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15 results for “ambition”
Results from the OnStove Nepal model "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes all result datasets and figures from the <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">OnStove Nepal</a> model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All model input data can be downloaded from the permanent repository at<em> </em><a href="https://doi.org/10.5281/zenodo.10641858">10.5281/zenodo.10641858</a>.</p> <h2>Folder structure</h2> <p>The folder structure consists of a <strong>Procedded GIS Data </strong>folder containing all GIS processed data. These are the outputs from the <strong>DataProcessor.ipynb </strong>script and the raw GIS input data files found in the input data repository.</p> <p>A folder for <strong>each scenario</strong> results. Within each scenario folder, there are:</p> <ul> <li>A <strong>model.pkl </strong>and a <strong>results.pkl </strong>files. These are a calibrated OnStove model with the scenario inputs and a complete results model file of the scenario respectively. Both of these files can be read and explored using the OnStove tool. </li> <li>A <strong>summary.csv </strong>file with the summary results of the scenario for each technology.</li> <li>A <strong>Subsidies_scenario_name.csv </strong>file showing the required total subsidies per technology of the scenario.</li> <li>Image files in pdf format for: <ul> <li>The baseline technologies used in the country (<strong>current_shares.pdf</strong>),</li> <li>The spatial mix of technologies providing the maximum net-benefits throughout the country (<strong>max_benefit_tech.pdf</strong>), </li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The bar plot of max benefit technology shares (<strong>tech_split.pdf</strong>),</li> <li>The max benefit technologies distribution over relative wealth in the country (<strong>tech_histogram.pdf</strong>),</li> </ul> </li> <li>A <strong>Rasters </strong>folder with raster files of different result maps in .tif format.</li> </ul> <p>Inside the <strong>MCA </strong>folder, all results from the prioritization analysis are found, including:</p> <ul> <li>The prioritized spatial technology mix to achieve the goals of the country (<strong>Prioritized_hh.pdf</strong>),</li> <li>The biogas cookstoves relative wealth distribution index (<strong>Biogas_index.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves relative wealth distribution index (<strong>Biomass_ICS_T3_index.pdf</strong>),</li> <li>The electrical cookstoves relative wealth distribution index (<strong>Electricity_index.pdf</strong>),</li> <li>The biogas cookstoves priority map (<strong>Biogas_priority_areas.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves priority map (<strong>Biomass_ICS_T3_priority_areas.pdf</strong>),</li> <li>The electrical cookstoves priority map (<strong>Electricity_priority_areas.pdf</strong>),</li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The prioritized technology shares distribution over relative wealth in the country (<strong>tech_histogram_prioritized.pdf</strong>),</li> <li>A <strong>Subsidies_prioritized.csv </strong>file showing the required total subsidies per technology,</li> <li>A <strong>mca.pkl </strong>file with the MCA model that can be manipulated using the OnStove tool,</li> <li>A <strong>access_results.txt </strong>file with the current and after prioritization clean cooking access shares in the country.</li> </ul> <p>A <strong>main_plot.pdf </strong>and a <strong>prioritized_plot.pdf </strong>files showing the compiled results for all scenarios and prioritized scenario respectively.</p> <h2>License</h2> <p>All datasets are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p>
Residual emissions in long-term national climate strategies show limited climate ambition - Supplementary Data
<p>This supplementary data file contains the strategy data required to produce all figures in 'Residual emissions in long-term national climate strategies show limited climate ambition', in addition to tables presented in Supplemental Information.</p> <p>See 'Title' tab for contents. </p>
Benchmark comparison tests between Ambit-SMIRKS and RDKit chemoinformatics tools
