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Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)
<p>This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX).</p> <p>The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France); Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.</p> <p>Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant.</p> <p>The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate.</p> <ol> <li> <p><strong>EXPERIMENT DESIGN</strong></p> </li> </ol> <p>We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005).</p> <p>We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022).</p> <p>We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named <strong>historical CO</strong><strong>2</strong> (<strong>hist CO</strong><strong>2</strong>); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named <strong>constant CO</strong><strong>2</strong><strong> (const CO</strong><strong>2</strong><strong>)</strong>. We ran both CO2 experiments under <strong>Land Use Change</strong> (<strong>LUC</strong>) and <strong>Potential Natural Vegetation </strong>(<strong>PNV</strong>) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. <strong>LUC historical CO</strong><strong>2</strong>; 2. <strong>LUC constant CO</strong><strong>2</strong>; 3. <strong>PNV historical CO</strong><strong>2</strong>; 4. <strong>PNV constant CO</strong><strong>2 </strong> (Rezende et al., 2022).</p> <p><strong>2. DATA DESCRIPTION</strong></p> <p>The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs.</p> <p><strong>2.1 SOFTWARE </strong></p> <p><br> The data were manipulated, worked, standardized, converted using the software: <strong>Climate Data Operators (cdo) version 1.7.0</strong>, <strong>Grid Analysis and Display System (Grads) (</strong><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a><strong>) version 2.0.2, and RStudio Desktop version</strong> 1.3.1093 <strong>(R Core Team, 2020)</strong>, through command lines and several scripts developed for this purpose. The figures were generated with <strong>Grads,</strong> and <strong>RStudio</strong>, and some images were enhanced with <strong>Gimp version 2.8.22</strong>. All the software used is freeware.</p> <p><strong> 2.2 PRIMARY DATA FROM SIMULATIONS</strong></p> <p>Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010.</p> <p><strong>Variables</strong>: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p>The naming of the files is according to the following rules:</p> <p><strong>DGVM_forcing_vegetation cover_CO2 concentration_attribute</strong></p> <p><strong>DGVMs</strong>;</p> <p> InLand (INLAND)</p> <p> LPJ-G (LPJ-GUESS)</p> <p> LPJmL (LPJmL4)</p> <p> ORCHI (ORCHIDEE)</p> <p>forcings: </p> <p> gld - GLDAS</p> <p> gsw – GSWP3</p> <p> wat – WATCH+WFDEI</p> <p> </p> <p>vegetation cover:</p> <p> LU – Land Use Change</p> <p> PNV – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration:</p> <p> CO2 – historical CO2</p> <p> noCO2 – constant CO2 = 278 ppm</p> <p> </p> <p>variables:</p> <p> E – evaporation</p> <p> Et – transpiration</p> <p> gpp – Gross Primary Productivity</p> <p> npp - Net Primary Productivity (not used in the experiment)</p> <p> </p> <p><strong>Example</strong>:</p> <p> inLand_gld_LU_noCO2_E.nc</p> <p> </p> <p><strong> 2.3 SUPPLEMENTARY DATA </strong></p> <p>These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).</p> <p>Files are named according to the following rules:</p> <p>variable_season_forcing_DGVM_vegetation cover CO2 concentration_region</p> <p>variable:</p> <p> inter – loss by interception</p> <p> </p> <p>season:</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>forcings: </p> <p> gl - GLDAS</p> <p> gs – GSWP3</p> <p> wa – WATCH+WFDEI</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or – ORCHIDEE</p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p> inter_D_gl_in_LC_SA.nc</p> <p><strong>2.4 PROCESSED DATA</strong></p> <p>Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p> </p> <p>Files are named according to the following rules:</p> <p> </p> <p><strong>season_DGVM_forcing_vegetation cover CO2 concentration_region</strong></p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>Example:</p> <p>D_in_Gs_LN.nc</p> <p><strong>2.5 DERIVED DATA</strong></p> <p> </p> <p>The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1). </p> <p> </p> <table> <tbody> <tr> <td> <p> WUE = GPP / Tr</p> </td> <td> <p>(Eq. 1)</p> </td> </tr> </tbody> </table> <p> </p> <p>These data refer to the study region: southern Amazon and apply to only one scenario: Land Use Change and historic CO2. Files are named naming of according to the following rules:</p> <p> </p> <p>Variable:</p> <p> WUE – Water Use Efficiency</p> <p> </p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p>wue_D_lm_Gl_LC_SA.nc</p> <p> </p> <p><strong>How to cite this work</strong>:</p> <p>Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p><strong>References</strong></p> <p><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015.</p> <p>Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a></p> <p>Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199.</p> <p>R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. </p> <p>Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg.</p> <p>Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6.</p> <p>Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p>Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>.</p> <p>Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637</p> <p>Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36</p> <p><br> </p> <p> </p>
