>Inputs</strong></td>
+<td>What resources are needed for the program (e.g.&nbsp;personnel, money, time, equipment, partnerships, etc.)?</td>
+<td>What is needed to undertake the synthesis research? For personnel, think in terms of the roles that are needed (e.g.&nbsp;data manager, statistician, writer, editor etc.) Consider the time frame. Condsider what data are needed and what already exists?</td>
+</tr>
+<tr class="odd">
+<td><strong>Outputs - Activities</strong></td>
+<td>What will be done (e.g.&nbsp;development, design, workshops, conferences, counseling, outreach, etc.)?</td>
+<td><p>What activities are needed to conduct the research?</p>
+<p>This could be high level or it could be broken down into details such as the types of statistical approaches.</p></td>
+</tr>
+<tr class="even">
+<td><strong>Outputs - Participants</strong></td>
+<td>Who will we reach (e.g.&nbsp;clients, participants, customers, etc.)?</td>
+<td>Who is the target audience? Who will be impacted by this work? Who is positioned to leverage this work?</td>
+</tr>
+<tr class="odd">
+<td><strong>Outputs - Products</strong></td>
+<td>What will you create (e.g.&nbsp;publications, websites, media communications, etc.)?</td>
+<td>What research products are planned or expected? Consider this in relation to the intended audience. Is a peer-reviewed publication, report or white paper most appropriate? How will derived data be handled? Will documentation, workflows, or code be published?</td>
+</tr>
+<tr class="even">
+<td><strong>Outcomes - Short-term</strong></td>
+<td><p>What short-term outcomes are anticipated among participants?</p>
+<p>These might include changes in awareness, knowledge, skills, attitudes, opinions or intent.</p></td>
+<td>Will this work represent a significant contribution to current understanding?</td>
+</tr>
+<tr class="odd">
+<td><strong>Outcomes - Medium-term</strong></td>
+<td><p>What medium-term outcomes are predicted among participants?</p>
+<p>These might include changes in behaviors, decision-making and actions.</p></td>
+<td>Will this work promote increased research activity or open new avenues of inquiry?</td>
+</tr>
+<tr class="even">
+<td><strong>Outcomes - Long-term</strong></td>
+<td><p>What long-term benefits, or impacts, are expected?</p>
+<p>This might include changes in social, economic, civic, or environmental conditions.</p></td>
+<td>Will this work result in local, regional or national policy change? What will be the long-term impact of increased investment in the ecosystem?</td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="activity-synthesis-planning-with-logic-models" class="level2" data-number="2.3">
+<h2 data-number="2.3" class="anchored" data-anchor-id="activity-synthesis-planning-with-logic-models"><span class="header-section-number">2.3</span> Activity: Synthesis Planning with Logic Models</h2>
+<div class="callout callout-style-default callout-tip callout-titled" title="Setup">
 <div class="callout-header d-flex align-content-center">
 <div class="callout-icon-container">
 <i class="callout-icon"></i>
 </div>
 <div class="callout-title-container flex-fill">
-Breakout: Synthesis planning with logic models
+Setup
 </div>
 </div>
 <div class="callout-body-container callout-body">
-<p>Breakout groups will focus on refining ideas for synthesis topcis using the logic modeling tools described in this section. The goal for this session is to develop one or more high-level logic models that:</p>
+<p>Breakout into your research synthesis groups to refine your ideas for potential synthesis topics. Use either the <strong>Logic Model Template</strong> or a <strong>Mermaid Flowchart</strong> to create your logic model.</p>
+<p>The goal for this activity is to develop one or more high-level logic models that:</p>
 <ol type="1">
-<li>Summarize the synthesis challenge</li>
-<li>Define the inputs needed to approach the synthesis</li>
-<li>Define the outputs, including activities and products that would would be needed to address the issue</li>
-<li>Define the short term outcomes and longer-term impacts of the work</li>
+<li>Summarize the synthesis research question</li>
+<li>Define the inputs needed to approach the synthesis research question</li>
+<li>Define the outputs including the activities, participants, and products needed to address the research question</li>
+<li>Define at least the short-term outcomes and long-term outcomes of the research</li>
 </ol>
-<p>Often it is helpful to start with a brainstorming activity to list activities and products that might be used to address the synthesis challenge, then connect those in terms of outcomes and impacts, and then circle back to the resource and data inputs needed to feed the logic model. Thinking of the whole model as a workflow can help conceptualize the dependencies among steps.</p>
-<p>Tools:</p>
-<ul>
-<li>Powerpoint <a href="https://docs.google.com/presentation/d/1MBUy35QIAkA5FNCdRzWJM2aPaFXfUmGl/edit?usp=sharing&amp;ouid=113328660159760492310&amp;rtpof=true&amp;sd=true">logic model template</a></li>
-<li><a href="https://mermaid.js.org/syntax/flowchart.html">Mermaid flowcharts</a> embedded in <a href="https://quarto.org/docs/authoring/diagrams.html#mermaid">Quarto documents</a></li>
-</ul>
+<p><strong>Often it is helpful to start by working backwards.</strong> Consider what outcomes are you trying to achieve, what outputs would help you get to those outcomes, and finally what inputs do you need to create those outputs.</p>
+</div>
+</div>
+<section id="logic-model-template" class="level3 unnumbered">
+<h3 class="unnumbered anchored" data-anchor-id="logic-model-template">Logic Model Template</h3>
+<p>Use our <a href="https://docs.google.com/presentation/d/1f15bbMz_-0Y66BYYNYIdR6TQ25JLCc6JzNX_S44CrQQ/edit?usp=sharing">Logic Model Template</a> to create your Logic Model. When you access the link, you must create your own copy to start editing.</p>
+<div class="quarto-figure quarto-figure-center">
+<figure class="figure">
+<p><img src="images/logic-model-template.png" class="img-fluid figure-img"></p>
+<figcaption class="figure-caption">Logic Model Template</figcaption>
+</figure>
+</div>
+</section>
+<section id="mermaid-flowchart" class="level3 unnumbered">
+<h3 class="unnumbered anchored" data-anchor-id="mermaid-flowchart">Mermaid Flowchart</h3>
+<p>Use a <a href="https://mermaid.js.org/syntax/flowchart.html">Mermaid Flowchart</a> to create your Logic Model. These is a tool that is embedded in <a href="https://quarto.org/docs/authoring/diagrams.html#mermaid">Quarto documents</a>.</p>
 <div class="cell">
 <div class="cell-output-display">
 <div>
@@ -274,8 +329,10 @@ <h2 data-number="2.2" class="anchored" data-anchor-id="logic-models-for-synthesi
 </div>
 </div>
 </div>
-<p>Source</p>
+<p>Source Code for Mermaid Flowhart Example (above):</p>
+<!--- formatting necessary to show mermaid code, but not execute it -->
 <pre class="mermaid"><code>```{mermaid}
+
 flowchart LR
     INPUTS --&gt; ACTIVITIES --&gt; OUTPUTS --&gt; OUTCOMES/IMPACTS
 
