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76,402,788 results
Core collapse supernova yield from the post-processing of a long-term 3D simulation
<p>This dataset accompanies the publication<i> "Production of 44Ti and Iron-group Nuclei in the Ejecta of 3D Neutrino-driven Supernovae"</i> published in the <i>Astrophysical Journal Letters</i> Volume <strong>957</strong>, Issue 2, id.L25.</p><p>The dataset consists of an ACII text file that contains the isotopic yields from the post-processing of a 3D long-term supernova simulation for a 18.88 solar mass progenitor model. The yields are given in units of solar masses. </p><p><strong>Important: The dataset does not include the full stellar yield. </strong>It only represents the inner 0.142 solar masses. The total ejecta mass is expected to be larger. </p><p>The dataset is also available on the websites of the Max-Planck Institute for Astrophysics in Garching, Germany: https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Sieverding2023/</p><p>The results have been obtained using the open source nuclear reaction network code <a href="https://github.com/starkiller-astro/XNet">XNet.</a></p><p>Calculations have been performed on the supercomputing cluster Cobra the Max-Planck Computing and Data Facility (MPCDF) in Garching, Germany. </p>
Properties and formaldehyde removal efficiency of biocarbon-MnO2 particles
<p>The dataset includes information about biocarbon particles doped with different concentrations of MnO2 photocatalyst (BC-MnO2)</p><p>Six different samples: MnO2, biocarbon, BC-MnO2-1, BC-MnO2-2, BC-MnO2-3, BC-MnO2-4</p><p>Characterization of the samples:</p><p>* SEM images collected using scanning electron microscope (Carl Zeiss SUPRA 35 VP)</p><p>* XRD data collected using Bruker D2 Phaser diffractometer</p><p>* Porosity data collected using physisorption analyzer (Autosorb iQ-XR-AG-AG). The dataset contains data about isotherms and pores size distributions from tests under nitrogen gas (meso and macro porosity) and CO2 gas (microporosity).</p><p>Formaldehyde removal potential of the samples: The raw data were generated from an electrochemical formaldehyde sensor (Stox-HCHO) at ambient conditions (temperature of 23 °C, relative humidity between 40% and 46%, and conventional visible light). The sensor was placed in a test chamber equipped with the sensor and 8 µl of formaldehyde (HCHO) solution was injected. Then the test chamber was hermetically closed and changes in formaldehyde levels were measured. The same sensor provided information about temperature and relative humidity in the test chamber. Data was aquired using TVOC-HCHO logger software. The formaldehyde removal efficiency (%) of the samples after 8h of experiment was determined from the raw data.</p>
Dataset for: A continuous classification of the 480,000 lakes of the conterminous US based on geographic archetypes
<p>These datasets were used in a journal article with the goal of developing a new geographic classification approach for ~480,000 lakes ≥ 1 ha in the conterminous U.S. based on archetypes defined as endmembers with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. We identified seven lake archetypes; each study lake was then assigned weights for each of the archetypes. The data used to develop the archetypes, archetype weights, and variables used in associated analyses is provided in three data tables. The first includes the lake-specific transformed predictors used to generate the seven archetypes, the weights corresponding to each archetype, the archetype with the maximum weight and the weight of that maximum archetype. The second provides lake-specific raw values for each predictor and for the 19 response variables used to explore aspects of the archetype classification. The final metadata table provides a data dictionary for all columns in the previously mentioned data tables.</p>
Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms (supplementary material)
