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7,195 results for “SET”

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zenodo32/100

Data set for the study "Perception Disparity between Women and Men on the Gender Gap in STEM at a Spanish University"

<p>Database for the study "Perception" carried out in the project "Engineering with a gender perspective" financed by the Women's Institute of the Ministry of Equality of the Spanish Government, call for proposals PAC 2021 INMUJERES (reference number 37/7ACT/21).<br>An adaptation of the GENCE 2.0 questionnaire (DOI: 10.5281/zenodo.2550690) was used to collect the data.&nbsp;</p>

opencc-by-4.0May 2024View details →
zenodo32/100

Developing Asparagaceae1726: an Asparagaceae-specific probe set targeting 1,726 loci for Hyb-Seq and phylogenomics in the family

<p>Dataset associated with the release of Asparagaceae1726: a Hyb-Seq probe set targeting 1726 conserved, low-copy nuclear genes specifically for phylogenomics in the angiosperm family Asparagaceae. Phylogenomic analysis of this number of genes could aid the often-challenging delineation of taxa and resolution of relationships within Asparagaceae.</p> <p>Asparagaceae1726 is a first attempt at establishing a standardized set of loci for phylogenomic analysis in Asparagaceae, which we hope will be widely used for extensible and reproducible investigations of diversification in the family.</p>

opencc-by-4.0Dec 2023View details →
zenodo32/100

Data set from longitudinal survey: ySKILLS' first wave

<p>This document provides the data set and information concerning the first wave of data collection of ySKILLS longitudinal survey items conducted within Task 4.2. in six countries: Estonia, Finland, Germany, Italy, Poland, and Portugal. The aim of Task 4.2. was to develop a robust longitudinal survey to measure both short and medium-term impact of information and communication technologies (ICT) on youth. The data is collected in three waves. The survey focuses on four dimensions of well-being (cognitive, physical, psychological, and social) and several areas related to ICT use. Specifically, the questionnaire is divided into&nbsp;sections. For a detailed overview of each section, see the data dictionary.&nbsp;</p> <p>Upon first request, users will be granted access to the dataset. As the project is still in progress, and the data collection of the follow-up waves is still ongoing the dataset is not yet open access. Once all survey waves are completed, the dataset is merged and cleaned,&nbsp;and the report on the three waves is published, the data of all waves will be open access.&nbsp;</p>

openJun 2022View details →
zenodo32/100

IMD New High Spatial Resolution (0.25X0.25 degree) Long Period (1901-2022) Daily Gridded Rainfall Data Set Over India: Matlab script for merging into single netcdf/mat file with datetime stamping

Open the record for dataset details and reuse information.

opencc-by-4.0May 2024View details →
zenodo32/100

Data set for: Pore-scale simulation of salt precipitation during CO2 injection into saline aquifers, Water Resources Research

<p>This dataset includes output data used for analyses presented in the paper titled: Pore-scale simulation of salt precipitation during CO2 injection into saline aquifers and submitted to Water Resources Research.</p>

opencc-by-4.0May 2024View details →
zenodo32/100

ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Corner Clamp Part 2 - Geared Caliper Base

Open the record for dataset details and reuse information.

opencc-by-4.0May 2024View details →
zenodo32/100

GIST: Generated Inputs Sets Transferability in Deep Learning (Part 2)

<p>Part2 of the Replication Package for the paper "GIST: Generated Inputs Sets Transferability in Deep Learning"</p> <p>Contains RoBERTa models and data for the KMNC property.</p> <p>Github link: https://github.com/FlowSs/GIST</p> <p>Part1 can be found here: https://zenodo.org/records/10028594</p> <p>Abstract:</p> <p>&nbsp; &nbsp;To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methods<br>for test case generation techniques are being developed.<br>&nbsp; &nbsp; When confronted with testing DNN models, the user can apply any existing test generation technique.<br>However, it needs to do so for each technique and each DNN model under test, which can be expensive.<br>Therefore, a paradigm shift could benefit this testing process: rather than regenerating the test set independently<br>for each DNN model under test, we could transfer from existing DNN models.<br>&nbsp; &nbsp; &nbsp;This paper introduces GIST (Generated Inputs Sets Transferability), a novel approach for the efficient<br>transfer of test sets. Given a property selected by a user (e.g., neurons covered, faults), GIST enables the<br>selection of good test sets from the point of view of this property among available test sets. This allows the<br>user to recover similar properties on the transferred test sets as he would have obtained by generating the<br>test set from scratch with a test cases generation technique. Experimental results show that GIST can select<br>effective test sets for the given property to transfer. Moreover, GIST scales better than reapplying test case<br>generation techniques from scratch on DNN models under test.</p>

