Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,145
datasets available to search
ShareScore release 0.9.0
Dataset results
3,145 results for “Well Being”
Rietveld quantitative phase analysis of Oil Well Cement: in situ hydration study at 150 bars and 150ºC
<p>Raw data for: Rietveld Quantitative Phase Analysis of Oil Well Cement: In Situ Hydration Study at 150 Bars and 150 °C;</p> <p>doi: <a href="https://doi.org/10.3390/ma12121897">https://doi.org/10.3390/ma12121897</a></p> <p>Oil well cements are multimineral materials that hydrate under high pressure and temperature. Its overall reactivity at early ages is studied by a number of techniques including the consistometer. However, for a proper understanding of the performances of these cements in the field, the reactivity of every component, at the field conditions, must be analysed. So far, <em>in situ</em> high energy synchrotron powder diffraction studies of hydrating oil well cement pastes have been carried out but the quality of the data was not appropriated for Rietveld quantitative phase analyses. Therefore, the phase reactivities were followed by the inspection of the evolution of non-overlapped diffraction peaks. Very recently, we have developed a new cell specially designed to rotate under high pressure and temperature. Here, this spinning capillary cell is used to <em>in situ</em> study the hydration of a commercial oil well cement paste at 150 bars and 150 ºC. The powder diffraction data have been analysed by the Rietveld method to quantitatively determine the reactivities of each component phase. The reaction degree of alite was 90% after 7 hours and that of belite was 42% at 14 hours. These analyses are accurate as the <em>in situ</em> measured crystalline portlandite content at the end of the experiment, 12.9 wt%, compares relatively well with the value determined <em>ex situ</em> by thermal analysis, 14.0 wt%. The crystalline calcium silicates forming at 150 bars and 150 ºC are also discussed.</p>
KE042-026011 Kilmalkedar Well
Class: Ritual site - holy well Townland: CILL MAOILCHÉADAIR The Early Christian and Medieval ecclesiastical complex at Kilmalkedar (KE042-026----) lies at the foot of the W slopes of Reenconnell hill, overlooking Smerwick Harbour. The Reenconnell ridge peaks at 907 feet/276m to NE of the site and the area around Kilmalkedar is sheltered on its N and S sides by spurs of this hill. (12) (KE042-026011-) Fig. 192f. The holy well located in the field to W of the roadway is now dry but the lintelled hollow that marks its site is still preserved. The cross-inscribed stone standing beside it is .92m high and bears a plain Latin cross on its W face. Source: Objaverse 1.0 / Sketchfab
Well
Its a stone wall well with a wooden roof n wooden pulley. It is almost 50-60 yrs old well. Source: Objaverse 1.0 / Sketchfab
Hampstead Chalybeate Well
The Chalybeate Well on Well Walk, Hampstead, London. From the inscription: ""To the memory of the Hon. Susanna Noel who with her son Baptist 3rd Earl of Gainsborough gave this well together with 6 acres of land to the use and benefit of the poor of Hampstead 20 December 1698." Date: 1882 https://historicengland.org.uk/listing/the-list/list-entry/1379173 https://alondoninheritance.com/under-london/chalybeate-well-hampstead/ 249 photos taken in July 2020 with a Sony a6000 and processed in Reality Capture. Texture at highest point slightly cleaned up. Source: Objaverse 1.0 / Sketchfab
West Heslerton - Well
Timber lined well excavated in the 1990s the oak planks lining the well were from a tree that started growing in AD410 and was cut down during October AD724. It is posible that this represnts a re-lining of a much earlier Well structure created in the Roman period. From 9 images from the excavation archive not taken for modelling purposes. The very high contrast between the black waterlogged timbers, which are in themselves very difficult to model in the best conditions, and the chalk and chert boulders, which may have served as foundations for some sort of superstructure, and the small number of effectively random views mean that the planks in the model are more fragmented than in reality. The results are imperfect but reflect how much this mode of recording/presentation is transfromational. How I wish that I had taken more and better positioned photographs then! Source: Objaverse 1.0 / Sketchfab
Small french artisan well
This well was photoscanned using 33 photos and the MicMac software. There is obviously still room for improvements. The ground around the well was particularly wrongly reconstructed and the texture projection was a bit off. Source: Objaverse 1.0 / Sketchfab
Old Water well - Dinan
105 shots - panasonic GF7 - Processed with agisoft Photoscan Not great but I won't work on it anymore, miss high texture on some parts. So I will just make it available to anyone. Hope some will find use of it. Enjoy. Source: Objaverse 1.0 / Sketchfab
Historic Well - iPhone LiDAR - Scaniverse
