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Sri Ksetra, Myanmar. Cross-section of the Excavations at the Yahanda Mound (2014-16) HMA.59 (test pit 8 & 9)
<p>Janice Stargardt, Sri Ksetra, Myanmar. Cross-section of the Excavations at the Yahanda Mound (2014-16) HMA.59 (test pit 8 & 9) carried out under <a href="https://cordis.europa.eu/project/id/609823">ERC synergy grant 609823.</a> </p>
Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study
<p>This section highlights the various methods used for this study. It covered study setting, study design, study approach, study population, sampling techniques, sample size calculation, inclusion and exclusion criteria, ethical consideration, data collection, data management and data analysis<strong>. </strong></p> <p> </p> <p><strong>Study Setting</strong></p> <p>The study was carried out at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi between February to August, 2022. The study covered all the six (6) Colleges in the University.</p> <p> </p> <p><strong>Study Design</strong></p> <p>This was a cross sectional study to ascertain health check practices among university lecturers.</p> <p> </p> <p><strong>Study Approach</strong></p> <p>The study employed quantitative approach in which data was collected using questionnaires with both closed- and open-ended questions.</p> <p><strong>Study Population</strong></p> <p>The study population involved 838 Lecturers across the six Colleges at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi. A study of the lecturer population per college revealed that Colleges of Health Sciences (highest) and Agric /Natural resources (lowest) were the outliers (Quality Assurance and Planning Office, 2020).</p> <p> </p> <p><strong>Sampling Technique </strong></p> <p> </p> <p>Simple probability technique was used to select the name of a college and the day/date to visit. Two sets of papers were folded with names of colleges (set 1) and day/date of visit (set 2). A picker picked one folded paper from each set and the name of the college and the day/date to visit was matched. In this case, the ordering of date and visit gave 1<sup>st</sup> College of Humanities & Social Sciences, 2<sup>nd</sup> College of Agric and Natural Resources, 3<sup>rd</sup> College of Art & Built Environment, 4<sup>th</sup> College of Engineering, 5<sup>th</sup> College of Science and 6<sup>th</sup> College of Health Sciences. We then used the ‘walk in’’ system to select the study participants. Within the days to visit a college, any lecturer we meet in his/ her office was a potential study participant.</p> <p> </p> <p> </p> <p> </p> <p><strong>Sample Size Calculation</strong></p> <p>The sample size was obtained using Yamane, 1967 formulae as shown below:</p> <p> </p> <p> </p> <p>Where n= is the population of Lecturers in at KNUST</p> <p>E= is the level of precision</p> <p>Therefore: n= 838</p> <p> 1+838 (0.0025)</p> <p>n = 838</p> <p>1+ 2.098</p> <p> </p> <p>838</p> <p>3.095</p> <p> </p> <p> n=270 </p> <p>However, due to logistical constrains, 205 participants were contacted across the 6 Colleges at Kwame Nkrumah University of Science and Technology. We then applied simple proportions to get the number of lecturers to be consulted in each college.</p> <p> </p> <p><strong>Inclusion and Exclusions Criteria</strong></p> <p>Inclusion criteria was made up of all Lecturers on KNUST campus who are in active service and consented to participate. All other staff not within this category were excluded from this research.</p> <p> </p> <p><strong>Ethical Considerations</strong></p> <p>Ethical approval was sought from the CHRPE, KNUST with approval reference no: CHRPE/AP/581/21. The aim of the research was explained to participants. Those who consented to participate in the research were given consent forms to sign and date. Again, participants were assured of confidentiality. Participants were told that, they were free to withdraw from the study in the cause of time. In other words, study participants were not coerced into the study.</p> <p> </p> <p><strong>Data Collection Tool</strong></p> <p>Data was collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues on physical inactivity and tobacco use. Aside these four main risk factors of NCDs, the questionnaire also captured frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-up, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,</p> <p> </p> <p><strong>Data Management</strong></p> <p>Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.</p> <p> </p> <p><strong>Data Analysis</strong></p> <p>Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ<sup>2</sup>) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).</p> <p> </p> <p><strong>Variables</strong></p> <p>BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank and lecturer’s colleges (categorized into binary variable; COHS /COS/COE and CABE/CANR/COHSS), family history of NCDs and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).</p> <p> </p> <p> </p>
Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study
<p><strong>Data Collection Tool</strong></p> <p>Data were collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues with physical inactivity, and tobacco use. Aside from these four main risk factors of NCDs, the questionnaire also captured the frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-ups, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of the job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,</p> <p> </p> <p><strong>Data Management</strong></p> <p>Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.</p> <p> </p> <p><strong>Data Analysis</strong></p> <p>Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ<sup>2</sup>) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges' effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).</p> <p> </p> <p><strong>Variables</strong></p> <p>BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank, and lecturer’s colleges (categorized into binary variables; Colleges, family history of NCDs, and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).</p> <p> </p>
Diagnostic strategies for muscular dystrophies: a Cross-Sectional Study
<p>Datos obtenido producto de un estudio de corte transversal con el fin de establecer la prevalencia de base hospitalaria en distrofias musculares, a través de un diseño de muestreo en fases.</p>
Symptoms in health care workers during the COVID-19 epidemic. A cross-sectional survey.
