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1,110 results for “accounting”
Monthly aerosol emissions and GHG concentration projections from 2020-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>This repository holds the netcdf files for emissions and concentrations projected by the scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020. The details of these activity estimates are available from <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/tree/endof2020</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> to have a different timeframe. </p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Four-year blip emissions changes due to COVID-19: modified SSP2-4.5 to account for sector activity level
<p>This repository holds the netcdf files for aerosol emissions projected by the scenario SSP2-4.5, from the Scenario4MIPs database ( <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown, projected out for 5 years after 2020 before returning to baseline. The details of these activity estimates runs in parallel to those described in <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a>, except instead of a 2-year blip, we have done a 4-year blip. Note that it is one year after the blip has finished before things return to baseline.</p> <p>The methodology behind these calculations is based on <a href="https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/">https://github.com/Rlamboll/modify_COVID19_netCDF_Emissions/</a>, a slight modification of the approach used in <a href="https://zenodo.org/record/3947917#.XxR_qyhKhPZ">https://zenodo.org/record/3947917#.XxR_qyhKhPZ</a> for aerosols emissions. We present only a single scenario (called 4-year blip, featuring a one year recovery after the end of the 4 years) compared to the baseline.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p>
Accounting for local temperature effect substantially alters afforestation patterns - data
<p>Output data produced in "Accounting for local temperature effect substantially alters afforestation patterns"</p> <p>Contact: michael.gregory.windisch@alumni.ethz.ch</p> <p>Content: Output of the partial equilibrium model MAgPIE (https://github.com/magpiemodel/magpie) version v4.3.1-BGP (https://github.com/magpiemodel/magpie/tree/v4.3.1-BGP) documentation (<a href="https://rse.pik-potsdam.de/doc/magpie/4.3.1/">https://rse.pik-potsdam.de/doc/magpie/4.3.1/</a>). Archived are all runs performed to obtain data and figures for the main assessment (main_output.zip) and the two sensitivity experiments (sensitivity_output_globalTCRE.zip; sensitivity_output_plantation.zip).</p> <p> </p> <p>Naming key:</p> <p>main_output control runs: BPHfillexp_ann_nobgp_{SSP}_noBECCS</p> <p>main_output experiment runs: BPHfillexp_ann_bph_{SSP}_local_{TCRE uncertainty}_noBECCS</p> <p>sensitivity analysis for global TCRE control runs: BPHfillexp_ann_nobgp_{SSP}_noBECCS</p> <p>sensitivity analysis for global TCRE experiment runs: BPHfillexp_ann_bph_{SSP}_global_{TCRE uncertainty}_noBECCS</p> <p>sensitivity analysis for plantation A/R control runs: BPH_plant_nobgp_{SSP}_noBECCS</p> <p>sensitivity analysis for plantation A/R experiment runs: BPH_plant_ann_bph_{SSP}_local_{TCRE uncertainty}_noBECCS</p>
Monthly CO2 emissions projections from 2015-2025: modified SSP2-4.5 to account for COVID-19 impacts on sector activity
<p>Monthly CO2 emissions projections 2015-2025, modified by country-specific impacts of COVID-19 lockdown in 2020-2023, with 4 different projections for the period 2024-2025. </p> <p>This repository holds the netcdf files for CO2 emissions from ground-level and aviation sources from the MESSAGE_GLOBIOM scenario SSP2-4.5, from the Scenario4MIPs database (<a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>), modified by the country and sector activity levels associated with lockdown for 2020. Sector activity level in 2020 is based on data up until June, and a fixed estimate is used thereafter. This is the monthly equivalent of <a href="https://zenodo.org/record/3951601#.XxYBsihKhPY">https://zenodo.org/record/3951601#.XxYBsihKhPY</a> for this time period.</p> <p>Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) <a href="http://constrain-eu.org/">http://constrain-eu.org/</a> </p> <p>see <a href="https://github.com/Priestley-Centre/COVID19_emissions">https://github.com/Priestley-Centre/COVID19_emissions</a> for more details.</p>
