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2,516 results for “Seri”
DATA _2.0_of "Time series characteristics of terrain data:Topography drives history"
Open the record for dataset details and reuse information.
Image series of Rallenreiher (Ardeola ralloides) recorded at Örmisweiher near Illnau, Kanton Zürich, Switzerland
Open the record for dataset details and reuse information.
Bailout From Sinus Jailing: In-Series TAVR-in-TAVR to Avoid Coronary Flow Obstruction
<p>Video from Casenghi M, Oliva OA, Squillace M, Bellamoli M, Poletti E, Popolo Rubbio A, Testa L, Bedogni F, De Marco F. Bailout From Sinus Jailing: In-Series TAVR-in-TAVR to Avoid Coronary Flow Obstruction. JACC Case Rep. 2021 Apr 21;3(4):678-681. doi: 10.1016/j.jaccas.2021.02.022. PMID: 34317602; PMCID: PMC8302790.</p> <p>Abstract</p> <p>Redo transcatheter aortic valve replacement (TAVR) may pose the risk of coronary flow obstruction. We report 2 cases of severe TAVR regurgitation due to different physiopathological mechanisms in which TAVR-in-TAVR could be at high risk for sinus sequestration. Both cases were successfully treated by in-series implantation of a second transcatheter heart valve, thus avoiding sinus sequestration. (<strong>Level of Difficulty: Intermediate.</strong>).</p>
Landsat 8-day surface reflectance time series over forests in the Greater Khingan Mountains and Ziwuling regions of China
<p>The dataset is associated with a research article "A new spatial-temporal depthwise separable convolutional fusion network for generating Landsat 8-day surface reflectance time series over forest regions".</p>
Dynamic cerebrovascular autoregulation in patients prone to postural syncope: Comparison of techniques assessing the autoregulation index from spontaneous variability series
<p>Gelpi F, Bari V, Cairo B, De Maria B, Tonon D, Rossato G, Faes L, Porta A. Dynamic cerebrovascular autoregulation in patients prone to postural syncope: Comparison of techniques assessing the autoregulation index from spontaneous variability series. Auton Neurosci. 2022 Jan;237:102920. doi: 10.1016/j.autneu.2021.102920. Epub 2021 Nov 18. PMID: 34808528.</p> <p>Abstract</p> <p>Three approaches to the assessment of cerebrovascular autoregulation (CA) via the computation of the autoregulation index (ARI) from spontaneous variability of mean arterial pressure (MAP) and mean cerebral blood flow velocity (MCBFV) were applied: 1) a time domain method (TDM); 2) a nonparametric method (nonPM); 3) a parametric method (PM). Performances were tested over matched and surrogate unmatched pairs. Data were analyzed at supine resting (REST) and during the early phase of 60° head-up tilt (TILT) in 13 subjects with previous history of postural syncope (SYNC, age: 28 ± 9 yrs.; 5 males) and 13 control individuals (noSYNC, age: 27 ± 8 yrs.; 5 males). Analysis was completed by computing autonomic markers from heart period (HP) and systolic arterial pressure (SAP) variability series via spectral approach. HP and SAP spectral indexes suggested that noSYNC and SYNC groups exhibited different autonomic responses to TILT. ARI analysis indicated that: i) all methods have a sufficient statistical power to separate matched from unmatched pairs with the exception of nonPM applied to impulse response; ii) ARI estimates derived from different methods might be uncorrelated and, even when correlated, might exhibit a significant bias; iii) orthostatic stressor did not induce any evident ARI change in either noSYNC or SYNC individuals; iv) this conclusion held regardless of the method. Methods for the ARI estimation from spontaneous variability provide different ARIs but none indicate that noSYNC and SYNC subjects have different dynamic component of CA.</p>
Dataset related to article "Early de-tethering: analysis of urological and clinical consequences in a series of 40 children"
<p>clinical dataset of raw data related to article at title</p>
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2020)