<p>data collected during the COVID-19 epidemics on workers of the Health Care Unit Roma4, Civitavecchia. Paper submitted.</p>
The Psychological Burden of the COVID-19 Pandemic and Its Associated Factors among the Frontline Doctors of Bangladesh: A Cross-sectional Study-Extended Data
<p>Using this document, we tried to assess the mental health status of the frontline doctors of Bangladesh during Coronavirus 2019 pandemic.</p>
Real and simulated cross-sectional and longitudinal images of hair
<p>This is the dataset containing simulated and real data used in the analyses for the paper "<em><a href="https://www.biorxiv.org/content/10.1101/2020.11.24.392191v1">High-throughput phenotyping methods for quantifying hair fiber morphology</a>" </em>and is part of the<em> <a href="https://tinalasisi.github.io/2020_HairPheno_manuscript/index.html">Hair Phenotyping Methods Project</a> </em>run by <a href="https://www.tinalasisi.com/">Tina Lasisi</a>. </p> <p>The data can be analyzed with the <em>fibermorph</em> Python package available on <a href="https://pypi.org/project/fibermorph/">PyPi</a> and <a href="https://github.com/tinalasisi/fibermorph">Github</a>. </p> <p>This repository has 2 datasets with 2 different types of data:</p> <ol> <li>Simulated hair data <ol> <li>Cross-sectional data (simulated ellipses)</li> <li>Curvature data (simulated arcs)</li> </ol> </li> <li>Real hair data <ol> <li>Cross-sectional data (micrographs of hair fiber cross-sections)</li> <li>Curvature data (longitudinal images of hair fiber fragments)</li> </ol> </li> </ol> <p>Visit the <a href="https://tinalasisi.github.io/2020_HairPheno_manuscript/index.html"><em>Hair Phenotyping Methods Project</em></a> website for the most up to date information about this project and any updates relevant to this dataset.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Details</strong></p> <p><strong>Simulated data</strong></p> <ol> <li>Cross-sectional data <ul> <li>These ellipses were simulated with a python script developed as part of <a href="https://github.com/tinalasisi/fibermorph/"><em>fibemorph</em></a>. A version of that code that doesn't require the original Python package has been made available with the dataset (sim_ellipse.py).</li> <li>The script simulates a single cross-section per image.</li> <li>Each image has a width of 5200px and a height of 3900 with a resolution set to 4.25 px/micron.</li> </ul> </li> <li>Curvature data <ul> <li>An R script used for curvature simulation, written by <a href="https://www.arslanzaidi.com/">Arslan Zaidi,</a> has also been made available with this dataset (sim_curvature.R). </li> <li>The script generates 25 arcs per image. We used a set length of 1.57.</li> <li>Each image has a resolution of 132 px/mm. </li> </ul> </li> </ol> <p>Please note that due to the use of random generations, it is not possible to recreate the exact same datasets that are saved here. </p> <p><strong>Real data</strong></p> <p>The real data images are very large files and have been split into multiple zip files. Please check the specific instructions for unzipping split zip files for your OS. </p> <p>The images are from hair samples collected by the <a href="https://shriverlab.psu.edu/">Shriver Lab</a> at Penn State. There were a total of 192 samples, although not all images made it past quality control so certain IDs may have cross-section images but not curvature images or vice versa. </p> <p>The images have been de-identified and the hair samples for these individuals were collected with informed consent and ethical approval by The Pennsylvania State University Institutional Review Board (#44929 and #45727).</p> <ol> <li>Cross-sectional data <ol> <li>We developed and used <a href="https://www.protocols.io/view/sample-preparation-protocol-for-cross-sectional-mi-bbwcipaw">this protocol</a> to embed, section, and image the hairs.</li> <li>We embedded 6 samples per person and took images of both sides of the sectioned sample (A and B). These should be mirror images of each other.</li> </ol> </li> <li>Curvature data <ol> <li>We developed and used <a href="https://www.protocols.io/view/sample-preparation-protocol-for-hair-fiber-curvatu-bbweipbe">this protocol</a> to cut, wash, and image the hairs.</li> <li>We used 3-5 hairs per person where available. A number of samples did not have enough hair for this, so the images contain fewer fragments. We have made these available for full transparency although we filtered them from our analyses downstream.</li> </ol> </li> </ol> <p>Please see the <a href="https://github.com/tinalasisi/2020_HairPheno_manuscript">GitHub repository</a> for additional related participant data we used in our analyses.</p> <p> </p> <p> </p>
