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75 results for “sustainability assessment”
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Chapter 6. Policy options for governing sustainable use of wild species
<p>Schematic and adapted figures from Chapter 6 of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Chapter 5. Future scenarios of sustainable use of wild species
<p>Schematic and adapted figures from Chapter 5 of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Chapter 2. Conceptualizing the sustainable use of wild species
<p>Schematic and adapted figures from Chapter 2 of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
Supporting material: Prospective life-cycle assessment of sustainable alternatives for road freight transport
<p><span>This study investigates decarbonization pathways for the road freight transport sector</span><span> </span><span>by evaluating three alternatives to conventional</span><span> </span><span>diesel trucks:</span><span> </span><span>trucks powered by biofuels, battery electric trucks, and fuel cell trucks with hydrogen</span><span>. A prospective life cycle assessment of these options is conducted under two policy scenarios for decarbonization across 12 distinct regions over the century. Employing a cradle-to-grave approach, the assessment covers activities from fuel and electricity production to the end-of-life of truck components. Findings reveal that, in eight of the 12 regions examined, an early transition to battery electric trucks could increase life-cycle greenhouse gas emissions by up to 70% by 2030 compared to the continued use of conventional diesel trucks, underscoring the significance of liquid fuels for short to medium-term decarbonization. However, in the long term, as electricity mixes and hydrogen production are decarbonized, battery electric trucks and hydrogen fuel cell trucks emerge as superior alternatives in all regions, emitting, at least, 29% less greenhouse gases than trucks powered by biofuels, and 45% less than diesel trucks.</span><span> </span><span>The optimal transition from conventional diesel trucks to trucks powered by biofuels and, subsequently, to battery electric trucks and/or hydrogen could avoid 134-204 Gt CO<sub>2-eq</sub> worldwide and prevent a temperature rise of 0.22-0.33°C compared to the diesel-based scenario. This emphasizes the crucial role of appropriate policies for the timely transformation of the road freight transport sector. </span></p>
BIOPLAT-EU: Target Area Base Layer providing statistical information on demography, employment and land use for sustainability assessment
<p>This dataset provides information on demographic variables related to population and employment as well as land use/land cover share on the basis of local administrative units (LAU). Within the BIOPLAT-EU project, this information is integrated into the webGIS sustainability assessment tool.<br> Main source of the administrative unit geometries is the spatial data set of local administrative units (LAU) (2016) provided by the European Commission (https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/lau#lau19). The data set was extended by Level 2 administrative boundaries of Albania (https://data.humdata.org/dataset/albania-administrative-level-0-3-boundaries) and Level 3 administrative boundaries of Ukraine as of 2017 (https://data.humdata.org/m/dataset/ukraine-administrative-boundaries-as-of-q2-2017?force_layout=light)</p> <p>Data on demography (2016) for LAU + Albania was acquired using the Eurostat statistical database (https://ec.europa.eu/eurostat/web/main/data/database). To calculate land use share for LAU and Albania, Corine Land cover (CLC) data from 2018 was used (https://land.copernicus.eu/pan-european/corine-land-cover). CLC classes were summarized into the following classes: urban areas (UrAr), forest (Fo), permanent crops (PeCr), annual crops (AnCr), permanent meadows and pastures (PeMaPa), industrial sites (InSi), water and wetlands (We), others (Ot).</p> <p>For Ukraine data on demography provided by the State Statistics Survey of Ukraine (http://www.ukrstat.gov.ua/) . To calculate land use share per administrative unit, the land use map produced by Myroniuk et al. 2020 (https://doi.org/10.3390/rs12010187) was used. Based to this map shares of the following land use classes are calculated: urban areas (UrAr), forest (Fo), annual and permanent cropland (AnPeCr), grassland (Gra), water and wetlands (We), others (Ot).</p> <p> </p> <p><em><strong>Terms of use:</strong></em> These data are provided "as is". <em>The authors make <strong>no </strong></em><strong><em>warranty</em></strong><em>, representation, or guaranty of any type as to the completeness, accuracy, content or fitness for any particular purpose or use of any </em><strong><em>open data</em></strong><em> set made available here</em><em>, nor shall any </em><strong><em>warranties</em></strong><em> be implied with respec</em><em>t to the data provided.</em></p>
