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1,287 results for “innovation”
Recognising innovative companies by using a diversified stacked generalisation method for website classification – the raw results
<p><strong>Introduction</strong></p> <p>The classification models were trained out by using the Classification and Regression Training package (caret) [1]. The models' parameters were fine-tuned by the 10-fold cross-validation procedure [2].</p> <p><strong>Cluster parameters</strong></p> <p>Most computations were carried out on a cluster having the following parameters:</p> <ul> <li>GPU: NVIDIA Tesla P100;</li> <li>CPU: 2.0 GHz Intel® Xeon® Platinum 8167M;</li> <li>The number of GPUs: 2;</li> <li>The number of CPU cores: 28;</li> <li>The number of CPU threads: 56;</li> <li>RAM: 192 GB;</li> <li>Storage: 3 TB.</li> </ul> <p>Only one model (k-nn) was calculated on a cluster having the following parameters:</p> <ul> <li>Processor: Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz 3.40 GHz;</li> <li>RAM: 16 GB;</li> <li>Windows 64 bit.</li> </ul> <p><strong>Performance statistics</strong></p> <p>All performance statistics are stored in cvs files. Each file corresponds to a particular machine learning method such as a file, "methodName-stat.csv" contains all data regarding a method, "methodName." All files cover the following columns:</p> <ul> <li><em>dataSetName – </em>a name of a data set on which evaluation was carried out; there are three possible values: (i) <em>firstPages</em> refers to the first data set (<em>L<sub>D</sub></em>) that contains textual description of a company; (ii) <em>firstPageLabels</em> refers to the second data set (<em>L<sub>L</sub></em>) that involves link labels that were extracted from an index page; (iii) <em>aggregateDocument</em> refers to the third data set (<em>L<sub>B</sub></em>) that consists of a so-called big document;</li> <li><em>fmeasure</em> - the number of features that were taken into account during evaluation;</li> <li><em>method</em> - the name of function in the caret package;</li> <li><em>parameters</em> - the values of parameters received from a tuning phase of a given classification method;</li> <li><em>precision </em>– the value of method’s precision;</li> <li><em>recall </em>– the value of method’s recall;</li> <li><em>fmeasure</em> - the value of method’s F-measure; </li> <li><em>error</em> - the value of method’s error;</li> <li><em>acc </em>– the value of method’s.</li> </ul> <p><strong>Time processing statistics</strong></p> <p>All time processing statistics, like the performance statistics, are stored in cvs files. Each file corresponds to a particular machine learning method such as a file, "methodName-time.csv". All files cover the following columns:</p> <ul> <li><em>dataSetName – </em>a name of a data set on which evaluation was carried out; there are three possible values: (i) <em>firstPages</em> refers to the first data set (<em>L<sub>D</sub></em>) that contains textual description of a company; (ii) <em>firstPageLabels</em> refers to the second data set (<em>L<sub>L</sub></em>) that involves link labels that were extracted from an index page; (iii) <em>aggregateDocument</em> refers to the third data set (<em>L<sub>B</sub></em>) that consists of a so-called big document;</li> <li><em>featureNo</em> - the number of features that were taken into account during evaluation;</li> <li><em>method</em> - the name of function in the caret package;</li> <li><em>user</em> - user time elapsed for executing a <em>method</em> as an R process;</li> <li><em>system</em> - system time elapsed for executing a <em>method</em> as an R process;</li> <li><em>elapsed</em> - total time elapsed for executing a <em>method</em> as an R process.</li> </ul> <p>For more information about user, system and total elapsed time, please see documentation [3].</p> <p><strong>References</strong></p> <p>[1] https://cran.r-project.org/web/packages/caret/</p> <p>[2] https://topepo.github.io/caret/model-training-and-tuning.html</p> <p>[3] https://stat.ethz.ch/R-manual/R-devel/library/base/html/proc.time.htm</p>
Shakespeare: Julius Caesar 1.2.30-187, sentences with Maximum Similarty Score. Appendix Table for "Innovation and Repetiton in Dramatic Texts"
<p>This is an illustrative table for the study "Innovation and Repetition in Dramatic Texts", published in the journal JCLS by Botond Szemes and Mihály Nagy. The table contains a scene from <em>Julius Caesar</em> 1.2.30-187' , assigning to each utterance the most similar sentence and the degree of similarity based on an S-BERT model.</p>