Dataset of the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"
<p>This archive contains the artifacts (datasets) for the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"</p>
DeepLabCut network trained to track mouse body parts during open field locomotion (top-down view)
<p>DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. <em>Open field: </em>20 images from 19 videos (10 or 30 fps) were extracted for a total of 380 labeled pictures. 8 body parts (snout, both ears, body center, both side laterals, tail base and tail end) and the 4 corners of the field arena were manually labeled and linked to each other using skeletons. A neural network was trained using these images for 170K iterations. 20 outlier frames were extracted from each video and relabeled. An additional 20 images from 19 videos with different recording conditions were labeled. The network was then refined for 210K iterations (from scratch), yielding a train error of 3.33 pixels and a test error of 8.83 pixels (with a likelihood p-cutoff of 0.6). This process was repeated a second time (using an additional 20 images from 15 new videos) to improve the pixel error; to a final 400 K iterations (train error: 2.65, test error: 3.71). 67 videos from 5 different experiments were analyzed on the final network.<em> </em></p> <p><em>Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023)</em></p>
Dataset accompanying Riesch et al. 2022. Grazing by wild red deer can mitigate nutrient enrichment in protected semi-natural open habitats. Oecologia
<p>This repository contains the data set on nutrient fluxes through wild red deer used by Riesch et al. 2022 in an article puplished in <em>Oecologia</em> (accepted 2022-05-01).</p> <p>Metadata are provided in the first excel worksheet. For further details please see the original article (DOI: 10.1007/s00442-022-05182-z).</p>
Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"
<p><br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"</p> <p><br> Description of the content:</p> <p><br> 1) "1_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps </p> <p><br> 2) "2_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: </p> <p><br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 3) "3_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.</p>
i-SoMPE Inventories A and B: open factor data
<p>Open data (factor data) of the inventory (A and B)</p>
How green is my valley? Measuring open access friendliness of Indian Institutes of Technology (IITs) through data carpentry (dataset)
<p>This data set is related to the book chapter with the following bibliographic details - Mukhopadhyay, P. (2022). How green is my valley? Measuring open access friendliness of Indian Institutes of Technology (IITs) through data<br> carpentry. In A. Biswas & M. Das Biswas (Eds.), Panorama of open access: Progress, practices & prospects (1st ed., pp. 67–89). Ess Ess. https://doi.org/10.5281/zenodo.6511080.</p> <p>It includes the truncated version of the final data set that has been used for analyzing Open Access Friendliness (OAF) of the Indian Institutes of Technology (IITs). The zipped version of the data set is around 95 MB (465 MB after decompress).</p>
TOPS Open Science Graphics
<p>Two TOPS figures promoting open science.</p>
Global demand data for PyPSA-Earth: An Open Optimisation Model of the Earth Energy System.
<p><strong>PyPSA-Earth </strong>is an open model dataset of the global power system at different network levels that cover our Earth. The African model can be built using the code provided at <a href="https://github.com/pypsa-meets-africa/pypsa-africa">https://github.com/pypsa-meets-africa/pypsa-africa</a>. Other regions follow soon under the same code base.</p> <p>Since the GitHub codebase is not suited for handling large changing files, we provide here separate <strong>data bundles and cutouts</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-meets-africa.readthedocs.io/en/latest/index.html">documentation</a></p> <p>The below-provided <strong>resource file </strong>contains demand time-series generated by <a href="https://github.com/niclasmattsson/GlobalEnergyGIS/blob/b23206f8701acafdf7359f9cc952dfd4e7b819e5/src/downloaddatasets.jl">GEGIS</a> covering the world. The time series are produced for different socio-economic scenarios (SSP), weather years, and prediction years<strong>.</strong></p>
Forschungsdaten zur Masterarbeit "Open Science und wissenschaftliche Bibliotheken. Disruptive Potentiale digitaler Transformation am Beispiel der Sächsischen Landesbibliothek — Staats- und Universitätsbibliothek Dresden"