Quick scan of a XIX century well in the main market square in [Kazimierz Dolny](https://theculturetrip.com/europe/poland/articles/the-best-things-to-see-and-do-in-kazimierz-dolny-poland/), a very picturesque town in Poland. Scanned with a toddler in one hand and the iPhone in the other, going around the well and avoiding capturing tourists in the frame ;-) Scanned with [Scaniverse](https://scaniverse.com/) Cleaned up a little bit in CloudCompare. I'm trying to post 1 scan a day in 2021. You can follow the tag [#1scanaday](https://sketchfab.com/search?q=1scanaday). Source: Objaverse 1.0 / Sketchfab
Wizard Well
This is the beginning stage of a Wizard sci fi well that has been overgrown with vines and almost forgotten about. Source: Objaverse 1.0 / Sketchfab
"Abdominal pain in emergency departments in a well-developed primary care system, a retrospective observational study"
<p>This is the dataset that is used for the original article:</p> <p>“Abdominal pain in emergency departments in a well-developed primary care system, a retrospective observational study”</p>
96 wells fluorescence reading and R code statistic for analysis
<p><strong>Overview</strong></p> <p>Data points present in this dataset were obtained following the subsequent steps: To assess the secretion efficiency of the constructs, 96 colonies from the selection plates were evaluated using the workflow presented in Figure Workflow. We picked transformed colonies and cultured in 400 μL TAP medium for 7 days in Deep-well plates (Corning Axygen®, No.: PDW500CS, Thermo Fisher Scientific Inc., Waltham, MA), covered with Breathe-Easy® (Sigma-Aldrich®). Cultivation was performed on a rotary shaker, set to 150 rpm, under constant illumination (50 μmol photons/m<sup>2</sup>s). Then 100 μL sample were transferred clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA) and fluorescence was measured using an Infinite® M200 PRO plate reader (Tecan, Männedorf, Switzerland). Fluorescence was measured at excitation 575/9 nm and emission 608/20 nm. Supernatant samples were obtained by spinning Deep-well plates at 3000 × <em>g</em> for 10 min and transferring 100 μL from each well to the clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA), followed by fluorescence measurement. To compare the constructs, R Statistic version 3.3.3 was used to perform one-way ANOVA (with Tukey's test), and to test statistical hypotheses, the significance level was set at 0.05. Graphs were generated in RStudio v1.0.136. The codes are deposit herein.</p> <p> </p> <p><strong>Info</strong></p> <p>ANOVA_Turkey_Sub.R -> code for ANOVA analysis in R statistic 3.3.3</p> <p>barplot_R.R -> code to generate bar plot in R statistic 3.3.3</p> <p>boxplotv2.R -> code to generate boxplot in R statistic 3.3.3</p> <p>pRFU_+_bk.csv -> relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>sup_+_bl.csv -> supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>sup_raw.csv -> supernatant mCherry fluorescence dataset of 96 colonies for each construct.</p> <p>who_+_bl2.csv -> whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>who_raw.csv -> whole culture mCherry fluorescence dataset of 96 colonies for each construct.</p> <p>who_+_Chlo.csv -> whole culture chlorophyll fluorescence dataset of 96 colonies for each construct.</p> <p>Anova_Output_Summary_Guide.pdf -> Explain the ANOVA files content</p> <p>ANOVA_pRFU_+_bk.doc -> ANOVA of relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>ANOVA_sup_+_bk.doc -> ANOVA of supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>ANOVA_who_+_bk.doc -> ANOVA of whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of <em>Chlamydomonas reinhardtii</em> </p> <p>ANOVA_Chlo.doc -> ANOVA of whole culture chlorophyll fluorescence of all constructs, plus average and standard deviation values.</p> <p> </p> <p><strong>Consider citing our work. </strong></p> <p>Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433</p> <p> </p> <p> </p>
MobileWell100+: A Multivariate Longitudinal Mobile Dataset for Investigating Individual and Collective Well-Being
<p>This study engaged 103 participants over a period spanning from November 14 to December 16, 2021, ensuring representation across various demographic factors: 51 females, 52 males, aged 18-70, with varied annual incomes and from 17 Spanish regions. The MobileWell100+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic details, COVID-19-related inquiries, emotional, behavioral, and well-being data. Complementing this, social welfare data from external sources offers contextual insight. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent. </p> <p>The following is a non-exhaustive list of collected data:</p> <ul> <li>Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, unlocked).</li> <li>Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, symptoms and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ).</li> <li>Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) are measured.</li> <li>Data corresponding to weekly surveys prompted via the smartphone, where information on work situation, symptoms and COVID vaccination, hours of physical activity per week, questions related to physical and mental health, etc. is requested.</li> </ul> <p>For a more detailed description of the study please refer to <strong>MobileWell100+StudyDescription.pdf</strong>.</p> <p>For a more detailed description of the collected data, variables and data files please refer to <strong>MobileWell100+FilesDescription.pdf</strong>. </p>