<p>This data set contains information on the agricultural land use in Germany for the year 2020.<br> The map was derived from dense time series of Sentinel-2 and Landsat 8 data, Sentinel-1 monthly composites and addtional environmental data. It is based on the methods described in <a href="https://www.sciencedirect.com/science/article/pii/S0034425721005514">Blickensdörfer et al. 2022</a> and can be seen as a continuation of the dataset provided under: <a href="http://zenodo.org/record/5153047#.YWFyXn1CREZ">https://zenodo.org/record/5153047#.YWFyXn1CREZ</a>.<br> The maps can be explored online in a <a href="https://ows.geo.hu-berlin.de/webviewer/landwirtschaft/">webviewer</a>.</p> <p>Due to specific user needs the class catalogue was slightly modified but a translation key (Table 1) and a translated map version (*_V1.tif) is provided. However, it has to be noted that some rather small classes in the previous maps were not differentiated anymore (e.g., onions, carrots, asparagus).Thus, the classes 34, 43, 92, 130, 140, 181 and 182 were excluded from the raster and legend files.</p> <p> </p> <p>Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022.</p> <table> <tbody> <tr> <td> <p><strong>New class code (V2) </strong></p> </td> <td> <p><strong>Class name (V2)</strong></p> </td> <td> <p><strong>Class code (V1)</strong></p> </td> <td> <p><strong>Class name (V1)</strong></p> </td> </tr> <tr> <td> <p>1101</p> </td> <td> <p>Winter wheat</p> </td> <td> <p>31</p> </td> <td> <p>Winter wheat</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>34</p> </td> <td> <p>Other winter cereals</p> </td> </tr> <tr> <td> <p>1102</p> </td> <td> <p>Winter barley</p> </td> <td> <p>33</p> </td> <td> <p>Winter barley</p> </td> </tr> <tr> <td> <p>1103</p> </td> <td> <p>Winter rye</p> </td> <td> <p>32</p> </td> <td> <p>Winter rye</p> </td> </tr> <tr> <td> <p>1201</p> </td> <td> <p>Spring barley</p> </td> <td> <p>41</p> </td> <td> <p>Spring barley</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>43</p> </td> <td> <p>Other spring cereals</p> </td> </tr> <tr> <td> <p>1202</p> </td> <td> <p>Oat</p> </td> <td> <p>42</p> </td> <td> <p>Spring oat</p> </td> </tr> <tr> <td> <p>1300</p> </td> <td> <p>Maize</p> </td> <td> <p>91</p> </td> <td> <p>Maize (silage)</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>92</p> </td> <td> <p>Maize (grain)</p> </td> </tr> <tr> <td> <p>1401</p> </td> <td> <p>Potatoe</p> </td> <td> <p>100</p> </td> <td> <p>Potatoe</p> </td> </tr> <tr> <td> <p>1402</p> </td> <td> <p>Sugar beet</p> </td> <td> <p>80</p> </td> <td> <p>Sugar beet</p> </td> </tr> <tr> <td> <p>1501</p> </td> <td> <p>Rapeseed</p> </td> <td> <p>50</p> </td> <td> <p>Winter rapeseed</p> </td> </tr> <tr> <td> <p>1502</p> </td> <td> <p>Sunflower</p> </td> <td> <p>70</p> </td> <td> <p>Sunflower</p> </td> </tr> <tr> <td> <p>1611</p> </td> <td> <p>Peas</p> </td> <td> <p>60</p> </td> <td> <p>Legume</p> </td> </tr> <tr> <td> <p>1612</p> </td> <td> <p>Broad beans</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1613</p> </td> <td> <p>Lupine</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1614</p> </td> <td> <p>Soy</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1603</p> </td> <td> <p>Vegetables</p> </td> <td> <p>120</p> </td> <td> <p>Strawberry</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>130</p> </td> <td> <p>Asparagus</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>140</p> </td> <td> <p>Onion</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>181</p> </td> <td> <p>Carrot</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>182</p> </td> <td> <p>Other leafy vegetables</p> </td> </tr> <tr> <td> <p>1602</p> </td> <td> <p>Cultivated grassland</p> </td> <td> <p>10</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>200</p> </td> <td> <p>Permanent grassland</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3003</p> </td> <td> <p>Fallow land</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3001</p> </td> <td> <p>Small woody features</p> </td> <td> <p>555</p> </td> <td> <p>Small woody features</p> </td> </tr> <tr> <td> <p>3002</p> </td> <td> <p>Other areas</p> </td> <td> <p>999</p> </td> <td> <p>Other agricultural