<p>Supplementary material for Sawi et al., 2023, <i>Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms </i>(The Seismic Record). Catalog of repeating earthquakes in sequences on a 10-km long segment of the San Andreas Fault in California from 1984-2019. </p><p> </p><p><strong>Catalog Header</strong></p><p>YR/MO/DY...........Date of event</p><p>HR/MN/SC...........Time of event</p><p>LAT/LON/DEP........Location of event</p><p>EX/EY/EZ...........Relative location uncertainty (in m)</p><p>MAG................NCSN magnitude</p><p>evID.................NCSN event ID</p><p>seqID................Repeating earthquake sequence ID</p><p>isRESp............Is quasi-periodic RES (bool)</p><p> </p><p><strong>References: </strong></p><p>Sawi T., Waldhauser F., Holtzman B. K., Groebner, N. (2023) Detecting repeating earthquakes on the San Andreas Fault with unsupervised machine-learning of spectrograms. The Seismic Record. </p><p>Waldhauser, F., and Schaff, D. P. (2021). A Comprehensive Search for Repeating Earthquakes in Northern California: Implications for Fault Creep, Slip Rates, Slip Partitioning, and Transient Stress. J Geophys Res B Solid Earth, 126(11), 1–22. <a href="https://doi.org/10.1029/2021JB022495">https://doi.org/10.1029/2021JB022495</a></p>
The DataCons Project: An Open-Access Archive of Late Roman Consular Dates
<p>The DataCons Project offers an open-access dataset of late Roman consular dating formulae from CE 284 to 541. Aimed at aggregating consular materials discovered globally, presently it contains over 4,800 documents penned in three distinct scripts, originating from ten regions of the late Roman world and categorised by material type and textual content.</p><p>With its roots in prominent scholarly references, every entry undergoes rigorous verification, including palaeographical assessments and exact transcription of dating formulae. Distinct columns highlight potential dating, the author's selected date, and further specificity, ensuring the dataset's precision. Its evolution promises broader temporal coverage, and its structure facilitates ease of use and extensive potential for interdisciplinary research.</p><p>The current version of the dataset (2.0.0) presents the Latin and Greek documentation dated CE 476 to 526, exclusively comprising papyri and inscriptions. It is anticipated that there will be periodic updates and an upcoming release of an online database titled <i>DataCons: The Digital Database of Late Roman Consular Dates</i>. This will enhance and support research utilising the DataCons dataset.</p>
Base datos segundo y tercer año 13_15_Teoría de la Educación EDU2012-32725.sav
<p>Base de datos de 426 sujetos, formada por tres grupos de alumnos (los grupos vienen recogidos en la primera variable de la base de datos). El primer grupo (grupo 1), correspondiente al curso académico 2012-13, de 65 sujetos, está formado, a su vez, por dos subgrupos; el primer grupo corresponde al profesor A y el segundo al profesor B (los profesores se recogen en la cuarta de las variables). El segundo (grupo 2), correspondiente al curso académico 2013-14, de 181 sujetos, está formado, a su vez, por tres subgrupos de alumnos; el primer grupo corresponde al profesor A , el segundo al profesor B y el tercero al profesor C (los profesores se recogen en la cuarta de las variables). El tercero (grupo 3), correspondiente al curso académico 2014-15, de 180 sujetos, también está formado por tres subgrupos de alumnos; el primer grupo corresponde al profesor A , el segundo al profesor B y el tercero al profesor C (los profesores se recogen en la cuarta de las variables).</p><p>La muestra, de la asignatura de Teoría de la Educación, cursada en 1º curso de los grados de Educación Social y Pedagogía de la Universidad de Valencia, se usó para analizar el impacto de la aplicación de métodos centrados en el aprendizaje, sobre los enfoques de aprendizaje de los alumnos de estos grupos y sobre sus capacidades y su percepción del entorno de aprendizaje articulado por sus profesores.</p><p>Los datos correspondientes al curso 2012-13 se tomaron de los alumnos en situación de postest, una vez acabada la docencia de la materia en cuestión, usando en ella metodología más bien tradicional, para tener puntuaciones de anclaje de cara a los siguientes cursos, En los dos cursos siguientes (2013-14 y 2014-15) se aplicaron a los tres grupos de la materia Teoría de la Educación métodos centrados en el aprendizaje y se pasó, cada uno de los cursos, pretest y postest, con un diseño transversal, ya que los alumnos eran diferentes cada curso.