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CoCryM project - Datasets - 05
<p>Source datasets for CoCryM project, more info on: https://sites.google.com/view/makhansary/publications </p>
Summer Rainfall Scenarios and Climate Change Factor Projections over Wanzhou County, China
<p>This dataset consists of rainfall scenarios and ensemble projections of extreme daily rainfall and mean summer season rainfall over Wanzhou County, China.</p> <p><strong>Precipitation Reference Period (1979-2018)</strong></p> <p>The reference scenario rainfall covers the period of 1979-2018, and is derived from the China Meteorological Forcing Dataset (https://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/). The extreme daily rainfall (in mm/day) is derived from Gumbel distributions fitted to monthly maximum daily rainfall covering the months of June to August. A spatial distribution of return periods from 2, 5, 10 20, 50 and 100 years for this scenario were derived and included in this dataset. The mean seasonal rainfall scenario covers the average daily rainfall (in mm/day) for the months of May to July to represent antecedent rainfall conditions of that could trigger shallow landslides during the summer season.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.1 degrees x 0.1 degrees</li> <li>Time period: 1979-2018</li> <li>Data Format: .csv files (.xyz file extensions)</li> <li>Variable: Rainfall (pr)</li> <li>Units: mm/day </li> </ul> <p><strong>Ensemble Projections and Climate Change Factors</strong></p> <p>The ensemble climate change projections cover two periods: Mid-21st Century (2021-2060) and Late-21st Century (2061-2100). The influence of climate change is assessed through climate change factors that represent a multiplicative factor of change between present and future climate model outputs. The ensemble projections are the mean climate change factor derived from four bias-corrected Regional Climate Model outputs. The ensemble consisted of the results REMO2015 and RegCM4 models that dynamically downscaled HadGEM2-ES, MPI-ESM-ML, and MPI-ESM-MR model outputs (https://esgf-data.dkrz.de/search/cordex-dkrz/). The bias correction was performed using the quantile delta method. An empirical transfer function for daily rainfall was used to derive the mean seasonal rainfall scenario, while a parametric (Gumbel distribution) transfer function was used to derive on the monthly maxima for the extreme daily rainfall scenarios.</p> <ul> <li>Spatial extent: Wanzhou County, China</li> <li>Spatial Resolution: 0.22 degrees x 0.22 degrees</li> <li>Time periods: Mid-21st Century (2021-2060) & Late-21st Century (2061-2100)</li> <li>Data Format: .csv files</li> <li>Variable: Climate Change Factor (ccf)</li> <li>Unit: Dimensionless</li> <li>Included ensemble projection statistics: <ul> <li>Standard deviation (sd)</li> <li>Coefficient of Variation (cv)</li> </ul> </li> </ul>
Project files provided as supporting information to the manuscript "Making sense of complex systems through resolution, relevance, and mapping entropy"
<p>README file to the project files provided as supporting information to the manuscript “Making sense of complex systems through resolution, relevance, and mapping entropy”</p> <p>Feb. 25, 2022</p> <p>Authors: Roi Holtzman, Marco Giulini and Raffaello Potestio</p> <p>==================================</p> <p>The dataset contains the following files:</p> <p>- A README file with the description of the pymap program for describing how different selections of *N* out of *n* degrees of freedom (mappings) affect the amount of information retained about a full data set.<br> - The pymap.py program<br> - The pymap.yml support file<br> - The data.tar tarball with the setup data<br> - The results.tar tarball with the output data<br> ===</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based bias adjusted potential evapotranspiration
<p>Potential evapotranspiration calculated from the UKCP18 RCM ensemble using the Penman-Monteith method as implemented by Robinson et al. (2017) and bias adjusted using Lange et al. (2019). This dataset was used for analysis of future drought characteristics in Reyniers et al. (2022). Details on the bias adjustment of this potential evapotranspiration dataset, as well as bias-adjusted precipitation and temperature from the same climate model ensemble, can be found in Reyniers et al. (2025).</p> <p>---</p> <p>Reyniers, N., Osborn, T. J., Addor, N., and Darch, G.: Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index, Hydrol. Earth Syst. Sci., 27, 1151–1171, https://doi.org/10.5194/hess-27-1151-2023, 2023.</p> <p>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025. </p> <p>Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J., Rudd, A. C. (2017). Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. HESS, <em>21</em>(2), 1189-1224.</p> <p>Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). <em>GMD,</em> <em>12</em>(7), 3055-3070.</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based SPI and SPEI data