<p><em>--- english version below ---</em></p> <p>Im Rahmen des Fernstudiengangs Bibliotheks- und Informationswissenschaften habe ich die Masterarbeit mit dem Titel "Open Science und wissenschaftliche Bibliotheken. Disruptive Potentiale digitaler Transformation am Beispiel der Sächsischen Landesbibliothek — Staats- und Universitätsbibliothek Dresden" im Zeitraum vom 17.02.2022 bis 17.06.2022 angefertigt. </p> <p>Für die Arbeit wurden Mitarbeitende in Interviews zum Thema "Open Science" befragt. Die Interviews wurden nach der Repertory-Grid-Technik durchgeführt und analysiert nach der Interpretive-Clustering-Methode, die erstmals <a href="http://doi.org/10.1080/14780887.2020.1794088">2020 von Burr, King und Heckmann</a> beschrieben wurde. Unterstützend konnte ein von Mark Heckmann entwickeltes Tool zur IC-Analyse verwendet werden: <a href="http://ic.openrepgrid.org/">http://ic.openrepgrid.org/</a>.</p> <p>Im Vorfeld der Interviews mussten Elemente als Basis gefunden werden. Die Elemente wurden mithilfe einer Korpusanalyse eines SLUB-Textkorpus mit dem Tool <a href="https://www.sketchengine.eu/">Sketch Engine</a> und nachfolgender Kuratierung gefunden. Es handelt sich um Nomina, die - nach logDice gewichtet- die höchste Kollokation zu den Begriffen "offen, Offenheit, offenbar" haben. Gewählt wurden:</p> <ul> <li>Werkstatt</li> <li>Kulturdaten</li> <li>Schnittstelle</li> <li>Zugang</li> <li>Meinungsfreiheit</li> <li>Lizenz</li> <li>Standard</li> <li>Wissenschaft</li> <li>Wissen</li> <li>Austausch</li> <li>Makerspace</li> </ul> <p><strong>Als Forschungsdaten zur Arbeit liegen vor:</strong></p> <ul> <li>Ergebnisse der Kollokationsanalyse zu "offen", "Offenheit" und "offenbar"</li> <li>Interviewleitfaden für die Interviews nach Repertory-Grid-Technik</li> <li>Vorlage für den ersten Teil der Interviews (Konstruktbildung)</li> <li>Vorlage für den zweiten Teil der Interviews (Skaleneinordnung der Elemente)</li> <li>Ergebnisse des ersten Teils der Interviews (Konstruktbildung)</li> <li>Ergebnisse des zweiten Teils der Interviews (Skaleneinordnung der Elemente)</li> <li>Ergebnisse der Analyse der Interviews nach der Interpretive-Clustering-Methode</li> <li>Beispiel für die (ungenügende) automatische Transkription eines Interviewanfangs mithilfe der Google Speech to Text API</li> </ul> <p>-----</p> <p><em>english version</em></p> <p>As part of the distance learning course in Library and Information Science, I wrote the Master's thesis entitled "Open Science and academic libraries. Disruptive potentials of digital transformation using the example of the Saxon State Library - State and University Library Dresden" in the period from 17.02.2022 to 17.06.2022. </p> <p>For the work, employees were questioned in interviews on the topic of "Open Science". The interviews were conducted using the repertory grid technique and analysed using the interpretive clustering method, which was first described by <a href="http://doi.org/10.1080/14780887.2020.1794088">Burr, King and Heckmann in 2020</a>. A tool developed by Mark Heckmann for IC analysis could be used as a support: <a href="http://ic.openrepgrid.org/">http://ic.openrepgrid.org/</a>.<br> In the run-up to the interviews, elements had to be found as a basis. The elements were found with the help of a corpus analysis of an SLUB text corpus with the tool <a href="http://www.sketchengine.eu/">Sketch Engine</a> and subsequent curation. These are nouns which - weighted according to logDice - have the highest collocation to the terms "open, openness, apparent". The following were chosen:</p> <ul> <li>Werkstatt</li> <li>Kulturdaten</li> <li>Schnittstelle</li> <li>Zugang</li> <li>Meinungsfreiheit</li> <li>Lizenz</li> <li>Standard</li> <li>Wissenschaft</li> <li>Wissen</li> <li>Austausch</li> <li>Makerspace</li> </ul> <p><strong>The research data available for the work are:</strong></p> <ul> <li>Results of the collocation analysis on "open", "openness" and "apparently".</li> <li>Interview guide for the interviews according to the repertory grid technique</li> <li>Template for the first part of the interviews (construct formation)</li> <li>Template for the second part of the interviews (scale classification of the elements)</li> <li>Results of the first part of the interviews (construct formation)</li> <li>Results of the second part of the interviews (scale classification of the elements)</li> <li>Results of the analysis of the interviews according to the interpretive clustering method</li> <li>Example of (insufficient) automatic transcription of an interview beginning using the Google Speech to Text API</li> </ul> <p> </p>
Acknowledged scholars extracted from open access journals
<p><strong>Published paper</strong><br> For details of the data development: </p> <p>Kusumegi, K., Sano, Y. Dataset of identified scholars mentioned in acknowledgement statements. Sci Data 9, 461 (2022). https://doi.org/10.1038/s41597-022-01585-y</p> <p>To use the data, please cite the above mentioned paper .</p> <p> </p> <p><strong>Data on scholars mentioned in acknowledgments extracted from open access journals.</strong></p> <p>This data firstly updated the repository at University of Tsukuba (Division of Policy and Planning Sciences Commons).</p> <p>https://commons.sk.tsukuba.ac.jp/data_en</p> <pre> <strong>Each csv correspond to:</strong> biology.csv as PLOS Biology compbiology.csv as PLOS Computational Biology genetics.csv as PLOS Genetics medicine.csv as PLOS Medicine ntds.csv as PLOS Neglected Tropical Diseases pathogenes.csv as PLOS Pathogens plosone.csv as PLOS ONE srep.csv as Scientific Reports Data details: Doi: DOI of the paper PaperId: Paper ID in MAG AcknowledgedId: Acknowledged scholar's ID in MAG CollaborationApproach: Acknowledged scholar is identified by collaboration relationship CitationApproach: Acknowledged scholar is identified by citation relationship </pre> <p> </p>