areas</p> </td> </tr> <tr> <td> <p>4001</p> </td> <td> <p>Grapevine</p> </td> <td> <p>110</p> </td> <td> <p>Grapevine</p> </td> </tr> <tr> <td> <p>4002</p> </td> <td> <p>Hops</p> </td> <td> <p>150</p> </td> <td> <p>Hops</p> </td> </tr> <tr> <td> <p>4003</p> </td> <td> <p>Orchard</p> </td> <td> <p>160</p> </td> <td> <p>Orchards</p> </td> </tr> </tbody> </table> <p> </p> <p>All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube. </p> <p>The models were trained in FORCE and applied to all areas in Germany that were defined as agricultural land, small woody features, heathland or peatland in ATKIS DLM 2020 (Geobasisdaten: © GeoBasis-DE / BKG (2020)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine and hops areas that were not labelled as the respective permanent crop in ATKIS DLM (BKG (2020); labelled as other agricultural areas in the final map). <br> </p> <p>The maps are provided as GeoTiff files together with QGIS legend files for visualization. </p> <p> </p> <p>References:</p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831</p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). </p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell. <br> https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p> </p> <p><a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
Apulian Aqueduct demo site: daily time series of reservoirs water levels and total releases 2010-2019
<p>This dataset contains the time series of water levels and total releases (including spillways) measured by the competent authority for the main reservoirs of Apulian aqueduct (demo site 1), at a daily time step.</p> <ul> <li>Temporal coverage: 2010-2019</li> <li>Spatial coverage: Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo</li> <li>Unit of measure: <ul> <li>water level: masl</li> <li>releases: m<sup>3</sup>/sec</li> </ul> </li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô Deliverable D4.1.</p>
Dataset related to article "Hypofractionated volumetric modulated arc therapy in ductal carcinoma in situ Toxicity and cosmetic outcome from a prospective series"
<p>This record contains raw data related to article "Hypofractionated volumetric modulated arc therapy in ductal carcinoma in situ Toxicity and cosmetic outcome from a prospective series"</p> <p>O</p> <p>BJECTIVE:</p> <p>Hypofractionated radiotherapy in early stage breast cancer is an effective adjuvant treatment, but there is a lack of randomized data for patients with ductal carcinoma in situ (DCIS). The aim of this study is the evaluation of skin toxicity and cosmesis, and early clinical outcome of DCIS patients enrolled in an institutional Phase II trial of hypofractionated breast irradiation.</p> <p>METHODS:</p> <p>137 DCIS patients were enrolled in the trial. All patients underwent volumetric modulated arc therapy (VMAT) to the whole breast with a total dose of 40.5 Gy in 15 fractions over 3 weeks, without tumour bed boost. Acute and late skin toxicities were recorded. Cosmetic outcomes were assessed as excellent/good or fair/poor. Early clinical outcome was reported.</p> <p>RESULTS:</p> <p>Median age was 58 y.o. (range 30-86). The median follow-up time was 22 months (range 6-45). At the end of the radiotherapy, skin toxicity was grade G1 in 56% of the patients, G2 in 15%, no patients presented G3 toxicity. In the range of 3-9 months of follow-up, the skin toxicity was G1 in 28% of patients, no G2-G3 cases; cosmetic outcome was good/excellent in 95% of patients. In the follow-up interval of 9-24 months, the skin toxicity was G1 in 12% of patients, no G2-G3 toxicity; cosmetic outcome was good/excellent in 96% of patients. After an early evaluation of clinical outcomes, 5 patients (3.6%) presented an in-breast recurrence.</p> <p>CONCLUSION:</p> <p>Hypofractionated radiotherapy using VMAT is a viable option for DCIS. A longer follow-up is needed to assess clinical outcomes and late toxicity. Advances in knowledge: The use of hypofractionated VMAT is dosimetrically feasible for treating breast DCIS.</p> <p> </p>
Retrograde amnesia in autoimmune encephalitis with anti-LGI1 and anti-CASPR2 antibodies: a case series and systematic review of the literature
Open the record for dataset details and reuse information.