HEartS Professional Survey: Charting the effects of COVID-19 on working patterns, income, and well-being among arts professionals in China (October 2020, August 2021)
<p>These data were collected using the HEartS Professional China survey from performing arts workers in China in October 2020 and August 2021. HEartS Professional China is an adaptation of the HEartS Professional surveys which were used in 2020-2021. All the surveys were designed as multi-strategy data collection tools with two main purposes: (1) to chart working patterns, income, sources of support, and indicators of mental and social well-being to identify trends in the effects of the lockdown at the time and (2) to explore the individual work and wellbeing experiences of performing arts professionals in their own words, to identify the subjective effects of lockdown in terms of challenges and opportunities. The survey covers six areas: 1) demographics; (2) information on illness or self-isolation related to COVID-19; (3) work profiles and income; (4) changes to work profiles and income as a result of the pandemic, as well as sources of support; (5) open-response questions about work and wellbeing experiences of lockdown including challenges and opportunities; and (6) validated measures of health, wellbeing, and social connectedness. The HEartS Professional surveys are adaptations of the HEartS Survey which charts the Health, Economic, and Social impacts of the ARTs (<a href="https://doi.org/10.5061/dryad.3r2280gdj">https://doi.org/10.5061/dryad.3r2280gdj</a>).</p>
Urban Agriculture and Farmers Well-Being In Dar Es Salaam And Greater Lomé
<p>Primary data collected on Urban Agriculture and Farmers Well-Being In Dar Es Salaam And Greater Lomé.</p>
Data for: Thirty-one years of warming and oxygen decline in Massachusetts Bay, a well-flushed non-eutrophic temperate coastal waterbody
<p><strong>This dataset consists of the primary data used in manuscript ("Thirty-one years of warming and oxygen decline in Massachusetts Bay, a well-flushed non-eutrophic temperate coastal waterbody") submitted to Journal of Geophysical Research Oceans.</strong></p> <p><strong>Description of the data and file structure</strong></p> <p>The primary data are in the file T-S-DO_1992-2022-20230907.csv, in columnar format, and the metadata, including column information, are in file ColumnAndCodeDescriptions.txt.</p> <p><strong>Sharing/Access information</strong></p> <p>The primary data were generated by the Massachusetts Water Resources Authority (MWRA) and provided in response to a data request. They are public data, available on request from MWRA by contacting <a href="mailto:sally.carroll@mwra.com" target="_blank" rel="noopener">sally.carroll@mwra.com (opens in new window)</a> or <a href="mailto:douglas.hersh@mwra.com" target="_blank" rel="noopener">douglas.hersh@mwra.com (opens in new window)</a>. The methods of data collection are detailed in the Quality Assurance Project Plan: </p> <p>Libby, P. S., Fitzpatrick, M. R., Willenberg, Z. J., Abramson Pala, S. L., Borkman, D. G., & Turner, J. T. (2024). Quality assurance project plan (QAPP) for water column monitoring 2024-2026: Tasks 4-8 and 11. Boston: Massachusetts Water Resources Authority. Report 2024-02. 73p. <a href="https://www.mwra.com/harbor/enquad/pdf/2024-02.pdf" target="_blank" rel="noopener">https://www.mwra.com/harbor/enquad/pdf/2024-02.pdf (opens in new window)</a></p> <p> </p>
MobileWell400+: A Large-Scale Multivariate Longitudinal Mobile Dataset for Investigating Individual and Collective Well-Being
<p>This study engaged 409 participants over a period spanning from July 10 to August 8, 2023, ensuring representation across various demographic factors: 221 females, 186 males, 2 non-binary, year of birth between 1951 and 2005, with varied annual incomes and from 15 Spanish regions. The MobileWell400+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic, emotional, social, behavioral, and well-being data. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent. </p> <p>The following is a non-exhaustive list of collected data:</p> <ul> <li>Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, locked, unlocked).</li> <li>Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, effects and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception.</li> <li>Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) and social interaction (quantity and quality) are measured.</li> <li>Data corresponding to weekly surveys prompted via the smartphone, where information on overall health, hours of physical activity per week, lonileness, and questions related to well-being are asked.</li> <li>Data corresponding to an final survey prompted via the smartphone, consisting of similar questions to the ones asked in the initial survey, namely psychological and well-being items (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception questions.</li> </ul> <p>For a more detailed description of the study please refer to <strong>MobileWell400+StudyDescription.pdf</strong>.</p> <p>For a more detailed description of the collected data, variables and data files please refer to <strong>MobileWell400+FilesDescription.pdf</strong>. </p>