<p>Standardised Precipitation Index (SPI; McKee et al., 1993) and Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2009) computed from UKCP18 Strand 3 simulations (Met Office Hadley Centre, 2018).</p> <p>This data was produced for the study by Reyniers et al. (<em>in prep</em>) analysing (diferences in) drought projections using these indicators. The methodology used to produce this data can be found there if/when the paper is accepted, however do not hesitate to reach out with any further questions. Please note the RCM data was bias adjusted prior to SPI and SPEI computation. There is one file per ensemble member containing the full simulated period on a monthly time step, using aggregation periods of 1, 3, 6, 12, 24 and 36 months for the computation of SP(E)I.</p> <p><strong>References</strong></p> <p>McKee, T. B., Doesken, N. J., Kleist, J., et al.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, pp. 179–183, Boston, 1993</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. Centre for Environmental Data Analysis, <em>date of citation</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Reyniers, N., Osborn, T. J., Addor, N., Darch, G.: Projected changes in droughts and extreme droughts in Great<br> Britain are strongly influenced by the choice of drought index. Hydrology and Earth System Sciences, <em>in prep. for HESS</em></p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2009.</p>
Raw survey data for the EU H2020 Blue Growth Farm project
<p>Raw survey data for four surveys completed in two locations (Isle of Islay - Scotland, and Reggio Calabria - Italy), two years apart (2019 and 2021) for the EU H2020 Blue Growth Farm project. </p> <p>The main questions were designed to collect opinions on each of the component parts of an Multifunctional Offshore Installation (Offshore Wind Energy and Fish Farming), before asking about opinions on the integrated platform and its location, with the aim to understand the local context for Social License to Operate. </p>
GitHub project dataset for license analysis
<p>This dataset consists of a number of GitHub repositories that cover the following programming languages: PHP, Java, JavaScript, C, C++, C#, Python, Visual Basic. The repositories were used by license extraction tools, i.e. FOSSology Nomos, Ninka, to see which open source software licenses exist in the source code. They were also used to perform analysis on the README.md file and discover licenses used in libraries as described in the README.</p>
Results of Rock Magnetic and Mineralogical Analyses of Ultramafic Cores from the Oman Drilling Project
<p>This dataset comprises of original rock magnetic data, microscope images, EPMA data, and EDS reports used for analysis of serpentinized ultramafic cores drilled during the Oman Drilling Project (OmanDP).</p> <p>Rock magnetic experiments have been performed onboard D/V Chikyu and in a shielded facility located at the University of Iceland. Results included in the dataset are as follows: Mass susceptibility, Natural remanence before and after alternating field (AF) and thermal demagnetization, and thermomagnetic measurements. </p> <p>Microscopic images and EDS reports are the results of backscatter electron microscopy that was held at Seoul National University with a JEOL JSM-7100F scanning electron microscope. EPMA results were produced using the JXA-8530F electron probe micro-analyzer at the National Center for Inter-University Research Facilities (NCIRF) of Seoul National University (SNU).</p> <p> </p>
GERONTE H2020 project - GERDAT001 - Core multimorbidity dataset
<p><strong>The present document is a dataset generated as part of Deliverable D1.1. of the GERONTE project, which has received funding from the European Union’s Horizon 2020 Programme under Grant Agreement N°945218. It aims to provide the geriatric oncology professional community with a dataset of core comorbidity data to be included and assessed in the evaluation of older patients with cancer and multimorbidity. </strong></p> <p>GERONTE is a 5-year research and innovation project (April 2021 to Mars 2026) funded by the European Union within the framework of the H2020 Research and Innovation programme, in response to the health societal challenge topic SC1-BHC-24-2020 “Healthcare interventions for the management of the elderly multimorbid patient”. The overall aim of GERONTE is to improve quality of life - defined as well-being on three levels: global health status, physical functioning and social functioning- for older multimorbid patients, while reducing overall costs of care. To this end, GERONTE will co-design, test, and prepare for deployment an innovative cost-effective patient-centred holistic health management system, hereafter referred to as the GERONTE intervention. GERONTE intervention will rely on an ICT based application for real-time collection and integration of standardised clinical and home patient-reported data. GERONTE intervention will be demonstrated in the context of care of multimorbid patients having cancer as a dominant morbidity, and be adaptable to any other combination of morbidities.