Internationalization in the Baltic Regional Accounts: A NUTS 3 Region Dataset
<p>This dataset is a NUTS 3 Region Dataset, corresponding to the Baltic regional accounts. The Baltic countries contain 21 NUTS 3 regions, distributed over 11 in Lithuania, 6 in Latvia and 5 in Estonia. Our dataset encompasses internationalization variables seldom available at regional level, involving trade, FDI, and migration. In addition, the dataset contains domestic variables on fixed capital, labor participation, price indexes, value added, information production and sharing, demographics, the population's well-being, and physical settlement features. All variables cover at least 2007 to 2015 and mostly up to 2019, but some variables cover a longer period from 1991 to 2020. By sharing the dataset, we hope to foster spatial research on internationalization and the spatial economy in the Baltics. When industry-specification is relevant and available, we report data across 11 industries, corresponding the classification applied in Eurostat and OECD's regional statistics. The dataset builds on a combination of micro and macro data sources. The collection and processing of the data is documented in the affiliated article: "<i>Holmen, R. B., Gavoille, N., Masso, J., & Burinskas, A. (2023). Internationalization in the Baltic Regional Accounts: A NUTS 3 Region Dataset, Data Descriptor, Data</i>". The dataset is provided in both Excel format and Stata format.</p>
Twitter Dataset for ≈15,000 accounts over a 1 day period
<p>This is almost 15,000 accounts on Twitter. Accounts were collected from a 1% sampled stream over a 24-hour period, from 2022-07-02T04:18:23.000Z to 2022-07-03T04:18:23.000Z.</p> <p>This data includes data from labeled data from https://doi.org/10.5281/zenodo.2653137, with an additional random sample of accounts from the stream to get to 15,000 accounts. Private profiles were removed.</p> <p>This data was collected with the intention of using it for unsupervised machine learning. You are free to do what you want with proper citation.</p>
Supplementary input data for accounting for component condition and preventive retirement in power system reliability of supply analyses
<div> <div>This data set contains supplementary data used for case studies on accounting for transformer condition in reliability of supply analyses in the following manuscripts: <br>1) H. Toftaker, J. Foros, I. B. Sperstad, "Accounting for component condition and preventive retirement in power system reliability of supply analyses", IET Generation, Transmission & Distribution, vol. 5, no. 1, 2023, DOI: 10.1049/gtd2.12761. <br>2) I. Bjerkebæk, I. B. Sperstad, H. Toftaker, G. Kjølle, "Simulating the Long Term Effect of Asset Management Strategies on Reliability of Supply", pre-print submitted for peer review, 2024. DOI: 10.36227/techrxiv.172107759.95745501/v1.</div> <div> See README.md for details.</div> </div>
Data to "Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>.<sup> †</sup>, Paulun, V. C.<sup> †</sup>, Klein, L. K. , & Fleming, R. W. (2018) Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection. <em>i-Perception,10</em>(1), 1–5. doi:10.1177/2041669519827608.</p> <p><sup>†</sup>co-first authors</p>
Supplementary File: Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia's most popular social network VK
<p>Supplementary file and dataset for the paper "Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK"</p>
[Dataset] Analysis of ego-networks of two CS-related Twitter accounts
<p><strong>Explanation/Overview:</strong></p> <p>This is the dataset for the analyses and results on Twitter Ego-Networks of two CS-related accounts (<a href="https://twitter.com/EuCitSci">@EuCitSci</a> & <a href="https://twitter.com/SciStarter">@SciStarter</a>). The username have been anonymised.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable. As such, it <strong>does not</strong> represent <strong>raw data</strong>, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R or any other network visualisation software (e.g., Gephi). The edges represent the follow relation and were retrieved using the Twitter API. All usernames except those of the two ego-accounts were anonymised by assigning a random number to each node. Due to the size of the network, we do not include any <code>.gexf </code>or <code>.gml </code>files in this upload, but rather resort to node and edge lists in the <code>.json</code> format</p> <p><strong>Relatedness:</strong></p> <p>The networks are the ego-networks for two related public accounts that are associated with citizen science (<a href="https://twitter.com/EuCitSci">@EuCitSci</a> & <a href="https://twitter.com/SciStarter">@SciStarter</a>).