Characterizing the Interaction of the Effects of a Museum Visit on Mobility, Cognition, and Well-being in People with Stroke
<h2><strong>Study description</strong></h2> <p>This exploratory small case study operated by Medical Physics Laboratory & Digital Innovation of School of Medicine of Aristotle University of Thessaloniki (AUTH) is a collaborative study between AUTH and McGill-CRIR-Canada, and for this reason it followed and replicated part of the protocol of the Canadian VITALISE partners described above. The aim of this particular small-case study was to investigate the interaction of the effects of a museum visit (Museum of Casts and Antiquities in Aristotle University of Thessaloniki) on mobility, cognition, well-being and the experience of individuals, as well as to investigate whether cognitively and verbally accessible audio guides of exhibits contribute to a better understanding of the descriptions of these exhibits, through measurements made using advanced technologies. In this context, the data collected aimed also to draw some conclusions regarding the feasibility and user satisfaction of conducting a range of different measurements and interventions in a museum setting.</p> <p>Piloting phase: The piloting phase began in May 2023 and was concluded at the end of the year. Prior to the museum visit, the participants were administered some questionnaires in the Thessaloniki Action for HeAlth & Wellbeing Living Lab - Thess-AHALL, aiming to obtain a holistic view on their cognitive status, as well as dimensions of their physical and mental wellbeing and quality of life. <a href="../api/records/11473970/draft/files/Questionnaires%20during%20the%20museum%20visit.xlsx/content" target="_blank" rel="noopener noreferrer">Questionnaires during the museum visit.xlsx</a></p> <p>Afterwards, the participants, one at a time, entered the museum with the members from the project’s research team, where they were asked to wear some specific equipment. In particular, they initially wore a pair of smart insoles (Digitsole Pro Smart Insoles) (<a href="../api/records/11473970/draft/files/Digitsole-pro-museum-dataset.csv/content" target="_blank" rel="noopener noreferrer">Digitsole-pro-museum-dataset.csv</a>) and a smart watch (Smartwatch-Fitbit Charge 5) <a href="../api/records/11473970/draft/files/Fitbit%20Datasets.zip/content" target="_blank" rel="noopener">Fitbit Datasets.zip</a> and were asked to walk at their own pace following a predetermined guided tour route through the museum spaces and exhibits. Breaks were provided whenever requested by the participants.<br>After completing their guided tour in the museum, they were asked to wear eye-tracking glasses (Pupil Labs) <a href="../api/records/11473970/draft/files/Pupils-dataset.zip/content" target="_blank" rel="noopener noreferrer">Pupils-dataset.zip</a> and headphones to listen to audio descriptions (audio guides) of three selected exhibits, the Tombstone, the Hermes of Praxiteles and the West Pediment from the temple of Zeus at Olympia. Specifically, they were asked to stand in front of three (3) exhibits and listen to the description. For each of the three exhibits, participants first listened to their original description (a description that someone can usually find next to the exhibit or in a relevant educational textbook) and then they listened to the modified, accessible version of the exhibit description (with simpler words, shorter sentences etc.). After each audio description (original and modified), they were asked to answer a number of predetermined questions related to the audio description they had just heard.</p> <p>At the end, after the aforementioned equipment was removed, they were asked to answer a number of questions and questionnaires about their overall museum experience and potential challenges or stress experienced (Satisfaction and feasibility questionnaire, Visual Analog Stress scale, State-Trait Anxiety Inventory) <a href="../api/records/11473970/draft/files/Analysis%20feasibility%20&%20satisfaction%20questionnaire.xlsx/content" target="_blank" rel="noopener noreferrer">Analysis feasibility & satisfaction questionnaire.xlsx</a>.</p>
Investigation of Oil Well Blowouts Triggered by Wastewater Injection in the Permian Basin, USA.