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2017, 2018 and 2019)
<p>Detailed maps of agricultural landscapes are a valuable data source for manifold applications, such as environmental modelling, biodiversity monitoring or the support of agricultural statistics. Satellites from the European Copernicus program, especially, Sentinel-1 and Sentinel-2, as well as the Landsat missions operated by NASA/USGS, acquire data with a spatial resolution (10 m to 30 m) that is sufficient to identify field structures in complex agricultural landscapes. Time series of combined Sentinel-2 and Landsat data facilitate to differentiate crop types with a high thematic detail based on differences in land surface phenology. However, large data gaps due to frequent cloud cover may hamper such classification approaches. </p> <p>We thus combined dense interpolated times series of Sentinel-2A/B and Landsat data with monthly composites of Sentinel-1 backscatter data to overcome periods with high cloud contamination. To further account for regional variations along the agroecological gradient within Germany, we additionally included a broad set of spatially explicit environmental data in a random forest classification model. </p> <p>All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube. </p> <p>For each year (2017, 2018 and 2019) we trained an individual random forest model with 24 agricultural classes. Each model was independently validated with area adjusted overall accuracies of 80% (2017), 79% (2018), and 78% (2019). Further details regarding the data and methods used as well as class wise accuracies can be found in Blickensdörfer et al. (2022). </p> <p>The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: © GeoBasis-DE / BKG (2018)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine/hops areas that were not labelled as the respective permanent crop in ATKIS DLM (labelled as other agricultural areas in the final map). </p> <p>The maps are provided as GeoTiff files together with a QGIS legend file for visualization. </p> <p>Class catalogue:</p> <p>10 Grassland<br> 31 Winter wheat<br> 32 Winter rye<br> 33 Winter barley<br> 34 Other winter cereal<br> 41 Spring barley<br> 42 Spring oat<br> 43 Other spring cereal<br> 50 Winter rapeseed<br> 60 Legume<br> 70 Sunflower<br> 80 Sugar beet<br> 91 Maize<br> 92 Maize (grain)<br> 100 Potato<br> 110 Grapevine<br> 120 Strawberry<br> 130 Asparagus<br> 140 Onion<br> 150 Hops<br> 160 Orchard<br> 181 Carrot<br> 182 Other vegetables<br> 555 Small woody features<br> 999 Other agricultural areas</p> <p> </p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831</p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 19. August 2021). </p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell. <br> https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).</p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p> </p> <p><a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2021 by Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
High-rate GNSS data in seismic moment tensor inversion. The study of anthropogenic earthquakes - GNSS displacement time series
<p>The dataset of GNSS displacement time series with duration of 30 seconds before and 90 seconds after the origin time of the mining tremors. The dataset was used in the research on High-rate GNSS data in seismic moment tensor inversion. The study of anthropogenic earthquakes.</p> <p>Further description of the HR-GNSS processing can be found in the paper by Kudlacik et al. (2021) and in the research paper "High-rate GNSS data in seismic moment tensor inversion. The study of anthropogenic earthquakes".</p>
Data in experimental stand-alone microgrid: solar production, domestic loads, battery storage and meteorological series.