opencc-by-4.0Dec 2023View details →
zenodo32/100

ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - NanoVise part 1

<p>@article{schieber2024asdf,<br>&nbsp; title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br>&nbsp; author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br>&nbsp; journal={arXiv preprint arXiv:2403.16400},<br>&nbsp; year={2024}<br>}</p>

opencc-by-4.0May 2024View details →
zenodo32/100

ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - NanoVise part 2

<p>@article{schieber2024asdf,<br>&nbsp; title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br>&nbsp; author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br>&nbsp; journal={arXiv preprint arXiv:2403.16400},<br>&nbsp; year={2024}<br>}</p>

opencc-by-4.0May 2024View details →
zenodo32/100

Data set for "Frequency of Prejudice Coverage in News Media Worldwide"

<p>Previous research has identified a post-2010 sharp increase of terms used to denounce prejudice (i.e. racism, sexism, homophobia, Islamophobia, anti-Semitism, etc.) in U.S. and U.K. news media content. Here, we extend previous analysis to an international sample of news media organizations. Thus, we quantify the prevalence of prejudice-denouncing terms and social justice associated terminology (diversity, inclusion, equality, etc.) in over 98 million news and opinion articles across 124 popular news media outlets from 36 countries representing 6 different world regions: English-speaking West, continental Europe, Latin America, sub-Saharan Africa, Persian Gulf region and Asia. We find that the post-2010 increasing prominence in news media of the studied terminology is not circumscribed to the U.S. and the U.K. but rather appears to be a mostly global phenomenon starting in the first half of the 2010s decade in pioneering countries yet largely prevalent around the globe post-2015. However, different world regions&rsquo; news media emphasize distinct types of prejudice with varying degrees of intensity. We find no evidence of U.S. news media having been first in the world in increasing the frequency of prejudice coverage in their content. The large degree of temporal synchronicity with which the studied set of terms increased in news media across a vast majority of countries raises important questions about the root causes driving this phenomenon.</p> <p>We provide here a reproducibility data set of counts of target terms and total number of unigrams in each article analyzed plus a Google searchable prefix of the headline of the article for verification of the integrity of the data set and the frequency counts.&nbsp;</p>

opencc-by-4.0Apr 2024View details →
dryad32/100

Beekeeping improves shea pollination and fruit set in West African Agroforestry parklands

<p>Shea (<em>Vitellaria paradoxa</em> C.F Gaertn) is a fruit tree of domestic and industrial importance in arid and semi-arid regions of Sub-Saharan Africa. Fruit set is largely dependent on insect pollination but recent studies have revealed a pollen deficit. Introduction of managed bees into orchards is an effective approach for enhanced pollination services in temperate climates. However, there is limited information to guide the adoption of this practice in shea agroforestry parklands.</p> <p>This study investigated the effect of managed honey bee colonies (<em>Apis mellifera</em>) on fruit yield in six shea parklands across three regions of Ghana.</p> <p>Tree proximity to the apiary had a detectable effect on fruit set within a 500 m range of the apiary.  Proximity of shea trees to apiary was significantly related to number of immature fruit set but not number of mature fruits. Fruit weight and size were not significantly related to distance from apiary nor pollination treatment.</p> <p>This implies that the introduction of beekeeping has the potential to address shea pollination deficit at least within a 500 m range of the apiary.  Further studies are needed to determine the optimal hive density per acreage of shea parkland to maximise pollination services.</p>

opencc-zeroMay 2024View details →
zenodo32/100

SNID template data set v1.0.0

<p>Original version of the SNID template data set distributed with snid-5.0.</p> <p>&nbsp;Reading template files...agn kcE kcS0 kcSB1 kcSB2 kcSB3 kcSB4 kcSB5 kcSB6 kcSa kcSb kcSc lbv01ac lbv03hy lbv99bw mstar sn00E sn00H sn00cx sn01el sn02ap sn02bo sn02cx sn02er sn02ic sn03cg sn03du sn03fg sn04S sn04aw sn04dj sn04eo sn04et sn05bf sn05cs sn05gj sn05hj sn05hk sn05kl sn06aj sn06bp sn06gz sn79C sn80K sn81B sn83N sn83V sn84L sn86G sn87A sn88L sn89B sn90B sn90I sn90K sn90N sn90O sn90U sn90aa sn91A sn91M sn91N sn91T sn91ar sn91bg sn92A sn92H sn92ar sn93J sn93ac sn94D sn94I sn94M sn94Q sn94S sn94T sn94ae sn95D sn95E sn95F sn95ac sn95al sn95bd sn96C sn96L sn96X sn96cb sn97br sn97cn sn97cy sn97dc sn97dd sn97dq sn97ef sn97ei sn98S sn98T sn98aq sn98bu sn98bw sn98dt sn99aa sn99ac sn99aw sn99by sn99di sn99dn sn99ee sn99em sn99ex sn99gi &nbsp;done<br>&nbsp;Loaded &nbsp; &nbsp; &nbsp; &nbsp; 1515 &nbsp;spectra out of &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;111 &nbsp;templates</p>