<p>This archive contains benchmark code and results for Ambit-SMIRKS software package (<a href="http://ambit.sf.net">http://ambit.sf.net</a>) , described in the publication “Kochev N,, Avramova S., Jeliazkova N. Ambit-SMIRKS: a Software Module for Reaction Representation, Reaction Search and Structure Transformation”. </p> <p>We have performed benchmark testing of Ambit-SMIRKS and RDKit SMIRKS transformation algorithms. For this purpose we used a set of 545 compounds (see file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/smiles-set.txt?versionId=a49915af-23b7-41d2-9625-4a90cee172b4">smiles-set.txt</a>) including normal constituents of the body or common components of food, provided by Munro et al. [1] and a set of 84 reactions from RetroTransformDB [2] represented as SMIRKS linear notations (see file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/SMIRKS-RetroDB.txt?versionId=2fa6cb02-013a-4f69-b89a-c18d03b09222">SMIRKS-RetroDB.txt</a>). In both software tools (RDKit and Ambit-SMIRKS), each reaction was applied for all compounds at all possible sites thus performing more than 46000 SMIRKS transformations. For the purpose of comparison, Ambit-SMIRKS was applied in mode ALL with a single copy of the products for each reaction site. The java code for Ambit-SMIRKS test is available in file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/TestAmbitSmirks.java?versionId=fcb01365-b869-4801-9c5d-43cf8765103a">TestAmbitSmirks.java</a> and respectively python code for RDKit test is present in <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/rdkit-smirks-test-02.py?versionId=d42ab1c5-4c1f-4fad-8457-a6e8a8f4a3ec">rdkit-smirks-test-02.py</a>. In order to run the tests, Ambit dependency modules (version 3.2.0) are required (see more about Ambit at https://ambit.sf.net/) as well as RDKit (release 2018.03) installation is needed (see http://www.rdkit.org/).</p> <p>The tests were performed on a PC computer (Intel/Core i5-8250U, 1.6GHz/12 GB RAM), under Win10 Operating system. The calculations took about 30 seconds for RDKit software and about 40 seconds for Ambit-SMIRKS. The computational time for both software includes the SMIRKS parsing and reaction application as well as molecule preprocessing and file operations. Each algorithm was run 3 times. Detail timing info is present in file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/time-stat.txt?versionId=57e06cd1-d454-4bc0-a369-13b1e4d6150f">time-stat.txt</a>.</p> <p>The raw data outputs for both software tools respectively are stored in files: <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/rdkit-out.txt?versionId=2fa730f9-6ed8-4e51-a42c-c47799368f3b">rdkit-out.txt </a>and <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/ambit-out-no-eq-filter.txt?versionId=0519c609-06ae-4da8-b3c8-28a316d517d0">ambit-out-no-eq-filter.txt </a></p> <p>The generated output files are constructed from blocks for each SMIRKS in the following format:</p> <p>##smirks-number <SMIRKS></p> <p><smiles1> --> <number of reaction sites> <products1>, <products2>, …</p> <p><smiles2> --> <number of reaction sites> <products1>, <products2>, …</p> <p>…</p> <p><smiles545> --> <number of reaction sites> <products1>, <products2>, …</p> <p>On the base of generated raw test data, comparison statistics was summarized in file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/compare-ambit-rdkit.xlsx?versionId=5d4d6673-a9f0-47fd-8512-86112b3483f6">compare-ambit-rdkit.xlsx </a>containing following columns: <strong>SMILES</strong> – target molecule smiles, <strong>smirks_num</strong> – the index of reaction SMIRKS applied against the target, <strong>Ambit-NEF</strong> – number of reacted sites in the target molecule for Ambit algorithm, <strong>RDKit</strong> - number of reacted sites in the target molecule for RDKit algorithm , <strong>Diff</strong> – absolute difference the number reacted sites in Ambit and RDKit, <strong>FlagDiff</strong> – it is 1 (true) if the <strong>Diff</strong> is non zero, <strong>FlagRDKitReact</strong> – it is 1 (true) if at least one site is reacted in the target molecule by RDKit tool (i.e. RDKit column values > 0), <strong>FlagAmbitReact</strong> - it is 1 (true) if at least one site is reacted in the target molecule by Ambit-SMIRKS tool (i.e. Ambit-NEF column values > 0).