The human splenic microcirculation is entirely open as shown by 3D models in virtual reality. Supplementary files
<p>These materials supplement our paper "The human splenic microcirculation is entirely open as shown by 3D models in virtual reality".</p> <p><strong>Summary</strong></p> <p>The human spleen is equipped with an organ-specific microcirculation. The initial part of the venous circulation is formed by spleen-specific large microvessels, the sinuses. Sinuses eventually fuse to form venules and veins. For more than 170 years there have been debates, whether splenic red pulp capillaries join sinuses, i.e., whether the microcirculation is closed or open - or even simultaneously closed and open. We have now solved this question by three-dimensional reconstruction of a limited number of immunostained serial sections of red and white pulp areas, which were visualized in virtual reality. Splenic capillaries have special end structures exhibiting multiple small diverging endothelial cell processes, which always keep a certain distance to the walls of sinuses. Only very few capillary ends were difficult to diagnose. Positive identification of these end structures permits to conclude that the human splenic microcirculation is entirely open. This is also true for the perifollicular capillary network and for capillaries close to red pulp venules. Follicles are supplied by a relatively dense open perifollicular capillary net, which is primarily, but not exclusively, fed by sheathed and few non- sheathed capillaries from the surrounding red pulp network.</p> <p> </p> <p><strong>Interactive models</strong></p> <p>Each <em>file_sX.zip</em> contains a 3D model, registered sequence of serial sections (the input data to generate and validate the model), and an interactive / VR viewer. After you have unzipped the file, there are multiple batch files. Any of them can be used for an interactive model display on a normal monitor, but we recommend the file <em>start_index.bat</em>. Now, if you have a virtual reality headset or a 4K monitor, there are better options:</p> <ul> <li>If you have a HTC Vive-compatible headset (i.e., the "wand" controllers, typically coming with HTC Vive, Vive Pro, Vive Pro 2, etc.), please use <em>start_vive.bat</em>.</li> <li>If you have a Valve Index headset (i.e., the "knuckles" controllers, should also work with Oculus devices), please use <em>start_index.bat</em>.</li> <li>If you have a high-resolution monitor (4K or better), please use <em>start_interactive_4k.bat</em> for a high-resolution interactive model.</li> </ul> <p>In the zip-file is also a <em>README.txt</em> file with instructions on the controls for the interactive and VR viewer. It is also available in the viewer at press of F1 button. In a nutshell: </p> <ul> <li>ASWD control the movement</li> <li>I (the "i" key) turns the sections on and off</li> <li>JK advance the sections</li> <li>CV adjust the height</li> <li>E turns the model on and off.</li> </ul> <p>The viewer executables are built for Windows. They work with Windows 10, should work with previous versions of Windows (64 bit) and also with future versions, such as Windows 11. Users of other OS, such as MacOS or Linux, can use <a href="https://www.meshlab.net/">MeshLab</a> to look at the model. Any image viewer can be used to inspect the sections in the <em>img</em> folder of the unpacked zip file. However, in this case, no VR experience and no simultaneous view of both 3D reconstruction and the sections is possible.</p> <p> </p> <p><strong>Videos</strong></p> <p>The videos with the same content are mostly supplied in three versions:</p> <ul> <li>On any modern hardware you should be able to play the H.265 videos, ending in <em>...4K_h265_10bit.mov</em>, there are in 4K resolution</li> <li>If it is not the case, but you want 4K resolution, use the H.264 version, ending in <em>...4K_h264_10bit.mov,</em> it is also in 10 bit quality</li> <li>A fallback for weaker hardware and maximal compatibility is the H.264 FullHD version. It should play anywhere. Those files end in <em>...1080p_h264.mov.</em></li> </ul> <p> </p> <p><strong>Supplementary Figures S1 and S2</strong></p> <p>The supplementary figures with their legends are available in the file <a href="https://zenodo.org/record/6599487/files/supp.pdf"><em>supp.pdf</em></a>.</p> <p> </p> <p><strong>3D models corresponding to Figs. 4a-d</strong></p> <p><a href="https://zenodo.org/record/6599487/files/file_s1.zip?download=1">Supplementary file S1</a>. 3D model of ROI 1 with open capillary ends in red</p> <p><a href="https://zenodo.org/record/6599487/files/file_s2.zip?download=1">Supplementary file S2</a>. 