opencc-by-4.0May 2024View details →
zenodo32/100

Tyre defect data set

<p>Data descriptor information</p> <p>1.Data source</p> <p>This tyre defect X-ray dataset comes from the open dataset of Shanghai University Network Control Centre. The original link to the dataset is below: https://aistudio.baidu.com/datasetdetail/215731</p> <p>&nbsp;</p> <p>2. Data content and structure</p> <p>There are 1142 training set images, 382 validation set images and 381 test set images in this dataset. The resolution of each image is 480x480 pixels. The data is stored in JPG format.</p> <p>&nbsp;</p> <p>3. Data labelling and tagging</p> <p>Each image contains an annotation file in TXT format, which records the location and type of defects, including four types of defects: "open", "cord_defects", "impurity", and "belt_cuobian".</p> <p>&nbsp;</p> <p>4. Description of data use</p> <p>The dataset is mainly used for the study of tyre defect detection, especially for defects containing elongated features. Due to the characteristics of the dataset, it may not be suitable for other types of image detection tasks.</p>

opencc-by-4.0May 2024View details →
zenodo32/100

Data set for publication "Effect of milling atmosphere on stability and surface properties of ZnO/vermiculite hybrid nanocomposite powders"

Open the record for dataset details and reuse information.

opencc-by-4.0May 2024View details →
zenodo32/100

Evaluation set DCASE 2024 task 4 (for submissions)

<p>This repo contains the evaluation dataset to download and submit system outputs for when participating in task 4 of the DCASE 2024 Challenge.</p>

opencc-by-4.0May 2024View details →
zenodo32/100

NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)