</p> <p><strong>Content:</strong></p> <p>In this Zenodo entry, two files can be found.</p> <ul> <li><code>edges.json</code></li> </ul> <p>Represents the edge list, with the columns:</p> <table> <tbody> <tr> <td><code>source</code></td> <td><code>target</code></td> <td><code>wherefrom</code></td> <td><code>account</code></td> </tr> <tr> <td>EuCitSci</td> <td>23929</td> <td>friendslist</td> <td>EuCitSci</td> </tr> </tbody> </table> <p><code>Source</code> and <code>target</code> are the necessary columns for the network creation and in this example indicate that EuCitSci follows 23929. <code>wherefrom </code>indicates the origin of this relation in the crawling (i.e., whether it was retrieved using the friends or follower list) and <code>account </code>indicates the ego-account it belongs to. Thus, the edges can also be separated using this <code>account </code>attribute as they represent two distinct networks.</p> <ul> <li><code>nodes.json</code></li> </ul> <p>Represents the nodes in the networks. The following data fields are contained:</p> <table> <tbody> <tr> <td><code>username</code></td> <td><code>CS</code></td> <td><code>followers_count</code></td> <td><code>friends_count</code></td> <td>...</td> </tr> <tr> <td>1116</td> <td>CS</td> <td>689</td> <td>514</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>favourites_count</code></td> <td><code>listed_count</code></td> <td><code>statuses_count</code></td> <td>...</td> </tr> <tr> <td>...</td> <td>2601</td> <td>18</td> <td>2141</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>degree</code></td> <td><code>in_degree</code></td> <td><code>out_degree</code></td> <td>...</td> </tr> <tr> <td>...</td> <td>21</td> <td>5</td> <td>16</td> <td>...</td> </tr> </tbody> </table> <table> <tbody> <tr> <td>...</td> <td><code>reciprocity</code></td> <td><code>account</code></td> </tr> <tr> <td>...</td> <td>0.48</td> <td>EuCitSci</td> </tr> </tbody> </table> <p> </p> <p><code>Username </code>represents the numerical and anonymised username, <code>CS</code> the community-membership. The different counts (e.g., <code>followers_count</code>) indicate the number of followers the user had at the time of the retrieval by the Twitter API. <code>degree</code> refers to the degree in the network (similarly the <code>in-</code> and <code>out_degree</code>), while <code>reciprocity</code> refers to the number of mutual edges in respect to the total number of edges per node (see <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.reciprocity.reciprocity.html">here</a>). <code>Account</code> is similar as described above.</p> <p> </p> <p><strong>Grouping:</strong></p> <p>The data is grouped according the ego-account it is associated to, as can be read above (i.e., the <code>account</code> attribute).</p>
Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches (Replication Package Part 3: OpenSSL dataset)
<h1><strong>The Replication Package of</strong></h1> <h1><strong>"Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches"</strong></h1> <h3><strong>Part 3 (OPENSSL Dataset)</strong></h3> <div> <div>This repository includes:</div> <ol> <li><em><strong>Code.zip</strong></em> that contains the codes to replicate some parts of this study:<br>a. <em>1_generate_datasets</em> implements our methodology to generate the datasets.<br>b. <em>2_run_models</em> runs the ML models during the evaluation.<br>c. <em>3_result_replication </em>generates charts presented in the paper from the ML evaluation results.</li> <li><em><strong>Datasets.zip</strong></em> that contain 2 folders:<br>a. <em>original</em> datasets: 1 from <a href="https://github.com/CGCL-codes/VulDeePecker" target="_blank" rel="noopener">NVD Vuldeepecker</a> and 3 extracted from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a>.<br> <div> <div>b. <em>OPENSSL</em> datasets: train, validation, test sets for each time of observation extracted using our methodology from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a> dataset for project <em>openssl</em>.</div> </div> </li> <li><em><strong>Pretrained-models.zip</strong></em> that we generated during our evaluation (3 test results for each time point in the timeline [2013-2019]).