Data archive for the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter""
<p>Data archive for figures accompanying the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter". In 2020 this article was accepted for publication in the journal <em>Atmospheric Chemistry and Physics</em>. Data are uploaded in the form of Igor Pro experiment files (.pxp).</p>
EBSD datasets for cross-sectioned structural steel hardness indentations - Adaptive Domain Misorientation
<p>Open access datasets for structural steel hardness indentations from the following publication: Ultramicroscopy 2021, Volume 222: <a href="https://doi.org/10.1016/j.ultramic.2021.113203">https://doi.org/10.1016/j.ultramic.2021.113203</a></p> <p>Files included:</p> <ul> <li>Adaptive domain misorientation calculated for Indentation 1 and 2 using misorientation thresholds (Delta theta) 0.5deg and 2deg, corresponding to dense dislocation walls and sub-grain boundaries</li> <li>Indentation 2: Raw dataset and associated mask file for excluding the edge of the data</li> </ul> <p>The methodology for analysing and plotting of the data is found at: <a href="https://doi.org/10.5281/zenodo.4430623">https://doi.org/10.5281/zenodo.4430623</a></p> <p>For further information visit: Aalto University Wiki - <a href="https://wiki.aalto.fi/display/EMDIDS">https://wiki.aalto.fi/display/EMDIDS</a></p>
Dataset (CryoSat-2 altimetry data over Brahmaputra River, river masks, model cross sections) used in Schneider et al., 2017. doi:10.5194/hess-2016-243
<p>Dataset used in</p> <p>Schneider, R., Nygaard Godiksen, P., Villadsen, H., Madsen, H., Bauer-Gottwein, P., 2017. Application of CryoSat-2 altimetry data for river analysis and modelling. Hydrol. Earth Syst. Sci.rticle. doi:10.5194/hess-2016-243</p> <p>The dataset contains</p> <ul> <li>CryoSat-2 satellite altimetry data over the Brahmaputra River from 2010 to 2013</li> <li>River masks, derived from Landsat NDVI imagery, used to filter the CryoSat-2 data</li> <li>Results from the cross section calibration described in Schneider et al., 2017</li> </ul> <p>All data is provided as a .zip file which includes a README.txt with more details on the data.</p> <p> </p>
Fig. 111. Dorsoventral cross section through a in Scaphites Of The ''Nodosus Group'' From The Upper Cretaceous (Campanian) Of The Western Interior Of North America
Fig. 111. Dorsoventral cross section through a small microconch of Hoploscaphites brevis (Meek, 1876), transitional in morphology to macroconch, BHI 4248, Baculites compressus–B. cuneatus zones, Pierre Shale, Meade County, South Dakota. The plane of the cross section approximately coincides with the line of maximum length. A. Camera lucida of cross section of the adult shell. The stippled area demarcates the mature body chamber. B. Camera lucida of cross section of the inner whorls. C. Photo of cross section of the inner whorls. Measurements are listed in appendix 2, table 4.
Fig. 73. Dorsoventral cross section through a in Scaphites Of The ''Nodosus Group'' From The Upper Cretaceous (Campanian) Of The Western Interior Of North America
Fig. 73. Dorsoventral cross section through a robust microconch of Hoploscaphites nodosus (Owen, 1852), USNM 536246, USGS Mesozoic loc. 9133, Pierre Shale, near Kremmling, Grand County, Colorado. The plane of the cross section approximately coincides with the line of maximum length. A. Camera lucida of cross section of the adult shell. The stippled area demarcates the mature body chamber. B. Camera lucida of cross section of the inner whorls. C. Photo of cross section of the inner whorls. Measurements are listed in appendix 2, table 3.
Fig. 112. Dorsoventral cross section through a in Scaphites Of The ''Nodosus Group'' From The Upper Cretaceous (Campanian) Of The Western Interior Of North America
Fig. 112. Dorsoventral cross section through a small microconch of Hoploscaphites brevis (Meek, 1876), AMNH 58555, Pierre Shale, Meade or Pennington County, South Dakota. The plane of the cross section approximately coincides with the line of maximum length. A. Camera lucida of cross section of the adult shell. The stippled area demarcates the mature body chamber. B. Camera lucida of cross section of the inner whorls. C. Photo of cross section of the inner whorls. Measurements are listed in appendix 2, table 6.