<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

SNID template data set v2.0.0

<div>New version (2.0.0) of the SNID template data set. This should be used in place of the v1.0.0 data set with snid-5.0.</div> <div>&nbsp;</div> <div>&nbsp;Reading template files...agn kcE kcS0 kcSB1 kcSB2 kcSB3 kcSB4 kcSB5 kcSB6 kcSa kcSb kcSc lbv1999bw lbv2001ac lbv2003hy mstar sn1979C sn1980K sn1981B sn1983N sn1983V sn1984A sn1984L sn1986G sn1987A sn1988L sn1989B sn1990B sn1990I sn1990K sn1990N sn1990O sn1990U sn1990aa sn1991A sn1991M sn1991N sn1991T sn1991ar sn1991bg sn1992A sn1992H sn1992ar sn1993J sn1993ac sn1994D sn1994I sn1994M sn1994Q sn1994S sn1994T sn1994ae sn1995D sn1995E sn1995F sn1995ac sn1995ak sn1995al sn1995bd sn1996C sn1996L sn1996X sn1996Z sn1996ab sn1996ai sn1996bk sn1996bl sn1996bo sn1996bv sn1996cb sn1997E sn1997Y sn1997bp sn1997bq sn1997br sn1997cn sn1997cy sn1997dc sn1997dd sn1997do sn1997dq sn1997dt sn1997ef sn1997ei sn1998S sn1998T sn1998V sn1998ab sn1998aq sn1998bp sn1998bu sn1998bw sn1998co sn1998de sn1998dh sn1998dk sn1998dm sn1998dt sn1998dx sn1998ec sn1998ef sn1998eg sn1998es sn1999X sn1999aa sn1999ac sn1999aw sn1999bh sn1999by sn1999cc sn1999cl sn1999cp sn1999cw sn1999da sn1999di sn1999dn sn1999dq sn1999ee sn1999ef sn1999ej sn1999ek sn1999em sn1999ex sn1999gd sn1999gh sn1999gi sn1999gp sn2000B sn2000E sn2000H sn2000bh sn2000bk sn2000ce sn2000cf sn2000cn sn2000cp sn2000cu sn2000cw sn2000cx sn2000dg sn2000dk sn2000dm sn2000dn sn2000fa sn2001E sn2001G sn2001N sn2001V sn2001ah sn2001ay sn2001az sn2001bf sn2001bg sn2001br sn2001cj sn2001ck sn2001cp sn2001da sn2001eh sn2001el sn2001en sn2001ep sn2001ex sn2001fe sn2001fh sn2001gc sn2002G sn2002ap sn2002aw sn2002bf sn2002bo sn2002cd sn2002cf sn2002ck sn2002cr sn2002cs sn2002cu sn2002cx sn2002de sn2002dj sn2002dl sn2002do sn2002dp sn2002ef sn2002er sn2002es sn2002eu sn2002fb sn2002fk sn2002ha sn2002hd sn2002he sn2002hu sn2002hw sn2002ic sn2002jg sn2002jy sn2002kf sn2003U sn2003W sn2003Y sn2003bg sn2003cg sn2003ch sn2003cq sn2003du sn2003fa sn2003gn sn2003hu sn2003hv sn2003ic sn2003it sn2003iv sn2003kc sn2003kf sn2004L sn2004S sn2004as sn2004at sn2004aw sn2004bd sn2004bg sn2004bk sn2004dj sn2004dt sn2004ef sn2004eo sn2004et sn2004fu sn2004fz sn2004gc sn2004gs sn2005A sn2005M sn2005am sn2005bc sn2005be sn2005bf sn2005bl sn2005bo sn2005cc sn2005cf sn2005cg sn2005cs sn2005el sn2005eq sn2005eu sn2005gj sn2005hc sn2005hf sn2005hj sn2005hk sn2005iq sn2005kc sn2005ke sn2005ki sn2005kl sn2005ls sn2005lu sn2005mc sn2005mz sn2005na sn2006D sn2006H sn2006N sn2006S sn2006X sn2006ac sn2006aj sn2006ak sn2006al sn2006ax sn2006az sn2006bp sn2006bq sn2006br sn2006bt sn2006bw sn2006bz sn2006cc sn2006cf sn2006cj sn2006cm sn2006cp sn2006cq sn2006cz sn2006em sn2006eq sn2006et sn2006eu sn2006ev sn2006gj sn2006gr sn2006gt sn2006gz sn2006hb sn2006kf sn2006le sn2006lf sn2006mo sn2006nz sn2006oa sn2006ot sn2006sr sn2006te sn2007A sn2007F sn2007S sn2007Y sn2007ae sn2007af sn2007al sn2007ap sn2007au sn2007ax sn2007ba sn2007bc sn2007bd sn2007bj sn2007bm sn2007bz sn2007ca sn2007cg sn2007ci sn2007co sn2007cq sn2007fb sn2007fs sn2007hj sn2007if sn2007jg sn2007kk sn2007le sn2007nq sn2007qe sn2007sr sn2007ux sn2008A sn2008C sn2008D sn2008L sn2008Q sn2008R sn2008Z sn2008ae sn2008af sn2008ar sn2008bf snls03D3bb &nbsp;done<br>&nbsp;Loaded &nbsp; &nbsp; &nbsp; &nbsp; 3754 &nbsp;spectra out of &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;349 &nbsp;templates</div>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Data set: Delivery of Carbon Dioxide to an Electrode Surface Using a Nanopipette

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2024View details →
zenodo32/100

SNID template data set v2.0.0 extended to 2 microns (1024 and 2048 wavelength bins)