</li> <li><em><strong>Results.zip</strong></em> of our evaluation, the folder <em>ALL</em> contains the overall results and other folders are results by model.</li> </ol> <p><strong>UPDATED version 5<br></strong>- added a GLOBAL_README.md which contains the 3 stages and how they are connected to each other<br>- updated LineVul.ipynb: import AdamW from torch.optim instead of transformers<br>- updated README.md in Code2Vec with the prerequisites of Java to run gradlew for astmine</p> <p><strong>UPDATED version 6<br></strong>- updated CodeBert.ipynb: import AdamW from torch.optim instead of transformers</p> <p>Documentations</p> <ol> <li><em><strong>INSTALL.pdf </strong></em>: how to install the codes</li> <li><em><strong>README.pdf</strong></em>: readme file</li> <li><em><strong>REQUIREMENTS.pdf</strong></em>: hardware and software requirements</li> <li><em><strong>STATUS.pdf</strong></em> : status for artifact submission</li> <li><em><strong>LICENSE.pdf</strong></em>: the license of this artifact</li> <li><em><strong>PAPER.pdf</strong></em>: the camera-ready version of the paper</li> </ol> </div> <div> <div>Please refer to the following repositories for the other datasets and pre-trained models:</div> <div>- Part 1 NVD Vuldeeepecker : <a href="https://doi.org/10.5281/zenodo.8207883" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8207883</a></div> - Part 2 LINUX : <a href="https://doi.org/10.5281/zenodo.10960662" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10960662</a><br> <div>- Part 4 POPPLER : <a href="https://doi.org/10.5281/zenodo.14713143">https://doi.org/10.5281/zenodo.14713143</a></div> <div> </div> <div>This work was partly funded by the EU under the H2020 Program AssureMOSS (Grant n. 952647) and the Horizon Europe Program Sec4AI4Sec (Grant n. 101120393), by the Italian Ministry of University and Research (MUR) under the P.N.R.R. – NextGenerationEU grant n.\ PE00000014 (SERICS subproject COVERT), and by the Dutch Research Council (NWO) under the grant NWA.1215.18.006 (Theseus) and grant KIC1.VE01.20.004 (HEWSTI). </div> </div>
Figure: Occupational Fatality and Health Metrics within EU Consumption and Various Supply Chain Accounting Frameworks
<p><span><strong>Occupational Fatality and Health Metrics within EU Consumption and Supply Chain Contexts.</strong> Directly taken from (Koundouri et al., 2023) and reproduced with the authors' permission. </span><span>It displays in Figure A the</span><span> work-related fatal occupational injuries tied to goods finally consumed within the EU (Consumption Based Accounting -CBA- framework) and those within supply chains passing through the EU (Throughflow Based Accounting -TBA- framework) </span><span><span>(Beaufils et al., 2023) and</span></span><span> the results denote fatalities. It also displays in Figure B the D<span>isability-Adjusted Life Years</span><em><span> (</span></em></span><span>DALYs) associated with asbestos, asthmagen, and chromium-related occupational fatalities linked to European goods consumption (CBA) and traversing supply chains (TBA). Last, in Figure C, it provides</span><span> a comparative breakdown for each commodity from panels A and B, illustrating proportions by accounting framework (TBA vs. CBA).</span></p> <p><span>This figure put forward that despite potential barriers to target direct import intervention, optimized supply chain management can markedly reduce occupational fatalities related to global value chains passing through EU. As such, ILO frameworks such as Occupational Safety and Health Convention, 2006 (No. 187) </span><span><span>(International Labour Organization, 2006)</span></span><span>, and Occupational Safety and Health Convention, 1981 (No. 155) </span><span><span>(International Labour Organization, 1981)</span></span><span> offers a path to significant fatality reductions</span></p>
2023-JRFM-Accounting_Semi-Identity-Based_Models
<p>This dataset contains the same 2 files published in figshare.com but the data is in a more-opened format (csv separated with commas-decimals are points, for the data) to facilitate access. </p> <p>In the csv you can find the data, and in the txt you have the description of variables, which are the columns of the data file, and the lines of code for Sintax to run all analyses, both those included and those just mentioned, like for example, the robustness tests. </p>