@@ -285,21 +342,11 @@ <h2 data-number="2.2" class="anchored" data-anchor-id="logic-models-for-synthesi
     B --&gt; C(Workshop Curriculum)
     B --&gt; D(Presentations and Practice)
     
-    C &amp; D --&gt; E[/Improved Delta management/] &amp; F[/Increased analytic efficiency/]
-```</code></pre>
-</div>
-</div>
-</section>
-<section id="resources" class="level2" data-number="2.3">
-<h2 data-number="2.3" class="anchored" data-anchor-id="resources"><span class="header-section-number">2.3</span> Resources</h2>
-<ul>
-<li><a href="https://deltacouncil.sharepoint.com/:p:/r/sites/Extranet-Science/Shared%20Documents/NCEAS-DSP%20working%20group/2023/2023-06-week-1/logic_model_template.pptx?d=w32341d669685448d96b70e61e7e5e187&amp;csf=1&amp;web=1&amp;e=2ojEmj">Logic model template</a> (ppt) on Sharepoint
-<ul>
-<li>Same <a href="https://docs.google.com/presentation/d/1MBUy35QIAkA5FNCdRzWJM2aPaFXfUmGl/edit?usp=sharing&amp;ouid=113328660159760492310&amp;rtpof=true&amp;sd=true">Logic model template</a> on Google Drive</li>
-</ul></li>
-</ul>
+    C &amp; D --&gt; E[/Improved Delta management/] &amp; F[/Increased analytic efficiency/]</code></pre>
+<p>```</p>
 
 
+</section>
 </section>
 
 </main> <!-- /main -->