</p> <p>Out of 46410 tests, 6096 test reactions were successfully applied for at least one site in Ambit-SMIRKS (i.e. the value in column Ambit-NEF is not zero) and 5729 reactions were successfully applied for at least one site in RDKit accordingly (i.e. the value in column RDKit is not zero). The obtained total number of reacted sites for Ambit-SMIRKS and RDKit is 41453 and 40782 respectively. We have performed statistics of the number of reacted sites for both software tools and differences were observed for 436 reactions. From our analysis we may infer that the observed differences are mainly due to different treatment of equivalent molecules sites and some small differences of the internal presentation of the molecules and the chemical reactions on both software packages. </p> <p>[1] Munro I., Ford RA, Kennepohl E, Sprenger J. Correlation of structural class with no-observed-effect-levels: a proposal for establishing a threshold of concern. Food Chem Toxicol. 1996;34:829–867.</p> <p>[2] https://doi.org/10.5281/zenodo.1209313</p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </p>
Can Digital Currencies End Financial Exclusion in Indonesia? Economic Realities and Policy Ambitions
<p>Digitalisation is driving Indonesia’s economic development. The empowerment<br> of an efficient system of digital payments is a key underlying factor for the<br> establishment of an inclusive and well-developed digital economy. As a first<br> and fundamental step, the Bank of Indonesia launched the “National Cashless<br> Movement” (GNNT) in 2014. This vision was further advanced with the “Indonesia<br> Payment System Blueprint 2025” (IPS), which aimed at boosting digitalisation in<br> the banking industry through open banking and technology developments. As a<br> result, Indonesia’s digital economy and finance is recording remarkable upward<br> trends – such as an increase of 36.9 per cent in e-commerce and 52.6 per cent in<br> fintech lending transactions between 2020 and 2021. Similarly, cashless payments<br> are experiencing a spectacular growth. The usage of the Quick Response Indonesia<br> Standard (QRIS) system, which enhances cashless payment, has doubled. Credit<br> card transactions and e-money usage increased by 20 per cent and 51.6 per cent,<br> respectively, in the same two-year period.</p> <p>In this context of vibrant, quick transformations, a diverse ecosystem – which<br> comprises incumbent financial institutions, start-ups and technology companies<br> – is trying to advance Indonesia’s digital payments while targeting the goal of<br> financial inclusion. The rise of digital payment solutions indeed provides a unique<br> window of opportunity to tackle the current 92 million unbanked Indonesians<br> and 62 million small and medium-sized enterprises (SMEs) that are excluded from<br> the formal economy in Indonesia.</p>
PyPSA-PL: High and medium ambition scenarios for RES deployment in Poland until 2030
<p>This record contains all the scripts and data from the PyPSA-PL modelling exercise that supported the report:</p> <ul> <li>Kubiczek P., Smoleń M. (2023). Polski nie stać na średnie ambicje. Oszczędności dzięki szybkiemu rozwojowi OZE do 2030 r. Instrat Policy Paper 03/2023. <a href="https://www.instrat.pl/pypsa-marzec-2023">https://www.instrat.pl/pypsa-marzec-2023</a></li> </ul> <p>The record structure is based on the PyPSA-PL repository <a href="https://github.com/instrat-pl/pypsa-pl">https://github.com/instrat-pl/pypsa-pl</a> (v2.0).</p> <p>Version 2: added data and figures that are directly presented in the report</p> <p>© Instrat Foundation 2023</p>
Data from: Raising the bar: recovery ambition for species at risk in canada and the US
<p>Routinely crossing international borders and/or persisting in populations across multiple countries, species are commonly subject to a patchwork of endangered species legislation. Canada and the United States share numerous endangered species; their respective acts, the Species at Risk Act (SARA) and the Endangered Species Act (ESA), require documents that outline requirements for species recovery. Although there are many priorities for improving endangered species legislation effectiveness, species recovery goals are a crucial component. We compared recovery goal quality, as measured by goal quantitativeness and ambition, for species listed under SARA and ESA. By comparing across ESA and SARA, the intent of the study was to identify differences and similarities that could support the development of stronger species' recovery goals under both legislations. Our results indicated that: (1) overall, only 38% of recovery goals were quantitative, 41% had high ambition, and 26% were both quantitative and high ambition; (2) recovery goals had higher quantitativeness and ambition under ESA than SARA; (3) recovery goals for endangered species had higher ambition than threatened species under ESA and SARA, and; (4) no recovery goal aimed to restore populations to historic levels. Combined, these findings provide guidance to strengthen recovery goals and improve subsequent conservation outcomes. In particular, species at risk planners should seek to attain higher recovery goal ambition, particularly for SARA-listed species, and include quantitative recovery goals wherever possible.</p>