Twitter Account Dataset - 10,000 accounts tested through the Botometer API
<p>This dataset contains 10,420 accounts extracted from Twitter from tweets containing the word "vaccine". We verified each one through the Botometer API.</p> <p>Includes:</p> <ul> <li>Account ID</li> <li>Echo Chamber Score (0-5)</li> <li>Fake Follower Score (0-5)</li> <li>Financial Score (0-5)</li> <li>Self Declared Score (0-5)</li> <li>Spammer Score (0-5)</li> <li>Other Score (0-5)</li> <li>Overall Score (0-5)</li> </ul> <p>Those appearing with an 'X' correspond to deleted, modified or private accounts.</p>
A neuro-metabolic account of why daylong cognitive work alters the control of economic decisions
<p>This is the data to reproduce the main analysis of the article "A neuro-metabolic account of why daylong cognitive work alters the control of economic decisions".</p> <p>Please contact antonius.wiehler@gmail.com if you have any questions.</p>
Data from: Accounting for predator species identity reveals variable relationships between nest predation rate and habitat in a temperate forest songbird
<p><strong>Abstract</strong></p> <p>Nest predation is the primary cause of nest failure in most ground-nesting bird species. Investigations of relationships between nest predation rate and habitat usually pool different predator species. However, such relationships likely depend on the specific predator involved, partly because habitat requirements vary among predator species. Pooling may therefore impair our ability to identify conservation-relevant relationships between nest predation rate and habitat. We investigated predator-specific nest predation rates in the forest-dependent, ground-nesting wood warbler <em>Phylloscopus sibilatrix </em>in relation to forest area and forest edge complexity at two spatial scales, and to the composition of the adjacent habitat matrix. We used camera traps at 559 nests to identify nest predators in five study regions across Europe. When analysing predation data pooled across predator species, nest predation rate was positively related to forest area at the local scale (1,000 m around nest), and higher where proportion of grassland in the adjacent habitat matrix was high but arable land low. Analyses by each predator species revealed variable relationships between nest predation rates and habitat. At the local scale, nest predation by most predators was higher where forest area was large. At the landscape scale (10,000 m around nest), nest predation by buzzards <em>Buteo buteo</em> was high where forest area was small. Predation by pine martens Martes martes was high where edge complexity at the landscape scale was high. Predation by badgers <em>Meles meles </em>was high where the matrix had much grassland but little arable land. Our results suggest that relationships between nest predation rates and habitat can depend on the predator species involved and may differ from analyses disregarding predator identity. Predator-specific nest predation rates, and their relationships to habitat at different spatial scales, should be considered when assessing the impact of habitat change on avian nesting success.</p>
Australian waste account 2007-2021
<p>This dataset updates the previous release from 2019 to 2021, in line with the <a href="https://www.dcceew.gov.au/environment/protection/waste/national-waste-reports/2022">National Waste Database</a> update. The previous release is available here: https://zenodo.org/records/5646740. This release also incorporates LGA waste generation data where it is available (<a href="https://www.epa.nsw.gov.au/-/media/epa/corporate-site/resources/wastestrategy/23p4660-lg-warr-report-2021-22.pdf">NSW </a>and <a href="https://www.vic.gov.au/victorian-local-government-waste-data-dashboard">Victoria</a>).</p> <p>Updated notes: </p> <p>The National Waste Database (https://www.awe.gov.au/environment/protection/waste/national-waste-reports/2020) is a repository for Australia's solid waste data. This collection of waste data is useful however has some issues: The timeseries is not complete, as some years are missing. Allocation to industries is very coarse, there are only 3 waste generating entities: construction and demolition, commercial and industrial, and municipal (households). Further, not all reporting regions (States and Territories) provide data at the same resolution of material type.