3D model of ROI 2 with open capillary ends in red</p> <p><a href="https://zenodo.org/record/6599487/files/file_s3.zip?download=1">Supplementary file S3</a>. 3D model of ROI 3 with open capillary ends in red</p> <p><a href="https://zenodo.org/record/6599487/files/file_s4.zip?download=1">Supplementary file S4</a>. 3D model of ROI 4 with open capillary ends in red</p> <p> </p> <p><strong>File corresponding to Fig. 7a</strong></p> <p><a href="https://zenodo.org/record/6599487/files/file_s5.zip?download=1">Supplementary file S5</a>. 3D model of sinus network and open capillary ends in red</p> <p> </p> <p><strong>Files corresponding to Figs. 9a,b</strong></p> <p><a href="https://zenodo.org/record/6599487/files/file_s6.zip?download=1">Supplementary file S6</a>. 3D model of ROI 2 with perifollicular capillary network in red correspondig to Fig. 9a</p> <p><a href="https://zenodo.org/record/6599487/files/file_s7.zip?download=1">Supplementary file S7</a>. 3D model of ROI 2 with perifollicular capillary network in red and open ends in yellow corresponding to Fig. 9b</p> <p> </p> <p><strong>Videos corresponding to Figs 5a-f</strong></p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s1_4K_h264_10bit.mov">Supplementary video S1</a>. Two capillaries with open ends in Fig. 5a-c</p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s2_4K_h264_10bit.mov">Supplementary video S2</a>. Capillary with at least two open ends in Fig. 5d-f</p> <p> </p> <p><strong>Videos corresponding to Figs 6a-d</strong></p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s3_1080p_h264.mov">Supplementary video S3</a>. Quality control of open ends shown in Fig. 5a-c and Fig. 6a,b</p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s4_1080p_h264.mov">Supplementary video S4</a>. Quality control of open end shown in Fig 5d-f and Fig. 6c,d</p> <p> </p> <p><strong>Videos corresponding to Fig. 4d and Figs 8a-f</strong></p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s5_1080p_h264.mov">Supplementary video S5</a>. Control of open capillary end shown in Fig. 8a-c</p> <p><a href="https://zenodo.org/record/6599487/files/sinus_-_video_s6_1080p_h264.mov">Supplementary video S6</a>. Control of open capillary end shown in Fig. 8d-f</p>
Core bibliometric Covid19 and comparable research dataset and code for the study "From intent to impact: Investigating the effects of open sharing commitments"
<p>This document provides the underlying dataset for the bibliometric component for the 2022 study "From intent to impact: Investigating the effects of open sharing commitments" by Research Consulting and Science-Metrix.</p> <p>Before reproducing the study findings or re-using the underlying datasets for other purposes, please cautiously review their limitations in the study's technical annex and main report, available at: https://zenodo.org/communities/data-sharing-in-public-health-emergencies/ </p> <p>Particularly, note that there is an error rate in attribution of signatory status to journal publications and preprints; in their location within specific thematic disease-based areas; or computing of dimension such as identification of data availability statement sections; identification of data depisition mentions within data availability statement sections; or matching of preprints and journal publications.</p> <p>These error rates are expected and have been estimated, please consult the technical report for full details.</p> <p> </p> <p>Definition of data fields is provided is the table below:</p> <table> <tbody> <tr> <td>Column name </td> <td>Definition</td> </tr> <tr> <td>document_type</td> <td>preprint or journal publication</td> </tr> <tr> <td>doi</td> <td>digital object identifier</td> </tr> <tr> <td>arxiv_id</td> <td>arXiv preprint server's unique identifier for its preprints</td> </tr> <tr> <td>ssrn_id</td> <td>SSRN preprint server's unique identifier for its preprints. Note that some of these IDs are contained within the DOIs also assigned to some (but not all) SSRN preprints , in the form of "10.2139/ssrn." + 'ssrn_id'</td> </tr> <tr> <td>coalesce_id</td> <td>coalesce function applied to the DOI, arxiv_id and ssrn_id. Redundant for journal publications.</td> </tr> <tr> <td>preprint_server</td> <td>Preprint platform on which a preprint has been published, restricted to arXiv, bioRxiv, medRxiv and SSRN for this study.</td> </tr> <tr> <td>journal_title</td> <td>Publishing journal name in the case of a journal publication.</td> </tr> <tr> <td>year</td> <td>The set is restricted to 2020 and 2021 for Covid19 preprints and journal publications. HVRD journal publications restricted to 2018-2019. HVRD preprints were restricted to 2020-2021 instead, to compensate for the lac of year-normalization for preprints, and generally better control findings against the launch of medRxiv in 2019.</td> </tr> <tr> <td>publication_title</td> <td>Title of the individual journal publication or preprint, not that of the publishing journal or preprint server.</td> </tr> <tr> <td>authors</td> <td>First 100 researchers that appear as authors of a preprint or journal publication. These are not parsed and provided for qualitative validation or assessments rather than for further quantitative treatment.