Fig. 72. Dorsoventral cross section through a in Scaphites Of The ''Nodosus Group'' From The Upper Cretaceous (Campanian) Of The Western Interior Of North America
Fig. 72. Dorsoventral cross section through a slender macroconch of Hoploscaphites nodosus (Owen, 1852), AMNH 9520/2, Pierre Shale, Sage Creek, Pennington County, South Dakota. The plane of the cross section approximately coincides with the line of maximum length. A. Camera lucida of cross section of the adult shell. The stippled area demarcates the mature body chamber. B. Camera lucida of cross section of the inner whorls. C. Photo of cross section of the inner whorls. Measurements are listed in appendix 2, table 1.
Methodology for measuring photonuclear reaction cross sections with an electron accelerator based on Bayesian analysis
<p>Measurement data, simulation data and code from the manuscript Braccini et al. "Methodology for measuring photonuclear reaction cross sections with an electron accelerator based on Bayesian analysis" </p> <p>ArXiv preprint arXiv:2309.11270 [nucl-ex] at https://doi.org/10.48550/arXiv.2309.1127</p>
Predictors of medical staff's knowledge, attitudes, and behavior of dysphagia assessment: A cross-sectional study
<p>This study aimed to develop training resources and standardize the assessment of dysphagia in patients with stroke. This study was a cross-sectional study. A total of 430 nurses and doctors from four provinces(Guangdong Province, Hunan Province, Guangxi Province, and Shaanxi Province) who were selected by convenience sampling were invited to complete the questionnaire through WeChat, DingTalk, and Tencent QQ from May 23 to 31, 2022. A self-reported questionnaire was used to assess participants' Knowledge, Attitude, and Behavior regarding dysphagia. Participants' sociodemographic, training, and nursing experience were measured using the general information sheet and assessed as potential predictors of medical staff's Knowledge, Attitudes, and Behavior of dysphagia assessment. A multiple linear regression model was used to identify the factors predicting medical staff's Knowledge, Attitudes, and Behavior regarding dysphagia assessment. The mean scores for Knowledge, Attitudes, and Behavior of dysphagia assessments were 92.654(SD 17.519). Multiple linear regression results indicated that experience in dysphagia patients' nursing, related training for dysphagia, working years in the field of dysphagia-related diseases, specialized training in geriatric, swallowing & rehabilitation, and department related to neurology, rehabilitation & elderly were significant predictors, accounting for 35.1% of the variance in scores of medical staff's Knowledge, Attitudes and Behavior of dysphagia assessment. Our findings imply that nursing experience, training, and work for patients with swallowing disorders could have positive effects on the Knowledge, Attitudes, and Behavior of medical staff regarding dysphagia assessment. Hospital administrators should provide relevant resources, such as videos of dysphagia assessment, training centers for the assessment of dysphagia, and swallowing specialist nurses. It is important that health policies fully recognize the role of training and support systems in caring for people with dysphagia.</p>
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore. in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera
→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore.
Data for "Impact of early cleft lip and palate surgery on maxillary growth in 5- and 10-Year-old patients with unilateral cleft lip and palate: a cross-sectional study"
<p>Relative frequency in % (absolute frequency is shown above each bar). Frequency of 5YO indices in cleft patients and frequency of GOSLON indices in cleft patients.</p>
FIG. 5. — Serial cross sections from below upwards through a in Comparative floral anatomy of some species of Brassicaceae and its taxonomic significance
FIG. 5. — Serial cross sections from below upwards through a floral bud of Matthiola incana (L.) R.Br. showing: A, pedicel vasculature; continuous siphonostele; B-F, calyx vasculature; two sepal median bundles emerge directly from central stele and two from sepal-median-nectarial complexes; D-G, corolla vasculature; from petal-sepal marginal complexs; D-H, androecium vasculature; six staminal bundles to six fertile stamens emerge directly from the central stele; I-L, gynoecium vasculature, eight vascular masses; two dorsal carpellary bundles, two ventral carpellary masses, four lateral carpellary bundles. Scale bar: 500 μm.
FIG. 3. — Serial cross sections from below upwards through a in Comparative floral anatomy of some species of Brassicaceae and its taxonomic significance
FIG. 3. — Serial cross sections from below upwards through a floral bud of Coronopus didymus (L.) Sm. showing: A, pedicel vasculature; dissected siphonostele; B-D, calyx vasculature; four sepal median bundles arise directly from the central stele as distinct sepal median traces without ramification; C-E, corolla vasculature; four petal vascular bundles protrude from petal-nectarial complexes; C-G, androecium vasculature; two staminal vascular bundles to two fertile stamens emerge directly from the central stele; F-K, gynoecium vasculature; six vascular masses, two dorsal carpellary bundles, two ventral carpellary masses, two septal bundles. Scale bar: 60 μm.
ScienceDex guides
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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.