Assessing consumers' attitudes, expectations and intentions towards health and sustainability regarding seafood consumption in Italy
<p>The EU and its Member States have articulated a sustainability vision ‘to live well within the limits of our planet’ by 2050. In this context, consumers play a key role, being able to drive seafood production sustainability and responsibility according to their behaviour, also in relation to their attitudes towards health, nutrition and wellbeing. On the basis of these premises, this research explores Italian consumers' attitudes towards health and sustainability in relation to seafood, in order to segment different target of consumers. The framework used in this study is mainly focused on a quantitative exploratory data collection based on an online survey. Three groups of consumerswere identified based on general health interest, perceived benefits of eating seafoods and attitude towards seafoods: Health seekerswho eat seafood for duty; Health seekers and seafood lovers; Lowcommitment to health and indifferent to seafood. Differences among groups related to socio-demographic characteristics, sustainability attitudes, intentions and interest in information about seafood productswere also investigated. In particular, the first two groups are more familiar with sustainable seafood products and more interested in information on these products than the third, both in terms of product origin and seasonality. Consumers belonging to second group show a higher probability to buy seafood products considering this characteristic than the other two groups. Based on the results obtained, a strategic plan could be developed to achieve relevant goals in education, communication and sustainability labelling related with seafood products. Following a preliminary scouting carried out with all the seafood stakeholders, a specific territory to test this approach has been already identified in Torre del Cerrano (Italy). These results have a strong implication for policy makers and educational<br> institutions as they identify differences in attitudes and perceptions among consumers that are crucial in order to<br> design the right communication strategy strategies as well as messages content.</p>
Dataset for Assessing Multi-Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix into the Future
<p>This data repository feeds into the meta-repository setup for post-processing of GCAM-SAM outputs. GitHub link of meta-repository is: <a href="https://github.com/JGCRI/Kyle-etal_2022_EF">https://github.com/JGCRI/Kyle-etal_2022_EF</a> <br> <br> Folders: <br> <strong>model/</strong> is the static version of the model used to simulate 8 scenarios. See the <a href="https://github.com/pkyle/gcam-core/tree/gpk/paper/sam">GitHub GCAM-SAM repository</a> to follow active development of this model. <br> <strong>inputs/</strong> folder contains input datasets and scripts used to prepare files while postprocessing. This is to be used with <a href="https://github.com/JGCRI/Kyle-etal_2022_EF">GitHub post-processing meta-repository</a>. <br> <strong>outdata/</strong> contains <a href="http://github.com/pkyle/gcam-core/tree/gpk/paper/sam">GCAM-SAM</a> output and <a href="http://github.com/JGCRI/Kyle-etal_2022_EF">post-processed</a> output files used to plot figures. <br> <br> Key files: <br> <em><strong>SAM-matrix.dat</strong></em> is the consolidated GCAM-SAM output. Use <em>proj_load.R</em> in the <a href="https://github.com/JGCRI/Kyle-etal_2022_EF">metarepo</a> to read the file. <br> <em><strong>region_vals.csv</strong></em> has all 8 indicators in all 8 scenarios for years 2020 till 2100 on a 10 year time step. <br> <br> Short introduction to the study:</p> <p>In this paper sustainable agriculture matrix (SAM) is estimated to 2100 using Global Change Analysis Model (GCAM). We model combinatorial variations of yield intensification, dietary shift, and greenhouse gas mitigation scenarios. Findings include scenarios having significant tradeoffs across multiple environmental, economic, and social dimensions. Assessment of these multi-dimensional tradeoffs in a consistent framework improves the quality of information for decision-making.<br> <br> Should you have any questions, feel free to reach out Page Kyle at <a href="mailto:pkyle@pnnl.gov">pkyle@pnnl.gov</a>. </p>
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Summary for policymakers
<p>Schematic and adapted figures from the Summary for policymakers of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES).</p>
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Chapter 1. Setting the Scene
<p>Schematic and adapted figures from Chapter 1 of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
Survey data of an integrative evaluation framework for assessing the sustainability of different types of urban agriculture