</p> <p>We have created an open source dataset in an attempt to solve some of these issues. Missing years are filled using linear interpolation. The regional resolution is disaggregated to SA2 regions using the ABS Business Register (https://www.abs.gov.au/Ausstats/abs@.nsf/0/49658AFA6CC395CECA2583A700121A41). Municipal (households) waste is split into SA2s from state totals using population. The ABS Waste Account is used to establish a relationship between waste types and generating sectors (https://www.abs.gov.au/statistics/environment/environmental-management/waste-account-australia-experimental-estimates/latest-release).</p> <p>The data is published as labelled flat files (.csv). The dataset dimensions are:</p> <p>- years: 2007 - 2021,</p> <p>- regions: 2310 SA2 (2016) ASGS regions,</p> <p>- entities: 116 generating entities; 115 SUPG (supply-use product group) industries + 1 households,</p> <p>- waste_types: 69 waste material types,</p> <p>- treatments: 5 waste treatment methods</p> <p>This dataset is made available under a Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/</p>
Dataset from "Natural capital accounting reveals ecosystems' role in water and energy security in Colombia's Sinú Basin"
<p>The data archived here are associated with the publication titled "Natural capital accounting reveals ecosystems' role in water and energy security in Colombia's Sinú Basin", available at: <a href="https://doi.org/10.1038/s43247-025-02254-9">https://doi.org/10.1038/s43247-025-02254-9</a>. The files within "Sinu_SDR_inputs.zip" and "Sinu_SWY_inputs.zip" were prepared and run in <a href="http://releases.naturalcapitalproject.org/?prefix=invest/3.12.0/">InVEST version 3.12.0</a>. "SDR" refers to the InVEST Sedimnet Delivery Ratio (SDR) model and "SWY" refers to the InVEST Seasonal Water Yield (SWY) model. Results of these model runs are found within "Sinu_SDR_results.zip" and "Sinu_SWY_results.zip" for the SDR and SWY models, respectively. These models were calibrated using observed data on average monthly water flows (from 1959 to 1992) and average annual sediment loads (from 1972 to 1992) from gauge stations on Colombia's Sinú River. Those observed data were obtained from Colombia's Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) hydrometeorological monitoring network <a href="http://dhime.ideam.gov.co/atencionciudadano/">webportal</a> and are summarized in the files included here, "MeanMonthlyObservedFlowsXgaugeStation.csv" for monthly water flows and "annualObservedSedimentXgaugeStation.csv" for annual sediment loads. "EcosystemTypeTable.xlsx" is the table of ecosystem values. "Cuenta_Sinu_SankeyData_v2_paper.xlsx" contains the Sankey and accounts tables.</p>
BotHawk: GitHub Bot Account Detection Dataset
<div> <p>The <strong>"BotHawk: GitHub Bot Account Detection Dataset"</strong> is a specialized resource developed for the research and development community focused on identifying and understanding bot accounts within the GitHub ecosystem. This comprehensive dataset offers an in-depth look into the behaviors, contributions, and patterns of bot accounts, distinguishing them from human user activities on GitHub.</p> <p>Designed to enhance the accuracy and efficiency of bot detection algorithms, the BotHawk Dataset encompasses a variety of features, including commit history, issue participation, pull request activities, and other interaction metrics specific to GitHub. It provides an invaluable foundation for developing machine learning models capable of distinguishing between bot-generated and human-generated activities, contributing to more secure and trustworthy interactions within the GitHub platform.</p> <p>This dataset not only serves as a tool for academic research in cybersecurity, machine learning, and software engineering but also offers practical applications for developers and platform administrators seeking to improve the health of their projects by filtering out bot-based noise and manipulation. The BotHawk dataset is instrumental in advancing our comprehension of bot dynamics within the GitHub ecosystem, supporting the broader objective of fostering a secure, transparent, and collaborative open-source community.</p> </div>
Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action
<p>Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a ’care in the community’ framework.</p>
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.