Interviews for New Business Models for Pharmaceutical Innovation and Access to Medicines - Case Study of the Oral Cholera Vaccine Development
<p>These supplementary materials represent the partial dataset in the form of semi-structured interviews, collected and analyzed in the research article "The 30-year evolution of oral cholera vaccines: A case study of a collaborative network alternative innovation model". This article is one of the outcomes of the "New Business Models for Pharmaceutical Innovation and Global Access to Medicines" research project, conducted at the Global Health Center, within the Geneva Graduate Institute. The dataset contains 8/16 interviews collected and used in this article, which are published with the informed consent of the interviewees.</p>
BIOBUILD Innovative Concept
<p>BIOBUILD project infographic demonstrating the techological processes and methods. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or REA. Neither the European Union nor the granting authority can be held responsible for them.<br>Grant agreement ID: 101135629</p>
Map of the archaeological sites mentionned in the paper "Abstraction in Archaeological Stratigraphy: a Pyrenean Lineage of Innovation (late 19th–early 21th century)"
<p>Projection: WGS 84. QGIS 3.14.16</p> <p>Sources:</p> <ul> <li>DEM: GEBCO (<a href="https://doi.org/10.5285/A29C5465-B138-234D-E053-6C86ABC040B9">https://doi.org/10.5285/A29C5465-B138-234D-E053-6C86ABC040B9</a>)</li> <li>Borders:<em> Límites municipales, provinciales y autonómicosRecintos municipales y líneas límite (municipales, provinciales y autonómicos)</em>. BDLJE CC-BY 4.0.</li> </ul>
Dataset for review on innovative contracts for the promotion of biodiversity and ecosystem services in agricultural management: contract design and governance characteristics
<p>Dataset for H2020 project Contracts2.0 (GA No 818190), WP2, Task 2.1, Deliverable 2.1.</p> <p>This dataset contains the information needed to reproduce the results in:<br> Bredemeier, Birte; Herrmann, Sylvia; Sattler, Claudia; Prager, Katrin; van Bussel, Lenny, Rex, Julia (submitted): Can the greater integration of biodiversity and ecosystem services into agricultural management be achieved through innovative contract design? Submitted to Ecosystem Services.<br> <br> The Contracts2.0 project aims to develop novel contract-based approaches to incentivise farmers for the increased provision of environmental public goods alongside private goods.<br> Based on a literature review and integration of expert knowledge, we identified a comprehensive set of approaches to innovative contracts deviating from mainstream AECM contracts, and provide an overview of the variety of contracts currently being tested and experimented with. This includes different contract types like innovative Payments for Ecosystem Services (PES) approaches, value chain approaches and land tenure contracts, as well as their hybrids.</p> <p><br> We analysed 62 cases providing insights into characteristics of contract design and contract governance and the wider policy framework.</p> <p><br> The present dataset contains information on the criteria and specifications used to describe the cases studied, as well as the evaluation of each case.</p> <p><br> This information has been compiled to the best of our knowledge based on the sources available.<br> <br> For further information on the project Contracts2.0, please visit our website www.project-contracts20.eu. </p>
WoS and Scopus records for the bibliometric analysis in the output D2.2 Digital transformation of research and innovation roadmap of the reSEArch-EU project
<p>These files represent the exported WoS and Scopus records, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.</p>
An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona
<p><em><strong>The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets.</strong></em></p> <p><strong>How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a></strong></p> <p>This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions.</p> <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> <p>note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index</p> <p>We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is :</p> <p>VV_new_perNN_LSTWW</p> <p>where:</p> <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> <p>Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65. </p> <p> </p>
Animation video on responsible research and innovation (RRI)