<p>In this dataset we present core data of an integrative evaluation framework for assessing the environmental, social, and economic sustainability of urban agriculture. The multi-criteria analysis is conducted by an Analytic Hierarchy Process and a participatory approach. The data integrate the selection and weighting of sub-criteria based on two online surveys:</p> <p>1) Survey 1: The selection of suitable sub-criteria for assessing the sustainability of urban agriculture was done by European scientific experts.</p> <p>2) Survey 2: The weighting of the selected sub-criteria was done on the example of vertical farming and community supported agriculture. Therefore, we involved stakeholders representing key actors for the implementation of urban agriculture: city administrations and non-governmental organizations (NGOs) of ten German case study cities, practitioners and technical-scientific experts.</p> <p> </p> <p><strong>List of data and content</strong></p> <p>1) Survey_1 (*.zip):</p> <ul> <li>Survey_1_Criteria_Selection_English: Online survey in English (*.pdf)</li> <li>Survey_1_Information_Sub-criteria_English: Information about the sub-criteria provided in the survey (in English) (*.pdf)</li> <li>Survey_1_Groups: Results of the statistical analyses (U-tests and Kruscal-Wallis) to detect group-specific differences (e.g. gender, different length or degree of experience with urban agriculture, scientific focus, target group, expertise); the tests were conducted with IBM SPSS Statistics 25 (*.xlsx)</li> </ul> <p>2) Survey_2 (*.zip):</p> <ul> <li>Survey_2_AHP_City_Administrations_German: Online survey for city administrations in German (*.pdf)</li> <li>Survey_2_AHP_Practitioners_German: Online survey for practitioners and technical-scientific experts in German (*.pdf)</li> <li>Survey_2_AHP_NGOs_German: Online survey for NGOs in German (*.pdf)</li> <li>Survey_2_Information_Sub-Criteria_German: Information about the sub-criteria provided in the survey (in German) (*.pdf)</li> <li>Survey_2_Groups: Results of the statistical analyses (U-tests and Kruscal-Wallis) to detect group-specific differences (e.g. gender, different length or degree of experience with urban agriculture, scientific focus, target group, expertise); the tests were conducted with IBM SPSS Statistics 25 (*.xlsx)</li> <li>rdata_CA_AHP_edible_Cities_2022-03-18_10-28: Results of the survey for city administrations (*.csv)</li> <li>rdata_NGO_AHP_edible_Cities_2022-03-18_10-40: Results of the survey for NGOs (*.csv)</li> <li>rdata_PE_AHP_edible_Cities_2022-03-18_10-41: Results of the survey for practitioners and technical-scientific experts (*.csv)</li> <li>rdata_all_AHP_edible_Cities_2022-03-18_09-53: Total results of the survey</li> </ul> <p> </p> <p><strong>Data acquisition and processing</strong></p> <p>The methods are described in this linked publication:</p> <p><span>John, H., & Artmann, M. (2024). Introducing an integrative evaluation framework for assessing the sustainability of different types of urban agriculture. </span><span>I<em>nternational Journal of Urban Sustainable Development, </em></span>16 (1), 35-52<em><span>. </span></em><span>doi:<em> </em>10.1080/19463138.2024.2317795</span></p> <p>The methodology of the performed analytic hierarchy process (AHP) is published in a separate repository on GitHub including a paper that systematically explains the AHP by means of code examples, starting with the raw data, through their adaptation to the software functions of the ahpsurvey R-package, and finally, execution of the AHP up to the visualization of the results.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The authors thank Mabel Killinger and Marie Herzig for their help in stakeholder identification as well as all experts and stakeholders for their participation in the two online surveys and their helpful comments. Data processing and analysis by means of an Analytic Hierarchy Process in R would not have been possible without the help of Björn Kasper.</p>
Assessment of tobacco and N. benthamiana as biofactories of irregular monoterpenes for sustainable crop protection
<p>Irregular monoterpenes are important precursors of different compounds employed in pest control such as insecticides and insect sex pheromones. Metabolically engineered plants are appealing as biofactories of such compounds, but specially as potential live biodispensers of related bioactive volatiles, which could be continuously emitted to the environment from different plant tissues. Here we assess the use of cultivated tobacco and Nicotiana benthamiana as biofactories for the irregular monoterpenes chrysanthemol and lavandulol. We evaluate the impact of high levels of constitutive metabolite production on the plant physiology and biomass, and their biosynthetic dynamics for different plant tissues and developmental stages. As an example of an active pheromone compound, we super-transformed the best lavandulol-producing tobacco line with an acetyl transferase gene to obtain a tobacco lavandulyl acetate biodispenser producing up to 0.63 mg of lavandulyl acetate per plant every day. We estimate that with these volatile emission levels, between 200 and 500 plants per hectare would be sufficient to ensure a daily emission of pheromones comparable to commercial lures. This is an important step towards plant-based sustainable solutions for pest control, and it lays the ground for further developing biofactories for other irregular monoterpenoid pheromones, whose biosynthetic genes are yet unknown.