<p>This set of SNID templates extends the v2.0.0 set out to 2 microns (wavelength range 2500 Ang-2 microns). It should be used when the input spectrum has data extending beyond 1 micron, since the original templates-2.0 set only covers 2500-10000 Ang.</p> <p>Two versions of the data set are published, one with 1024 wavelength bins, the other with 2048 wavelength bins.</p> <p>&nbsp;Reading template files...sn1979C sn1980K sn1981B sn1983N sn1983V sn1984A sn1984L sn1986G sn1987A sn1988L sn1989B sn1990B sn1990I sn1990K sn1990N sn1990O sn1990U sn1990aa sn1991A sn1991M sn1991N sn1991T sn1991ar sn1991bg sn1992A sn1992H sn1992ar sn1993J sn1993ac sn1994D sn1994I sn1994M sn1994Q sn1994S sn1994T sn1994ae sn1995D sn1995E sn1995F sn1995ac sn1995ak sn1995al sn1995bd sn1996C sn1996L sn1996X sn1996Z sn1996ab sn1996ai sn1996bk sn1996bl sn1996bo sn1996bv sn1996cb sn1997E sn1997Y sn1997bp sn1997bq sn1997br sn1997cn sn1997cy sn1997dc sn1997dd sn1997do sn1997dq sn1997dt sn1997ef sn1997ei sn1998S sn1998T sn1998V sn1998ab sn1998aq sn1998bp sn1998bu sn1998bw sn1998co sn1998de sn1998dh sn1998dk sn1998dm sn1998dt sn1998dx sn1998ec sn1998ef sn1998eg sn1998es sn1999X sn1999aa sn1999ac sn1999aw sn1999bh sn1999by sn1999cc sn1999cl sn1999cp sn1999cw sn1999da sn1999di sn1999dn sn1999dq sn1999ee sn1999ef sn1999ej sn1999ek sn1999em sn1999ex sn1999gd sn1999gh sn1999gi sn1999gp sn2000B sn2000E sn2000H sn2000bh sn2000bk sn2000ce sn2000cf sn2000cn sn2000cp sn2000cu sn2000cw sn2000cx sn2000dg sn2000dk sn2000dm sn2000dn sn2000fa sn2001E sn2001G sn2001N sn2001V sn2001ah sn2001ay sn2001az sn2001bf sn2001bg sn2001br sn2001cj sn2001ck sn2001cp sn2001da sn2001eh sn2001el sn2001en sn2001ep sn2001ex sn2001fe sn2001fh sn2001gc sn2002G sn2002ap sn2002aw sn2002bf sn2002bo sn2002cd sn2002cf sn2002ck sn2002cr sn2002cs sn2002cu sn2002cx sn2002de sn2002dj sn2002dl sn2002do sn2002dp sn2002ef sn2002er sn2002es sn2002eu sn2002fb sn2002fk sn2002ha sn2002hd sn2002he sn2002hu sn2002hw sn2002ic sn2002jg sn2002jy sn2002kf sn2003U sn2003W sn2003Y sn2003bg sn2003cg sn2003ch sn2003cq sn2003du sn2003fa sn2003gn sn2003hu sn2003hv sn2003ic sn2003it sn2003iv sn2003kc sn2003kf sn2004L sn2004S sn2004as sn2004at sn2004aw sn2004bd sn2004bg sn2004bk sn2004dj sn2004dt sn2004ef sn2004eo sn2004et sn2004fu sn2004fz sn2004gc sn2004gs sn2005A sn2005M sn2005am sn2005bc sn2005be sn2005bf sn2005bl sn2005bo sn2005cc sn2005cf sn2005cg sn2005cs sn2005el sn2005eq sn2005eu sn2005gj sn2005hc sn2005hf sn2005hj sn2005hk sn2005iq sn2005kc sn2005ke sn2005ki sn2005kl sn2005ls sn2005lu sn2005mc sn2005mz sn2005na sn2006D sn2006H sn2006N sn2006S sn2006X sn2006ac sn2006aj sn2006ak sn2006al sn2006ax sn2006az sn2006bp sn2006bq sn2006br sn2006bt sn2006bw sn2006bz sn2006cc sn2006cf sn2006cj sn2006cm sn2006cp sn2006cq sn2006cz sn2006em sn2006eq sn2006et sn2006eu sn2006ev sn2006gj sn2006gr sn2006gt sn2006gz sn2006hb sn2006kf sn2006le sn2006lf sn2006mo sn2006nz sn2006oa sn2006ot sn2006sr sn2006te sn2007A sn2007F sn2007S sn2007Y sn2007ae sn2007af sn2007al sn2007ap sn2007au sn2007ax sn2007ba sn2007bc sn2007bd sn2007bj sn2007bm sn2007bz sn2007ca sn2007cg sn2007ci sn2007co sn2007cq sn2007fb sn2007fs sn2007hj sn2007if sn2007jg sn2007kk sn2007le sn2007nq sn2007qe sn2007sr sn2007ux sn2008A sn2008C sn2008D sn2008L sn2008Q sn2008R sn2008Z sn2008ae sn2008af sn2008ar sn2008bf snls03D3bb &nbsp;done<br>&nbsp;Loaded &nbsp; &nbsp; &nbsp; &nbsp; 4388 &nbsp;spectra out of &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;333 &nbsp;templates</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

A set of seamless 0.05-degree, daily SIF product data (FGSIF)

<p>A set of seamless 0.05-degree, daily SIF product data (FGSIF)</p>

opencc-by-4.0Jun 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record

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Your academic CV and NIH biosketch

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