<p>This is a short animation video about responsible research and innovation. Watch it here: https://youtu.be/fOG5U2QweBo</p> <p>In research guided by responsible research and innovation (RRI), members of society work with scientists to align both the entire research process and its results with societal needs. The researcher and the research institute or research funder turn openly to citizens and involve the relevant societal actors in formulating research questions and collecting and analysing data. In RRI-guided research, scientists never forget to ask themselves: will our research do good to society and the Earth? What will its long-term social, ethical, and ecological consequences be? If a researcher or research-performing organisation integrates responsibility into their work, they will feel more useful and impactful in their profession, all in order to improve the lives of their fellow human and non-human beings. For this reason, it is important for researchers to reflect on their own organisational operations and change their daily routines. This is exactly what the Co-Change Project started to experiment with. In order to raise awareness of RRI, the project initiated ongoing deliberation within and beyond its own organisations through discussing ethics, gender, and open-science-related topics that engage all units that deal with research and innovation, as well as involving external experts and stakeholders in co-creating change.</p> <p> </p>
Integration of RISE innovations in the fields of OELF, RLA and SHM: input and output datasets (Version 1.0)
<p>Repository of input and output data files associated with Deliverable 6.1 of the <a href="http://rise-eu.org/home/">RISE project</a>.</p> <p>Full deliverable available <a href="http://static.seismo.ethz.ch/rise/deliverables/Deliverable_6.1.pdf">here</a>.</p>
ETHNA Validation Survey on Drivers and Barriers of Responsible Research & Innovation (RRI)
<p>This entry includes the ETHNA Validation Survey on Drivers and Barriers of Responsible Research & Innovation (RRI) questions in PDF and the servey results in Excel. Ths survey was conducted by Centre for Social Innovation (ZSI) as part of the WP4 of the European ETHNA System project. The survey explores potential good RRI practices and possible measures to gauge the RRI institutionalisation progress at research-performing organisations.</p> <p>The online survey was sent in October 2021 to a broad group (10.000+) of potentially relevant expert stakeholders identified through a Web of Science database search with the aim of assessing the relevance of the identified drivers, barriers and good practices of RRI institutionalisation. Altogether 888 responses were received from 69 countries with a balanced gender representation, involving the opinion of mostly senior experts (55% having more than 15 years of experience). After filling out general demographic and organisational information, the respondents rated the perceived relevance of the RRI incentives, barriers and good practices that were the highest ranked at the end of the second consultation phase (using a Likert scale of 1-10). </p>
Sistematic Mapping review of literature about public policies innovation and reading competences
<p>The systematic mapping method makes possible to identify what is known about a topic, what has been researched, what aspects remain unknown, and to obtain information about current trends and future challenges regarding this topic. In this Excel , the following articles were included: studies on innovative public policies related to reading skills, those published only in journals that can be found in two databases: Web of Science (WOS) and Scopus, published from January 2015 to December 2019, and related to the educational sector (social sciences, psychology, multidisciplinary, educational research, medical education, environmental education).</p>
Sprint for your eLife! How the eLife Innovation Sprint Helps Drive Forward Open Science Projects
<p><strong>Episode Summary</strong></p> <p>In this episode we cover the eLife Innovation Sprint 2020, that was held online on September 2nd and 3rd, 2020. The sprint facilitates collaboration between people who are working on tools, services, and other projects that enhance open science and research.</p> <p>We talk to the organiser Dr Emmy Tsang, who at the time was the Innovation Community Manager for eLife but is now the Community Engagement Manager for TU Delft. As well as participants from two of the projects that took part: Dr Cássio Amorim, creator of SciGen.Report, 'a platform to easily share and view any information that researchers may have on the reproducibility of papers'. And Esha Datta and Daniel Nüst, who who both joined the sprint to work on the Expanding Open Grants project, developed by Dr Hao Ye, which increases accessibility to examples of grants and proposals. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://sprint.elifesciences.org/">eLife Sprint</a></p> <ul> <li>Emmy Tsang: @emmy_ft </li> <li>@eLifeInnovation</li> </ul> <p><a href="https://scigen.report/">SciGen.Report</a></p> <ul> <li>@ScigenReport </li> </ul> <p><a href="https://www.ogrants.org/">Expanding Open Grants</a></p> <ul> <li>@nordholmen</li> <li>The new GH org for the sprint with latest developments:<br> <a href="https://github.com/expanding-open-grants/">https://github.com/expanding-open-grants/</a></li> <li>The wrap up slides are at:<br> <a href="https://docs.google.com/presentation/d/1PlW7Iq5xOKu1kbo_CLvnYXBJ3ldPdd4ulggZJl4F2-Y/edit#slide=id.g96e132450a_0_112">https://docs.google.com/presentation/d/1PlW7Iq5xOKu1kbo_CLvnYXBJ3ldPdd4ulggZJl4F2-Y/edit#slide=id.g96e132450a_0_112</a> </li> </ul>
Unblocking Breakthroughs: How DEIP Are Using Blockchain and Open Science to Provide Innovation Evaluation
<p><strong>Episode Summary:</strong></p> <p>In this episode we talk to Alex Shkor about his company DEIP which is a peer review platform and blockchain protocol for the evaluation of intellectual capital, research, and innovation. We discussed why Alex created this platform, what the potential for digital research and big data is, and how Open Science can accelerate research. </p> <p><strong>Episode Links: </strong></p> <p><a href="https://deip.world/">DEIP</a></p>
Doctoral Studies as part of an Innovative Training Network (ITN): Early Stage Researcher (ESR) experiences - supplemental material & data
<p>Table and data repository for the manuscript "Doctoral Studies as part of an Innovative Training Network (ITN): Early Stage Researcher (ESR) experiences"</p> <p><strong>Supplemental Tables:</strong></p> <ul> <li>table1_ESIT Project Table</li> <li>table2_TIN-ACT Project Table</li> <li>table3_ITN_tinnitus</li> <li>table4_ITN_other</li> <li>table5_Individual PhDs</li> </ul> <p><strong>Individual-level and de-identified survey data (raw data):</strong></p> <ul> <li>raw_data_ITN_tinnitus (survey results from PhDs as part of an ITN with a focus on tinnitus)</li> <li>raw_data_ITN_other (survey results from PhDs associated to ITNs with another focus)</li> <li>raw_data_Individual_Phds (survey results from PhDs not part of an ITN)</li> </ul>
Innovations in scholarly communication - data of the global 2015-2016 survey
<p>Innovations in scholarly communication - data of the global 2015-2016 survey.</p> <p>This data set contains:</p> <ul> <li>Full raw (anonymized) and cleaned data files of the 2015-2016 global Survey on Innovations in Scholarly Communication. Data are in xls format (raw and cleaned) and csv format (only for cleaned data as the raw data contain non-Roman script).</li> <li>Survey questionnaires for 7 languages (zipped PDFs)</li> <li>Variable list (xls)</li> <li>Readme file (txt)</li> </ul> <p>The data files contain >3,000,000 cells, and thus cannot be opened in their entirety in Google Drive.</p> <p>Many new websites and online tools have come into existence to support<br /> scholarly communication in all phases of the research workflow. To what extent<br /> researchers are using these and more traditional tools has been largely<br /> unknown. This 2015-2016 survey aimed to fill that gap. Its results may help<br /> decision making by stakeholders supporting researchers and may also help<br /> researchers wishing to reflect on their own online workflows. In addition,<br /> information on tools usage can inform studies of changing research workflows.<br /> The online survey employed an open, non-probability sample. A largely<br /> self-selected group of 20663 researchers, librarians, editors, publishers and<br /> other groups involved in research took the survey, which was available in seven<br /> languages. The survey was open from May 10, 2015 to February 10, 2016. It<br /> captured information on tool usage for 17 research activities, stance towards<br /> open access and open science, and expectations of the most important<br /> development in scholarly communication. Respondents’ demographics<br /> included research roles, country of affiliation, research discipline and year of<br /> first publication.</p> <p>A full description of data collection, survey response and methodology is in a data publication in F1000 Research:</p> <p>Kramer, Bianca & Jeroen Bosman (2016) Innovations in scholarly communication - global survey on research tool usage. F1000 Research. DOI:10.12688/f1000research.8414.1</p> <p>Contact:</p> <p>Jeroen Bosman: http://orcid.org/0000-0001-5796-2727 / j.bosman@uu.nl</p> <p>Bianca Kramer: http://orcid.org/0000-0002-5965-6560 / b.m.r.kramer@uu.nl</p>