<p>At the centre of Reunion Island, a region denoted « Mafate » is non-connected to the main electrical grid. Presented data come from an experimental local microgrid between 3 homes at the place called « Roche Plate », where electrical production is obtained by photovoltaic panels and storage by batteries.</p>
Multiphasic personality assessment in a case series of adolescent patients with suicidal ideation and/or attempt
<p><strong>Introduction. </strong>This database includes the raw data linked with the paper “Multiphasic personality assessment in a case series of adolescent patients with suicidal ideation and/or attempt”.</p> <p>In this paper, we reported sociodemographic features, family history of any DSM-5 psychiatric disorder, personal history of any DSM-5 psychiatric disorder, suicidal ideation and or/attempt, early risk factors for suicide, and psychopathological assessment. We aimed to explore the differences in personality characteristics between a group of adolescents with suicidal ideation (SI) and a group with a history of suicidal attempt (SA).</p> <p><strong>Methods</strong>. We enrolled 55 adolescent patients (12-18) who presented SI and/or SA, assed with the Columbia Suicide Severity Rating Scale (C-SSRS), who filled in the Minnesota Multiphasic Personality Inventory – Adolescent (MMPI-A).</p> <p><strong>Results (in brief). </strong>Adolescents in the SA group have higher difficulties in social relations, risky behaviours, and a more intense suicidal ideation compared to the ones in the SI group. Adolescents in the SA group scored higher in Omission, in the Lie scale, in the Conduct Problem Scale, in the Less Aspirations Scale, in the Repression Scale and in the item 283, compared to the other group. The results showed that the assessment of certain features (e.g. tendency to lie, repression) by means of the MMPI-A questionnaire could be useful in identifying adolescents at high risk of suicide.</p>
Dataset for "Estimation of free energy for gate opening in a series of flexible adsorbents based on adsorption observations"
<p>Supporting dataset for: “Estimation of free energy for gate opening in a series of flexible adsorbents based on adsorption observations”, Sebastian Ehrling, Hiroki Miura, Stefan Kaskel and Jack D. Evans, <strong>2023</strong></p> <p><strong>Contains:</strong></p> <ul> <li>data: adsorption isotherms in .aif, .json and .csv format</li> <li>fits: parameters for the model fits to the isotherms in .pkl and .csv format.</li> <li>notebooks: python notebooks demonstrating how the isotherms were fit, analysed for enthalpy and free energy.</li> </ul>
Cognitive function in DMD carriers: personal case series and literature review
<p>Introduction: This database includes the raw data linked with the paper “Cognitive function in DMD carriers: personal case series and literature review” published on “Acta Myologica”. Aim: This study aimed to investigate the cognitive involvement in a cohort of DMD carriers.</p> <p>Methods: Our case series consisted of 22 carrier patients from two different centers (IRCCS Mondino, Pa- via and Policlinico Gemelli, Rome), for whom we retrospectively collected cognitive, clinical and genetic data.</p> <p>Results: We found that the average IQ of DMD carriers was lower (74; very low) than the average score on normal curve (100 as average standard score). Furthermore, about 50% of them fell in the “extremely low IQ” range, compared with 2-3% of general population. A higher incidence of intellectual disability was confirmed in symptomatic DMD carriers (mean IQ 66; extremely low) from the IRCCS Mondino, but not for the asymptomatic ones (mean IQ 99; average), when compared to the general population.</p>
AtGenExpress: Developmental series (shoots and stems)
GEO Series GSE5633. Arabidopsis thaliana. 42 samples. Type: Expression profiling by array.
Identification of potential biomarkers for patients with neurodegenerative parkinsonian syndromes using serum cytokine microarray analysis; series 6
GEO Series GSE32037. Homo sapiens. 16 samples. Type: Protein profiling by protein array.
AtGenExpress: Pathogen Series: Response to Erysiphe orontii infection
GEO Series GSE5686. Arabidopsis thaliana. 48 samples. Type: Expression profiling by array.
FM95-14 Fetal Myoblast Differentiation Time Series
GEO Series GSE3780. Homo sapiens. 60 samples. Type: Expression profiling by array.
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.