The Koo Dataset: An Indian Microblogging Platform With Global Ambitions
<p>This is the dataset released with the <a href="https://arxiv.org/abs/2401.07599">paper</a> titled "The Koo Dataset: An Indian Microblogging Platform With Global Ambitions". </p> <p>The dataset contains 43 JSON files containing the posts made on the platform, 34 JSON files for the comments, the shares and the likes. It also contains a JSON file for the user profiles. The metadata included in each data type is described in the paper.</p> <p>If you use our dataset, please cite the arXiv version:</p> <p><code>@misc{mekacher2024koo,</code><br><code> title={The Koo Dataset: An Indian Microblogging Platform With Global Ambitions}, </code><br><code> author={Amin Mekacher and Max Falkenberg and Andrea Baronchelli},</code><br><code> year={2024},</code><br><code> eprint={2401.07599},</code><br><code> archivePrefix={arXiv},</code><br><code> primaryClass={cs.SI}</code><br><code>}</code></p>
Flemish Political Ambition Survey
<p>Dataset based on a survey about political ambition among a random sample of the youth population ( aged 18-35) in Flanders (Belgium), N = 1,000</p>
Agroforestry-based community forestry as a large-scale strategy to reforest agricultural encroachment areas in Myanmar: ambition vs. local reality
<p>Abstract: <br> Context: <br> The high rate of deforestation in Myanmar is mainly due to agricultural expansion. One task of the Forest Department is to increase tree cover in the encroaching farmland by establishing large-scale agroforestry-based community forests (ACFs).<br> Aim: <br> The objectives of this study were to analyze the adoption and performance of the ACFs in the agricultural encroachment areas in the Bago-Yoma region, Myanmar; and to provide recommendations to enhance the adoption of ACFs by farmers.<br> Methods: <br> We inventoried 42 sample plots and surveyed 291 farmers. Survey responses were analyzed by binary logistic regression, one-way ANOVA, and non-parametric correlation tests to evaluate factors influencing the adoption of ACFs. Stand characteristics were calculated from the inventory data to evaluate the performance of ACFs.<br> Results: <br> Our results show that farmer participation in ACFs was lower than stated in the registry of the Forest Department. Farmers practiced four different agroforestry designs in ACFs with different outcomes. The Forest Department strongly determined tree species and planting designs, farmers’ perception and participation in ACFs. Farmland size, unclear and insufficient information on ACFs, and a negative perception of raising trees in crop fields were the major factors limiting the adoption rates of ACFs. <br> Conclusion: <br> We recommend capacity building for farmers and Forest Department staff and raising awareness about the benefits of planting designs and trees on farmland. A stronger consideration of farmers’ preferences for design and species selection could increase their motivation to adopt ACFs and improve the long-term sustainability of ACFs.</p> <p> </p>
Data from: Raising the bar: recovery ambition for species at risk in canada and the US
Open the record for dataset details and reuse information.
Underlying dataset to the Report on the Open Consultation of the NPOS2030 Ambition Document
<p>This is the underlying dataset to the <a href="https://zenodo.org/record/6106850">Report on the Open Consultation</a> on the NPOS2030 Ambition Document.</p> <p>This dataset contains the original responses to the online Open Consultation that was open between November 22<sup>nd</sup> and December 22<sup>nd</sup> 2021, aiming to give all Netherlands stakeholders the opportunity to reflect on the NPOS2030 Ambition Document of the Netherlands National Programme Open Science (<a href="http://openscience.nl/">NPOS</a>).</p> <p>Out of a total of 78 respondents, 54 institutions, networks, initiatives and persons gave their consent to publish their response including their name. They are listed below. 