</p><p>En este trabajo se quería valorar la influencia de los métodos centrados en el aprendizaje sobre los enfoques de aprendizaje y sobre diversas capacidades/habilidades de tres grupos de alumnos (426 sujetos) de 1º curso de los grados de Pedagogía y Educación Social de la Universidad de Valencia en la asignatura de Teoría de la Educación. Se utilizó un diseño cuasiexperimental de grupo de control no equivalente, con medidas de postest en el grupo de control y de pretest y postest en los grupos experimentales, evaluando los enfoques de aprendizaje mediante el cuestionario CPE y las capacidades del alumno mediante el cuestionario SEQ. </p><p>La base de datos, en SPSS 17.0, está integrada por 50 variables, siendo las cuatro primeras nominales y las restantes escalares, con valores que van de 1 (máximo desacuerdo) a 5 (máximo acuerdo). De la variable 5 a la 16 recogen resultados del cuestionario CPE, que evalúa los enfoques de aprendizaje del alumnado. </p><p>En el fichero que se ha subido, en txt, junto a la base de datos, se explican las variables y otras cuestiones relativas a la base de datos.</p><p> </p><p> </p><p> </p><p> </p>
Base segundo año 13_14 Validación SEQ EDU2012-32725
<p>Base de datos de resultados obtenidos de una muestra de 805 sujetos de tres universidades de la ciudad de Valencia: Universitat de Valènica, Universitat Politècnica de València y Universidad Católica de Valencia.</p><p>Esta muestra se utilizó para la validación del cuestionario SEQ (Study Engagement Questionnaire, de Kember y Leung (2005) en población española.</p><p>La base de datos, en SPSS, está integrada por 23 variables: Universidad, Rama, Titulación, Curso, Sexo, Edad, PsCrit (Pensamiento crítico), PesCrea (Pensamiento creativo), AprenAut (Aprendizaje autogestionado), Adapt (Adaptabilidad), Respro (Resolución de problemas), HabCom (Habilidades de comunicación), HabIn (Habilidades intergrupales), ManTec (Manejo nuevas tecnologías), AprenAct (Aprendizaje activo), EnCom (Enseñanza para la comprensión), Feedback (Feedback aprendizaje), Evaluación (Evaluación), RelProAl (Relación profesor-alumno), CarTrab (Carga de trabajo), RelEst (Relación con otros estudiantes), AprenCoop (Aprendizaje cooperativo), Coherencia (Coherencia plan de estudios)</p><p>Aviso: a partir de la variable Edad, se ha incluido el Nombre de la variable y la Etiqueta de la variable</p><p> </p><p> </p><p> </p><p> </p><p> </p>
Study of Calabrian Sounding Objects. Interviews and Field Notes - Festa della Pita
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Field Notes - G. Orlando
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Interview and Field Notes - G. Guidoccio
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Field Notes - S. Trunzo
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Field Notes - S. Mancini
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Study of Calabrian Sounding Objects. Interview and Field Notes - G. Vaccaro
<p>This dataset contains the ethnomusicological data concerning the study of traditional sounding objects in Calabria generated for the EU-funded research LoMus - Local Sound for a New Musicality. </p><p> </p><p>LoMus - Local Sound for a New Musicality. Enhancing Musical Participation through a Local Sonic Practice is a research project funded under the Marie Skłodowska Curie Action. LoMus investigates ways of expanding musical participation through the use of sounding objects and contemporary music techniques.</p>
Database Mobbing-UNIPSICO Scale in Spanish Teachers
<p>This dataset contains data of non-university teachers collected by paper and pencil at the workplace between October 2015 and May 2020. These data were collected by employees working in the INVASSAT (Instituto Valenciano de Seguridad y Salud en el Trabajo, Government of the Valencian Community, Spain). The INVASSAT employees went to all educational center and informed the director, union representative, and teachers at each school of the procedure. Then each teacher filled in the questionnaire individually. The questionnaire was done in the presence of the INVASSAT employees to answer any doubts, and the filled questionnaires were given to the INVASSAT employee.</p><p>The file contains demographic variables, the responses to the 20 items of the Mobbing-UNIPSICO scale questionnaire, the responses to the items on alcohol, tobacco and medication use, and the response to the item regarding the necessity of professional support.</p><p>The name of the variables and the value labels have been written in English to facilitate their understanding.</p><p>Data and codebooks are provided in csv format, following the FAIR principles.</p><p>Three files are provided:</p><p>1. Mobbing database, with the data related to sample characteristics and the answers to the items of the questionnaires and the other items.</p><p>2. Database codebook of variables, with information of the labels of the variables of the Database file.</p><p>3. Variable values codebook, with the labels of the values of the variables in the Database file.</p><p> </p>