</p> <p> </p> <p>The dataset includes GC-MS data (peak areas quantified for each compound with its Qi, normalized) for all the figures and supplementary figures of the associated manuscript. Peak areas are normalized with an admixture, analyzed on the same day as the samples.</p> <p>It also includes biomass data and measurements for the analyzed plants (plant height, leaf biomass, days to flowering time).</p> <p>File S3 shows data for quantitative PCR to determine transgene copy number, and for qRT-PCR to measure transgene expression levels.</p> <p>For all datasets, statistical analysis is also included to support the conclusions showed in the graphs.</p>
Experimental auction to assess consumer perception and willingness to pay for ready-to-eat fresh salads with more sustainable packaging
<p>The provided Excel sheet contains a dataset generated from an experiment conducted face-to-face with consumers of ready to eat fresh salads on December 19th and 20th, 2023, using a questionnaire. It was an auction aimed at understanding consumers' preferences and willingness to pay for 100% recyclable and 100% biodegradable packaging.</p> <p>Each column in the table represents a particular variable, and each row corresponds to a specific record in the dataset. The dataset comprises 70 columns and 306 rows, where the first row contains the variable names and the second row describes the meaning or question associated with each variable.<br>To enhance readability, each variable is color-coded differently. This means that as you move from one color to another, you are transitioning from one variable to the next.<br>Most variables are derived from validated scales, such as General Shopping Behavior, General Personal Values and Attitudes, Environmental Consciousness Scale, Openness to Innovation Scale, NEP Scale, and Green Consumption Values. These scales are generally assessed using a Likert scale of 5 or 7 points for each statement or item included in the scale. Each statement is in a separate column, and the combined columns of statements form the respective variable, which is the scale in this context. Therefore, the total number of variables included in this dataset is 24.</p>
Method-of-Use Study Assessing the Effect of Naltrexone Sustained Release (SR)/ Bupropion SR on Body Weight and Cardiovascular Risk Factors in Overweight and Obese Subjects
ClinicalTrials.gov study NCT01764386. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Development of a sustainability assessment algorithm and its validation using case studies on cryogenic machining
Open the record for dataset details and reuse information.
Data from: Assessing faculty professional development in STEM higher education: sustainability of outcomes
We tested the effectiveness of Faculty Institutes for Reforming Science Teaching IV (FIRST), a professional development program for postdoctoral scholars, by conducting a study of program alumni. Faculty professional development programs are critical components of efforts to improve teaching and learning in the STEM (Science, Technology, Engineering, and Mathematics) disciplines, but reliable evidence of the sustained impacts of these programs is lacking. We used a paired design in which we matched a FIRST alumnus employed in a tenure-track position with a non-FIRST faculty member at the same institution. The members of a pair taught courses that were of similar size and level. To determine whether teaching practices of FIRST participants were more learner-centered than those of non-FIRST faculty, we compared faculty perceptions of their teaching strategies, perceptions of environmental factors that influence teaching, and actual teaching practice. Non-FIRST and FIRST faculty reported similar perceptions of their teaching strategies and teaching environment. FIRST faculty reported using active learning and interactive engagement in lecture sessions more frequently compared with non-FIRST faculty. Ratings from external reviewers also documented that FIRST faculty taught class sessions that were learner-centered, contrasting with the teacher-centered class sessions of most non-FIRST faculty. Despite marked differences in teaching practice, FIRST and non-FIRST participants used assessments that targeted lower-level cognitive skills. Our study demonstrated the effectiveness of the FIRST program and the empirical utility of comparison groups, where groups are well matched and controlled for contextual variables (for example, departments), for evaluating the effectiveness of professional development for subsequent teaching practices.