</td> </tr> <tr> <td>Covid19</td> <td>Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>HVRD</td> <td>Human viral respiratory disease, the thematic area considered to be the closest to Covid19. Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>Journal_sig</td> <td>Journal publications where the publishing journal and/or its publishing house are Joint Statement signatories. Coded as 1 if they are signatories, 0 if not signatory, null if status could not be determined due to insufficient metadata. Not that all preprint servers included in this study are Joint Statement signatories. This category was fully removed from the models for preprints, rather than all preprints being assigned automatic signatory status.</td> </tr> <tr> <td>RPO_sig</td> <td>Journal publications and preprints where at least one author is affiliated with at least one research performing organization that is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata.</td> </tr> <tr> <td>Funder_sig</td> <td>Journal publications and preprints where at least one funder supporting the research is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata. Although funding is attributed to researchers rather than publications, funding metadata is more readily available at the second level. This approach also captures the flexible usage of financial resources that researchers may make accross mulitple concurrently ongoing research projects.</td> </tr> <tr> <td>overton_norm</td> <td>Year and subfield-normalized binary score of whether the journal publications has been cited by one or more policy-related documents from the Overton database. Null scores for journal publications not covered by the database.</td> </tr> <tr> <td>overton</td> <td>Normalizations being unable for preprints, binary score of whether the preprint has been cited by one or more policy-ralated documents from the Overton database. Null scores for preprints not covered by the database.</td> </tr> <tr> <td>daswriting_binary</td> <td>Binary score capturing identification of a data availability statement in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>deposition_binary</td> <td>Binary score capturing identification of a data availability statement and data deposition mention therein in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>is_oa</td> <td>Binary score capturing OA or free-to-read (also so-calleod "bronze OA" and "green OA") status of journal publications. Unpaywall categories have been used in a mutually exclusive implementation, with the best (gold > hybrid>bronze>green) possible applicable category being retained. Null scores for journal publications not covered in our Unpaywall dataset. Scores of 0 denote journal publications not available under an OA or free-to-read category.</td> </tr> <tr> <td>is_gold</td> <td>as above</td> </tr> <tr> <td>is_hybrid</td> <td>as above</td> </tr> <tr> <td>is_bronze</td> <td>as above</td> </tr> <tr> <td>is_green</td> <td>as above</td> </tr> <tr> <td>matched_journal_binary</td> <td>For preprints, whether one or more matching journal publications could be identified using the queries identified in the technical, or preprint servers' own lists of preprint-journal publication matches. Null scores for preprints with insufficient metadata information to perform the matching operation.</td> </tr> <tr> <td>matched_journal_doi</td> <td>For those preprints with or more matching journal publications, the DOI(s) of the matching journal publication(s). Note that some of the maching journal publications identified do not have DOIs.</td> </tr> <tr> <td>matched_preprint_binary</td> <td>For journal publications, whether one or more matching preceding preprints could be identified using the queries identified in the technical annex, or preprint servers' own lists of preprint-journal publication matches. Null scores for journal publications without sufficient metadata to run the analysis.</td> </tr> <tr> <td>matched_preprint_id</td> <td>For those journal publications preceded with one or more arXiv, bioRxiv, medRxiv or SSRN preprints, the DOI(s), arXiv ID and/or SSRN ID of the matching preprint(s). </td> </tr> <tr> <td>hasdoi</td> <td>Only journal publications with DOIs were retained in the core quantitative analyses.</td> </tr> <tr> <td>hasacknowledgements</td> <td>Only journal publications with funding acknowledgements (to determine funding-based signatory status) were retained in the core quantitative analyses.</td> </tr> <tr> <td>funder_array</td> <td>Array (but cast as string) of names of the funders on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>RPO_array</td> <td>Array (but cast as string) of names of the research performing organizations on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>DAS_excerpt</td> <td>Journal publication or preprint text excerpt on which succesful identifcation of data availability statements and/or data deposition mentions have been made. Null both where the query could not be run at all, or where the query was negative.</td> </tr> <tr> <td>big5</td> <td>Journal publication published in a journal owned by one of the following five publishing houses: Elsevier, Sage, Springer Nature, Taylor-Francis, Wiley.