TWIN2PIPSA - Twinning for strategic networking and impactful research and innovation in Biomedicine and Biotechnology
<p>Presentation by Prof. Cláudio M. Gomes at the 5<sup>th</sup> edition of Ciências Research & Innovation Day.</p>
Conflict reducing innovations in development enable increased multicellular complexity
<p>Obligately multicellular organisms, where cells can only reproduce as part of the group, have evolved multiple times across the tree of life. Obligate multicellularity has only evolved when clonal groups form by cell division, rather than by cells aggregating, as clonality prevents internal conflict. Yet obligately multicellular organisms still vary greatly in 'multicellular complexity' (the number of cells and cell types): some comprise few cells and cell-types, and others billions of cells and thousands of types. Here, we test if variation in multicellular complexity is explained by conflict-suppressing mechanisms, namely a single cell bottleneck at the start of development, and a strict separation of germline and somatic cells. Applying a phylogenetic comparative analysis to the life-cycles 138 lineages of plants, animals, fungi and algae, we show that an early segregation of the germline stem-cell lineage is key to the evolution of more cell types. In contrast, the presence of a strict single cell bottleneck was not related to either the number of cells or cell types but was associated with early germline segregation. Our results suggest that segregating the germline earlier in development enabled greater evolutionary innovation, possibly through conflict suppression or via greater developmental flexibility. </p>
Image repository for "Towards advancing Translators' Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 context"
<p>The files on this trusted repository are provided by the authors of the manuscript with the title “Towards advancing Translators’ Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 context” that was received by the MDPI journal Sustainability (ISSN 2071-1050) on 29 January 2024, got the manuscript ID sustainability-2872279, and is intended to become part of the special issue “Sustainable Materials, Manufacturing and Design” accessible under the link <a href="https://www.mdpi.com/journal/sustainability/special_issues/Sus_materials_manufacturing_design">https://www.mdpi.com/journal/sustainability/special_issues/Sus_materials_manufacturing_design</a>.</p> <p>The authors Paul-Ludwig Michael Noeske, Alexandra Simperler, Welchy Leite Cavalcanti, Vinicius Carrillo Beber, Brendon Weager, Tasmin Alliott, Peter Schiffels, and Gerhard Goldbeck aim at facilitating common access to the files representing high-resolution microscopy images (corresponding to the light microscopy (LM) and scanning electron microscopy (SEM) images shown in Figure 9 and Figure 12 in the manuscript or complementing them) given in .jpg and .tif format, respectively. Moreover, this repository comprises a .csv file containing the data points underlying the values presented in Table A1 of this manuscript and their description. The authors indicate here that following the sixth step of the translation process in materials modelling the translator may provide these data in this presentation that is adapted to the process-centric perspective required by representatives of an enterprise manufacturing prepregs and to their background knowledge disclosed to the translator beforehand.“</p>
Training sessions for innovation procurers: methods lessons learned, best practices
<p>These webinars explore practical methods and key lessons learned from implementing innovation procurement, featuring insights from the BUILD project. Using real-world case studies from municipalities across Europe, they highlight effective strategies for overcoming procurement challenges and fostering collaboration between public buyers and the market. The sessions are ideal for public procurement professionals, policymakers, and stakeholders seeking actionable guidance on leveraging procurement as a tool for innovation and sustainability.<br><br>They are available here:<br><br><a href="https://www.youtube.com/watch?v=v-9blcYpK_g">Public procurers’ exchange of best practices - Innovation Procurement Task Force webinar insights</a></p> <p><a href="https://www.youtube.com/watch?v=EY759zLpmdU">Innovation Procurement Webinar: Methods and lessons learned by real cases - the BUILD Project</a></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.