11 Responses are included on a basis of anonymity: this was sometimes caused by having to ask too many people in a network to give their consent. 13 participants decided to not disclose their responses.</p> <p>NB: Some respondents did say that their responses to the questions about the level of support for each of the four sections of the Ambition Document paint a too negative picture: due to the nature of scoring on a scale of 1-5, they did not see a better way than to give a low score to bring across that they always see ways to improve.</p> <p>The institutions, networks, initiatives and persons gave their consent to publish their response:</p> <ol> <li>4TU.ResearchData</li> <li>Adviescollege Open Science van de hogescholen</li> <li>Amsterdam University of Applied Sciences</li> <li>Avans Hogeschool</li> <li>brief consultation of five senior researchers</li> <li>Centre for Science and Technology Studies (CWTS), Leiden University</li> <li>Dutch Research Council NWO</li> <li>Eindhoven University of Technology</li> <li>Erasmus University Rotterdam </li> <li>Hogeschool Leiden</li> <li>KB, National Library of the Netherlands</li> <li>Maastricht University Recognition & Rewards Programme Team</li> <li>Media voor Vak en Wetenschap (MVW)</li> <li>Medical libraries of the STZ Samenwerkende Topklinische ziekenhuizen</li> <li>Netherlands eScience Center</li> <li>Network of Dutch Open Science Communities: the OSC-NL Board and 60 individual researchers who signed this response</li> <li>NL-RSE (Netherlands Research Software Engineers network)</li> <li>NRO (Nationaal Regieorgaan Onderwijsonderzoek)</li> <li>NWO Institute Organisation</li> <li>ODISSEI (social sciences)</li> <li>Open Science programme of the University of Groningen</li> <li>Open Science Public Engagement Fellow Network, Utrecht University</li> <li>Pedagogische en Onderwijswetenschappen, Faculteit der Maatschappij en Gedragswetenschappen, UvA</li> <li>Promovendi Netwerk Nederland (PNN)</li> <li>PULSAQUA</li> <li>Radboud University</li> <li>Radboudumc Nijmegen</li> <li>Rathenau Instituut</li> <li>Regieorgaan SIA</li> <li>Samenwerkingsverband Universiteitsbibliotheken en Koninklijke Bibliotheek (UKB)</li> <li>SciComNL (Science Communication Association Netherlands)</li> <li>Springer Nature</li> <li>Stuurgroep DCC-PO</li> <li>SURF</li> <li>The Dutch Parkinson's Association</li> <li>The Open Science Community Amsterdam (OSCA). OSCA consists of members from the Amsterdam University of Applied Sciences (HvA), the University of Amsterdam (UvA), the Vrije Universiteit Amsterdam (VU) and the Student Initiative for Open Science (SIOS).</li> <li>Three open science specialists at Utrecht University Library: Bianca Kramer, Jeroen Sondervan and Jeroen Bosman</li> <li>Tilburg University Open Science Focus Group</li> <li>TU Delft</li> <li>Universiteit Leiden</li> <li>University of Amsterdam</li> <li>University of Twente</li> <li>Utrecht University Freudenthal Institute Science Communication and Public Engagement group</li> <li>Vrije Universiteit Amsterdam</li> <li>Zone "Towards digital (open) educational resources" from the Dutch Acceleration Plan</li> <li>Prof.dr. Karin Pfeffer</li> <li>Drs Marina Noordegraaf</li> <li>Mr. Niels van Tol</li> <li>Dr. Egon Willighagen</li> <li>Drs (Msc) Gaby Lutgens</li> <li>Jeroen Jansen</li> <li>Dr. Miaomiao Zhou</li> <li>Prof.dr. René Bekkers</li> <li>Dr.ir. Rolf Hut</li> </ol>
An uMbrella Study of BIomarker-driven Targeted Therapy In Patients With Platinum-resistant Recurrent OvariaN Cancer(AMBITION)
ClinicalTrials.gov study NCT03699449. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Insights from scenarios in line with #EUGreenDeal ambitions - data behind the graphs - JRC report
<p>This Excel file contains the data behind the graphs of the following JRC report:</p> <p>Tsiropoulos I., Nijs W., Tarvydas D., Ruiz Castello P., Towards net-zero emissions in the EU energy system by 2050 – Insights from scenarios in line with the 2030 and 2050 ambitions of the European Green Deal, EUR 29981 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-13096-3, doi:10.2760/081488, JRC118592.</p> <p>The report is downloadable from: https://ec.europa.eu/jrc/en/publication/towards-net-zero-emissions-eu-energy-system-2050 </p>
Alcohol Use Disorders- Mobile Based Brief Intervention Treatment (AMBIT): A Pilot RCT
ClinicalTrials.gov study NCT04078360. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
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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.