</td> </tr> <tr> <td>LMIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a lower-middle income country as defined by the World Bank</td> </tr> <tr> <td>LIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a low income country as defined by the World Bank</td> </tr> <tr> <td>SouthNorth</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a upper-middle income country, a lower-middle income country, or a low income country as defined by the World Bank; as well as at least one researcher affiliated with at least one institution located in a high income country. For the purpose of this indicator, Sicnece-Metrix exceptionally includes China and Bulgaria in the list of high income countries.</td> </tr> <tr> <td>DID_allauthors_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_allauthors_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>Preprint_authorcontrol</td> <td>Preprint is included in the the analytical breakdowns where a filter has been applied to control for author-level biases. Note that authors have been kept constant in preprints on the basis of their belonging to all analytical breakdowns in journal publications rather than in preprint-based groups.</td> </tr> </tbody> </table> <p> </p>
Is Open Data Strategy for Covid-19 used for other global health threats? A systematic review of the literature
<p>Dataset result of the literature review run between march and may 2022 on PubMed, Cinahl, Scopus and Google Scholar about open data and growing trend in scientific research about infection risk comparing Covid-19, Anti Microbial Resistance (AMR), and Health Assistance Correlated Infections (HAIs).</p>
Open data
<p>file1: AGV wheel engine speed</p> <p>file2: AGV wheel engine acceleration</p> <p>file 3: acceleration and temperature registered by mean of sensors, each one installed on a different container, during the shipment of components</p>
wGrapeUNIPD-DL: an open dataset for white grape bunch detection
<p>National and international Vitis variety catalogues can be used as image datasets for computer vision in viticulture. These databases archive ampelographic features and phenology of several grape varieties and plant structures images (e.g. leaf, bunch, shoots). Although these archives represent a potential database for computer vision in viticulture, plant structure images are acquired singularly and mostly not directly in the vineyard. Localization computer vision models would take advantage of multiple objects in the same image, allowing more efficient training. The present images and labels dataset was designed to overcome such limitations and provide suitable images for multiple cluster identification in white grape varieties. A group of 373 images were acquired from later view in vertical shoot position vineyards in six different Italian locations at different phenological stages. Images were then labelled in YOLO labelling format. The dataset was made available both in terms of images and labels. The real number of bunches counted in the field, and the number of bunches visible in the image (not covered by other vine structures) was recorded for a group of images in this dataset.</p>
Supplementary Information for "G-type Halohydrin Dehalogenases Catalyze Ring Opening Reactions of Cyclic Epoxides with Diverse Anionic Nucleophiles"
<p>This is the external Supplementary Information for our publication "G-type Halohydrin Dehalogenases Catalyze Ring Opening Reactions of Cyclic Epoxides with Diverse Anionic Nucleophiles".</p> <p>The .zip files contain the raw NMR data for all compounds as well as the protein structural data described in the manuscript.</p>
Angle measurements with 3D Vector antenna for localization purposes – open-access datasets
<p>This dataset contains data on positioning measurements of the angle of arrival (AoA) as well as the azimuth angle estimation using the MUSIC Algorithm. The data was collected from four ports (p1,p2,p3,p4) of a 3D Vector Antenna (3D VA) provided by ENAC. Data were captured in a laboratory environment with conditions that affect the positioning performance. </p>
Integrated Statistical Indicators from Scottish Linked Open Government Data
<p>Integrated statistical indicators that were retrieved from the official Scottish data portal in order to facilitate the exploitation of Machine Learning methods in Open Government Data. Data include 60 statistical indicators from seven categories such as health and social care, housing, and crime and justice. The indicators refer to the 6,976 “2011 data zones” of Scotland, while the year of reference is 2015. Data are ready to be used by the research community, students, policy makers, and journalists and give rise to plenty of social, business, and research scenarios that can be solved using Machine Learning technologies and methods.</p>
The Open Khipu Repository