Database of local seismicity registered on ocean bottom seismometers (OBS). Database related to Bornstein et al. (accepted in Earth and Space Science), PICKBLUE
<p>We assembled a database of Ocean Bottom Seismometer (OBS) waveforms and manual P and S picks from local seismicity, on which we trained PickBlue, a deep-learning picker, using the seismometer data and the hydrophone channel. The dataset belongs to Bornstein et al. (accepted 2023 in Earth and Space Science). The picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records.</p><p>The complete database is also accessible with SEISBENCH: <br><a href="https://seisbench.readthedocs.io">https://seisbench.readthedocs.io</a><br>SEISBENCH on github:<br><a href="https://github.com/seisbench">https://github.com/seisbench</a></p><p>Related paper:</p><p>Bornstein, T., Lange, D., Münchmeyer, J., Woollam, J., Rietbrock., A., Barcheck, G., Grevemeyer, I., Tilmann, F. (accepted 2023 in Earth and Space Science). PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning, Earth and Space Science. </p>
S1000 corpus, large-scale tagging results and other supplementary files
<p>Data associated with the S1000 corpus</p><p>The tagger software for which the dictionary files in <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/tagger-organisms-dictionary-S1000.tar.gz">tagger-organisms-dictionary-S1000.tar.gz </a>can be used with can be found here: <a href="https://github.com/larsjuhljensen/tagger">https://github.com/larsjuhljensen/tagger</a></p><p>The online version of the annotation documentation can be found here: <a href="https://katnastou.github.io/s1000-corpus-annotation-guidelines/">https://katnastou.github.io/s1000-corpus-annotation-guidelines/</a></p><p>The S1000 corpus split in training, development and test sets in BRAT format can be found in <a href="https://zenodo.org/api/records/10285825/files/S1000-corpus.tar.gz">S1000-corpus.tar.gz</a><a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/S1000-corpus.tar.gz?versionId=ac7ce430-c265-49bb-8c8f-9b5f8e271cbe"> </a>and in CoNLL format here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/s1000-conll.tar.gz">s1000-conll.tar.gz</a></p><p>The tagging results of Jensenlab tagger for the S1000 test set are here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/S1000-jensenlab-tagger.tar.gz?versionId=d8d9c9f5-ee3b-4738-aefa-a4a95475d25d">S1000-jensenlab-tagger.tar.gz</a></p><p>The result from the large scale run in entire PubMed and PMC Open Access articles for Jensenlab tagger is provided here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/Jensenlab_tagger_large_scale_matches_with_rank.tsv.gz?versionId=48825928-9fc9-423c-8a4c-4f8994e95805">Jensenlab_tagger_large_scale_matches_with_rank.tsv.gz</a></p><p>The model used for the large scale run of the transformer-based method is here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/S1000_Transformer_based_tagger_large_scale_model.tar.gz?versionId=8e974f64-9abc-4449-a377-e3f97e91d612">S1000_Transformer_based_tagger_large_scale_model.tar.gz</a> and the results from the large scale tagging here: <a href="https://zenodo.org/api/files/b8a0e221-3cc3-4db5-a2e9-f19a1bd2e5cb/Transformer_based_tagger_large_scale_matches_with_rank.tsv.zip?versionId=dc21a6ba-9763-4130-9f02-0341a885c692">Transformer_based_tagger_large_scale_matches_with_rank.tsv.zip</a></p>
BASE DE DATOS CON DIMENSIONES Y SUBDIMENSIONES CECAPEU 2023 para perfiles en manejo competencia AaA.sav
<p>Base de datos de 1234 sujetos de tres universidades de la ciudad de Valencia, dos públicas (Universidad de Valencia y Universidad Politécnica de Valencia), y una privada (Universidad Católica de Valencia).</p><p>La base de datos se utilizó para analizar los perfiles en el manejo de la competencia "aprender a aprender" de uma muestra de alumnos universitarios lo suficientemente representativa.</p><p>La base de datos, en SPSS 26.0, está integrada por 49 variables, siendo las cinco primeras nominales (Universidad, Facultad, Titulación, Curso, Sexo), la sexta ordinal (Edad), y las variables 8 a la 11 de escala, con valores que pueden oscilar de 0 a 10 (son calificaciones) y las restantes escalares. Desde de la variable 12 a la 49 son puntuaciones de escala, con valores que van de 1 (máximo desacuerdo) a 5 (máximo acuerdo) (se trata de puntuaciones medias de sumatorio de ítems integrantes de cada dimensión; los ítems de los que emergen tenían puntuación que oscilaba de 1 -máximo desacuerdo- a 5 máximo acuerdo-). Estas variables son puntuaciones de dimensiones y subdimensiones del cuestionario CECAPEU, que evalúa el aprendizaje de la competencia "aprender a aprender" del alumnado universitario. </p><p>En el fichero que se ha subido, en txt, junto a la base de datos, se explican las variables y otras cuestiones relativas a la base de datos.</p>