Data from: Assessing the sustainability of African lion trophy hunting, with recommendations for policy
While trophy hunting provides revenue for conservation, it must be carefully managed to avoid negative population impacts, particularly for long-lived species with low natural mortality rates. Trophy hunting has had negative effects on lion populations throughout Africa, and the species serves as an important case study to consider the balance of costs and benefits, and to consider the effectiveness of alternative strategies to conserve exploited species. Age-restricted harvesting is widely recommended to mitigate negative effects of lion hunting, but this recommendation was based on a population model parameterized with data from a well-protected and growing lion population. Here, we used demographic data from lions subject to more typical conditions, including source–sink dynamics between a protected National Park and adjacent hunting areas in Zambia's Luangwa Valley, to develop a stochastic population projection model and evaluate alternative harvest scenarios. Hunting resulted in population declines over a 25-yr period for all continuous harvest strategies, with large declines for quotas >1 lion/concession (~0.5 lion/1,000 km2) and hunting of males younger than seven years. A strategy that combined periods of recovery, an age limit of ≥7 yr, and a maximum quota of ~0.5 lions shot/1,000 km2 yielded a risk of extirpation <10%. Our analysis incorporated the effects of human encroachment, poaching, and prey depletion on survival, but assumed that these problems will not increase, which is unlikely. These results suggest conservative management of lion trophy hunting with a combination of regulations. To implement sustainable trophy hunting while maintaining revenue for conservation of hunting areas, our results suggest that hunting fees must increase as a consequence of diminished supply. These findings are broadly applicable to hunted lion populations throughout Africa and to inform global efforts to conserve exploited carnivore populations.
Strengthening the global environmental assessment seascape in support of ocean sustainability
<p>Ambitious evidence-based policies are urgently needed to redirect mankind’s trajectory towards ocean sustainability. While global environmental assessments (GEAs) synthesizing ocean knowledge are multiplying, it is crucial to ensure that their processes and outputs are conducive to social legitimacy, scientific credibility, and are relevant to decision makers’ needs. Building upon institutional and scientific literature, we consolidate a list of best practices for GEAs to achieve legitimacy, credibility, and salience. We then develop a standardized framework to score the level of implementation of these best practices and to assess the coverage of ocean knowledge in GEAs. Lastly, we apply this framework to review 12 influential reports at the ocean science-policy interface. We found that credibility best practices are well implemented but that all GEAs, and in particular ocean-focused assessments, have significant opportunities to strengthen the implementation of legitimacy and salience best practices. We identify that increasing stakeholder engagement and broadening the diversity of knowledge represented is needed to improve the legitimacy of GEAs. Additionally, we found that GEAs could increase their salience by featuring more actionable knowledge for decision-makers, including futures thinking, multi-scale intervention options, and evaluation of progress towards international targets. Finally, we highlight four recommendations to strengthen the GEA seascape: elevating co-production practices, bridging scales through multi-level approaches, increasing transparency in knowledge choices and gaps, and coordinating assessment processes.</p>
Supporting Information: Assessing the Social Dimension in Strategic Network Design for a Sustainable Development: The Case of Bioethanol Production in the EU
<p>Supporting Information S2 provides all parameters of the optimization model, both those associated with the social objective and hotspot functions of the study at hand, and those from the underlying model by Wietschel et al. (2021). To put the method of the study into perspective, it also gives an overview of referenced frameworks (GSLCAPO, SHDB, SDGs) and their categories, subcategories, and indicators. Furthermore, it includes detailed results for all social, environmental, and economic objective functions in all scenarios, and of the Pareto optimization. Lastly, the underlying data for all figures of the manuscript and Supporting Information S1 is provided.</p>
Assessing Potential Groundwater Storage Capacity for Sustainable Groundwater Management in the Transitioning Post-Subsidence Metropolitan Area