<p><strong>Release v2.0.0 changes:</strong></p> <p>Implements the khipu renaming procedure described in Brezine, Clindaniel, Ghezzi, Hyland and Medrano (Under Review) "A New Naming Convention for Andean Khipus" (see replication code here: https://doi.org/10.5281/zenodo.6908292).</p> <p>Note that as a part of this release, we dropped several duplicate khipu recordings and implemented a more neutral "KH"-based naming scheme in a new <code>okr_num</code> field in the <code>khipu_main</code> table (see replication code linked above for more detail).</p> <p><strong>Description of Repository</strong></p> <p>This open-source digital repository stores the most up-to-date data and metadata on extant Inka-style khipus from archaeological sites in the Andes, as well as museums around the world. Inka khipus were unique pre-Columbian, Andean recording devices that used three-dimensional signs -- primarily knots, cords, and colors -- as symbols functionally akin to those of early writing systems in other cultures. Spanish chronicles, as well as contemporary khipu studies indicate that the Inka used khipus to record everything from accounting records to historical narratives. The khipu recording system remains undeciphered, however. The purpose of this repository is to enable computational khipu research and Inka khipu decipherment efforts.</p> <p>Currently, data is stored in a serverless SQLite relational database (khipu.db) that contains all known khipu data recorded and published by Inka khipu scholars. A list of known contributors is available <a href="https://github.com/khipulab/open-khipu-repository/blob/master/contributors">here</a> in the repository. If you have recorded khipu data in the database, but are not listed, please contact us (<a href="mailto:okr-team@googlegroups.com">okr-team@googlegroups.com</a>).</p> <p>The data in this repository will continue to be updated as more is learned about khipus and additional khipus become available for study. All new releases and changes to the repository are overseen by the Open Khipu Repository Advisory Board (<a href="mailto:okr-team@googlegroups.com">okr-team@googlegroups.com</a>), currently consisting of Carrie Brezine, Mackinley FitzPatrick, Iván Ghezzi, Sabine Hyland, Manuel Medrano, and Jeffrey Splitstoser. The repository is administered by the Open Khipu Research Laboratory, under the direction of Mackinley FitzPatrick (<a href="mailto:mackfitzpatrick@fas.harvard.edu">mackfitzpatrick@fas.harvard.edu</a>).</p> <p>A part of the OKR Advisory Board's mission is to positively influence the field of khipu studies in ways that are inclusive and respectful of all interested scholars. We recognize that the data in the OKR were not collected in a vacuum. The existence of the OKR is a result of power structures within the global discipline of Anthropology and of academia more generally. The data compiled here reflect funding disparities between scholars at different levels and between institutions in different countries. They reflect geographic privilege and economic privilege, for instance in scholars' disparate access to travel. The OKR is itself one artifact of colonialist Archaeology and Anthropology as practiced by North American and European scholars. It is also a product of unfortunate linguistic distancing -- we are well aware that native speakers of Andean languages have so far had little involvement in collecting the data in the OKR. We hope to change this as the field of khipu studies continues to evolve.</p> <p>In the wake of recent allegations of widespread sexual harassment within the field of khipu studies, Andean studies, Anthropology, and academia generally, the OKR board wishes to make it clear that we do not tolerate sexual harassment by members of the board or by those with whom we collaborate. We deplore the role that sexual exploitation and abuse have played in the compilation of this database in the past. We unequivocally condemn all forms of gender-based harassment and abuse. We do not tolerate discrimination based on sexual identity, gender, nationality, or ethnic identity. The OKR recognizes the patriarchal colonialist legacy of Anthropology and related disciplines, and we will actively work against the perpetuation of colonialist norms in Andean Studies.</p> <p>Our goal is to make the field of khipu studies open, inclusive, and safe for all scholars. To achieve that:</p> <ul> <li>We will not collaborate with scholars against whom there are credible allegations of sexual harassment.</li> <li>We will not collaborate with scholars against whom there are credible allegations of bullying or other identity-based discrimination or harassment.</li> <li>We recognize that khipu are not solely Peruvian. We will use language that does not exclude other Andean countries that consider khipu a part of their material heritage.</li> <li>We will include Andean scholars on the board whenever possible.</li> <li>We are in the process of implementing a more neutral khipu identification system that does not privilege the initials of scholars in each khipu name.</li> <li>We will actively work towards making the OKR available to all scholars with interest, recognizing that true accessibility will take time.</li> <li>We will commit to making OKR documentation publicly accessible and available in multiple languages, beginning with English and Spanish.</li> </ul>
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.