Base de datos para validación CECAPEU 2019 EDU2017-83284-R.sav
<p>Base de datos de 1237 sujetos de tres universidades de la ciudad de Valencia, dos públicas (Universidad de Valencia y Universidad Politécnica de Valencia), y una privada (Universidad Católica de Valencia).</p><p>El trabajo realizado con esta muestra se centró en el diseño y validación de un cuestionario cuantitativo estandarizado para evaluar la adquisición de la competencia "aprender a aprender" en estudiantes universitarios, dadas las limitaciones de los actualmente disponibles. Para ello se hizo uso de un diseño de validación de pruebas. </p><p>Se usó un muestreo no probabilístico intencional, seleccionándose de cada universidad alumnado de una de sus grandes áreas de conocimiento: Ciencias de la Salud (UVEG), Ingenierías y Arquitecturas (UPV), y Educación (UCV). Se pretendía lograr una muestra lo suficientemente variada y representativa de grandes ámbitos/áreas de conocimiento diferentes, cada uno de una de estas tres universidades.</p><p>El producto final es un cuestionario con cinco dimensiones/escalas (cognitiva, metacognitiva, afectivo-motivacional, social-relacional y ética), veintiuna subdimensiones/ subescalas y 85 ítems, más sólido y completo que los anteriormente disponibles. Es un instrumento que permite el avance del conocimiento en este ámbito y que será útil para los investigadores, sirviendo para el diagnóstico y evaluación de la competencia y para contrastar resultados en muestras amplias de población.</p><p> </p><p>La base de datos, en SPSS 25.0, está integrada por 206 variables, siendo las seis primeras nominales y las restantes escalares, con valores que van de 1 (máximo desacuerdo) a 5 (máximo acuerdo). De la variable 7 a la 204 recogen resultados de los 198 ítems iniciales, diseñados antes de su validación estadística definitiva, del cuestionario CECAPEU, que evalúa el aprendizaje de la competencia "aprender a aprender" del alumnado universitario. Las variables 205 y 206 recogen los datos de dos ítems recodificados.</p><p>En el fichero que se ha subido, en txt, junto a la base de datos, se explican las variables y otras cuestiones relativas a la base de datos.</p><p> </p><p> </p>
High resolution pond velocity measurements, Idaho21
<p>These scientific data were obtained by Jeffrey Nielson and Stephen Henderson of Washington State University, working in collaboration with Sandra Mayne, Caren Goldberg and Jeffrey Manning. High-resolution current meters were used to obtain detailed measurements of water velocity, with supporting measurements of wind velocity and water temperature profiles. Overview of observations in referenced Henderson et al. (2024) L&O paper, more details in included files. </p>
St Clair River delta velocities - North, Middle and South channels
<p>Velocity data collected from the Middle Channel of the St. Clair River Delta. These data were collected using a vertically mounted ADCP, Teledyne RDI Sentinel V, 1000MHz.</p><p>The data are velocity magnitude and direction beginning 0.99m above the riverbed and a value reported every 0.5 meters of depth to within approximately 1.5 meters of the surface. </p><p> </p><p>-The instrument was set up to ping every 1 second for 120 seconds with a new collection of vertical bins collected beginning every 600 seconds. </p><p>-Setup provides a two minute average, in each bin, every 10 minutes</p><p>'Range to Boundary' set by pressure</p><p>removed the 'side lobe interference'</p><p> </p><p>Instruments were deployed on different days but generally have data for the following period</p><p>Start Date Dec 2018 10:10 am Eastern Standard Time</p><p>End Date: April 2019 12:20 pm Eastern Standard Time</p><p> </p><p>The instruments were placed at the following coordinates:</p><p>North Channel: lat: N42.61720 Long: W82.57020 </p><p>Middle Channel: lat: N42.59983 long: W82.60316</p><p>South Channel: lat N42.58007 long: W82.56192</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.