<p>Citation: <br>Lin, S.‐H., Hu, J.‐C., & Wang, S.‐J. (2024). Assessing potential groundwater storage capacity for sustainable groundwater management in the transitioning post‐subsidence metropolitan area. Water Resources Research, 60, e2023WR036951. https://doi.org/10.1029/2023WR036951</p>
Quantitative Assessment of G7's Collaboration in Sustainable Development Goals
<div> </div> <p><strong>Authors</strong>: Kai Liu <sup>[1]</sup>, Ali Raisolsadat (<a href="mailto:arraisolsadat@uwaterloo.ca">arraisolsadat@uwaterloo.ca</a>) <sup>[2]</sup>, Xander Wang (<a href="mailto:xxwang@upei.ca">xxwang@upei.ca</a>) <sup>[3,4]</sup>, and Quan Van Dau (<a href="mailto:vdau@upei.ca">vdau@upei.ca</a>) <sup>[3,4]</sup></p> <p><strong>Institutions</strong>:</p> <ol> <li>School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada C1A 4P3</li> <li>Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1</li> <li>Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St. Peter's Bay, Prince Edward Island, Canada C0A 2A0</li> <li>School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada C1A 4P3</li> </ol> <p><strong>Corresponding Author</strong>: Dr. Xander Wang<br><strong>Contact Information</strong>: <a href="mailto:xxwang@upei.ca">xxwang@upei.ca</a></p> <div> <h2>Repository Contents</h2> </div> <p>This repository contains the code and data for the project titled "Quantitative Assessment of G7's Collaboration in Sustainable Development Goals".</p> <div> <h2>Information about the Folders</h2> </div> <ul> <li><strong><code>sdg_raw_data</code></strong>: Contains the raw Sustainable Development Goals indicator data from the "Our World in Data" database.</li> <li><strong><code>sdg_grouped_raw_data</code></strong>: Contains the raw SDG indicator data, but grouped for each goal (1-15).</li> <li><strong><code>results_datasets</code></strong>: Contains the main results for Domestic Changes, Foreign Changes, and Synergy data in <code>.CSV</code> and <code>.RData</code> formats.</li> <li><strong><code>main_manuscript_figures</code></strong>: Contains the 5 main figures used in the manuscript text.</li> <li><strong><code>s1_s12_supplementary_figures</code></strong>: Contains the 12 figures from the supplementary material of the manuscript.</li> <li><strong><code>partial_true_direction_un.csv</code></strong>: Contains the indicator directions from Table 1 of the manuscript.</li> <li><strong><code>SDG_Data.xlsx</code></strong>: An Excel file which contains all the data used in the manuscript results in multiple sheets, including SDG raw data and results datasets.</li> </ul> <div> <h2>Prerequisites</h2> </div> <ul> <li><strong>R</strong>: Please ensure that you have installed the latest version of the R software for your device. You can download it from <a href="https://cran.r-project.org/" rel="nofollow">CRAN</a>.</li> <li><strong>RStudio</strong>: It is recommended to use RStudio for running the R scripts. You can download it from <a href="https://rstudio.com/products/rstudio/download/" rel="nofollow">RStudio's official website</a>.</li> </ul> <div> <h2>How to Run</h2> </div> <ol> <li><strong>Download <a href="../api/records/11659806/draft/files/Synergy-2024-1.0.0.zip/content" target="_blank" rel="noopener noreferrer">Synergy-2024-1.0.0.zip</a> to your local computer and unzip it</strong></li> <li> <p><strong>Set the directory to the unzipped folder</strong></p> </li> <li><strong>Set the R working directory to the unzipped folder</strong></li> <li> <p><strong>Run Main Code</strong>:</p> <ul> <li>Open and run the <code>gross_synergy_markdown.Rmd</code> file. This is the main code for our manuscript.</li> <li>The resulting datasets will be saved in the <code>results_datasets</code> folder.</li> </ul> </li> <li> <p><strong>Generate Figure 1</strong>:</p> <ul> <li>Open and run <code>figure_1.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> folder.</li> </ul> </li> <li> <p><strong>Generate Figure 2</strong>:</p> <ul> <li>Open and run <code>figure_2.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> and <code>s1_s12_supplementary_figures</code> folders, respectively.</li> </ul> </li> <li> <p><strong>Generate Figure 3</strong>:</p> <ul> <li>Open and run <code>figure_3.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> folder.</li> </ul> </li> <li> <p><strong>Generate Figure 4</strong>:</p> <ul> <li>Open and run <code>figure_4.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> and <code>s1_s12_supplementary_figures</code> folders, respectively.</li> </ul> </li> <li> <p><strong>Generate Figure 5</strong>:</p> <ul> <li>Open and run <code>figure_5.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> folder.</li> </ul> </li> </ol>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.