<p>This dataset is a part of research work titled: "Investigation of Oil Well Blowouts Triggered by Wastewater Injection in the Permian Basin, USA."</p> <p>Authors: <br>Vamshi Karanam, Zhong Lu, Jin-Woo Kim, Roger P Denlinger</p> <p><br>The folder contains six datasets. They are explained in detail below.</p> <p>1. Deformation rate map<br>filename: deformation_rate.geojson<br>The data can be accessed using QGIS, ArcGIS or other GIS software<br>The attribute table contains:<br>field_0: point number<br>field_1:Code<br>field_2:height<br>field_3: height standard deviation<br>field_4:deformation rate (mm/yr)<br>field_5:standard deviation of deformation rate<br>field_6: Coherence<br>field_7: effective area</p> <p><br>2. Monthly njection volumes<br>filename: injection_volumes.geojson<br>The data can be accessed using QGIS, ArcGIS or other GIS software<br>The attributes table contains:<br>field_0: API Number<br>field_1 to field_150: Monthly injection volumes from 20100101 to 20220601 (m^3)<br>field_151: Longitude<br>field_152 : Latitude</p> <p><br>3. Depth to the top of formations<br>filename: main_formations_new.geojson<br>The data can be accessed using QGIS, ArcGIS or other GIS software<br>The attribute table contains:<br>field_0: API Number<br>field_1 to field_12: Depth to top of different formations (m)<br>field_13: True depth of the well (m)<br>field_14: Latitude<br>field_15: Longitude</p> <p><br>4. Blowout Modeling results<br>This dataset contains six files as follows:<br>i. blowout_bestfit_alpha.mat: best fit parameters for the Modeling of sill alpha. <br>ii. blowout_bestfit_alpha_summary.mat: summary of the best fit parameters for the Modeling of sill alpha<br>iii. blowout_bestfit_beta.mat: best fit parameters for the Modeling of sill beta<br>iv. blowout_bestfit_beta_summary.mat: summary of the best fit parameters for the Modeling of sill beta<br>v. blowout_bestfit_results.csv: Best fit results of the penny crack modeling. The csv file contains five columns: Longitude, Latitude, Observed deformation, Modeled deformation and Residual<br>vi. blowout_input_data.mat: Input data for the modeling of blowout. The mat file contains five files. InSAR Phase (Phase), Incidence angle (Inc), Heading angle (Heading), Longitude (Lon), Latitude (Lat)</p> <p><br>5. Cumulative uplift Modeling results<br>This dataset contains six files as follows:<br>i. uplift_bestfit_alpha.mat: best fit parameters for the Modeling of sill alpha. <br>ii. uplift_bestfit_alpha_summary.mat: summary of the best fit parameters for the Modeling of sill alpha<br>iii. uplift_bestfit_beta.mat: best fit parameters for the Modeling of sill beta<br>iv. uplift_bestfit_beta_summary.mat: summary of the best fit parameters for the Modeling of sill beta<br>v. uplift_bestfit_results.csv: Best fit results of the penny crack modeling. The csv file contains five columns: Longitude, Latitude, Observed deformation, Modeled deformation and Residual<br>vi. uplift_input_data.mat: Input data for the modeling of cumulative uplift. The mat file contains five files. InSAR Phase (Phase), Incidence angle (Inc), Heading angle (Heading), Longitude (Lon), Latitude (Lat)</p> <p>6. Metadata for the Sentinel-1 datasets used in this study</p> <p>i. sentinel_1_datasets_descending.csv: CSV file with filenames, date of acquisition and other metadata along with URL to access the datasets in descending geometry</p> <p>ii. sentinel_1_datasets_ascending.csv: CSV file with filenames, date of acquisition and other metadata along with URL to access the datasets in ascending geometry</p>
Supplemental Table. Historical cohort of 560 well-described papillary craniopharyngiomas reported in the medical literature (560c).
<p><span>Table </span><span>listing the historical cohort of </span><span>560 </span><span>well-described/illustrated individual papillary craniopharyngiomas reported in the medical literature, including their corresponding references.</span><span><span><span> </span></span></span></p>
Participant responses - Enhancing Workplace Well-being through Digital Solutions
Open the record for dataset details and reuse information.
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.