</p> <p>An important component of the GerOnTe care pathway was to determine which comorbidity information is need for optimizing treatment decision making and the subsequent care trajectory. We showed a list of comorbidities from literature and subsequently asked an expert panel to determine which of these were relevant for oncologic decision making and care. As the experts also stated that additional information on the severity of a comorbid condition is needed to know if it impacts an oncologic decision or a treatment trajectory. Therefore, we asked the experts to state for the sixteen somatic/psychiatric comorbidities considered important in the previous round, whether or not the presence itself is sufficient information or if they needed extra information quantifying the severity; if so, we also asked which information.</p> <p>This led to the composition of the dataset presented here.</p> <p> </p>
Bibles metadata from the BiblIndex project
<p>This data set contains the bibliographical references to the Bibles used by the BiblIndex project (https://biblindex.org). A csv table was extracted from the MySQL underlying database (5.5.5-10.3.31-MariaDB-0+deb10u1).</p>
Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory
<p><strong>Summary</strong></p><p>This dataset is a collection of X-ray projection images of 23 mandarins moving over a circular trajectory in such a way that the projections of multiple adjacent mandarins overlap. The dataset was acquired to test out Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT), about which a paper is published in IEEE Transactions on Computational Imaging [Schut 2022].</p><p> </p><p><strong>Description</strong></p><p><i>Sample information</i></p><p>The samples are 23 mandarins. The first 10 are of the Nadorcott cultivar, and the remaining 13 are of the Clemenrubi cultivar. The diameter of the mandarins ranges between 50 and 58 mm. Per sample metadata can be found in the mandarin_metadata.csv file.</p><p><i>Scanner information</i></p><p>The dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].</p><p><i>Scanning geometry</i></p><p>The mandarins were moved according to a custom scanning protocol, with the intention to simulate a conveyor belt setup. A wooden disk was attached on top of the rotation stage and six evenly spaced object positions were marked on the disk at a fixed distance from the center of rotation. Pieces of cardboard tube were used as sample holders to make sure the mandarins wouldn't roll as the disk would rotate and to raise them from the disk without attenuating too much of the X-ray signal. The rotation stage was positioned in such a way that over a full rotation of the disk, each mandarin would be completely in view of the detector for more than 180 degrees of the rotation, while there would also be a position at which it would be completely out of view. An image illustrating the exact dimensions is included in mandarin_carousel_dimensions.png.</p><p>The scan was performed in phases. Every phase 400 projection images were acquired, while rotating the disk for 60 degrees. This would rotate one of the positions out of view of the scanning setup. Before the first 6 phases a mandarin was added on the position that was out of view of the setup. For the phases after that the position that would be out of view would contain a mandarin that had rotated the full circle so that mandarin was replaced with a new mandarin. At the last 6 phases there would be no new mandarins left to add so the mandarin that was out of view of the setup would only be removed. The projection images acquired from each phase were concatenated resulting in a dataset of 11200 projections. At most 5 mandarins were in view at a given time.</p><p>Note: Due to a small oversight while scanning, the 19th mandarin is not included on projections 9200-9205. This area can be masked out during reconstruction.</p><p><i>Scanning settings</i></p><p>A peak voltage of 90kV was used, the target power was set to 49.5W and the spectrum was pre-filtered using 0.1mm of copper. An exposure time of 200 ms was used for each projection. A start-stop acquisition scheme was used to minimize vibrations and to make adding and removing mandarins easier: After each projection image was acquired, the stage was rotated to a new position and the scanner was paused for 200 ms before acquiring the next projection image. Darkfield and flatfield images were acquired before and after all the mandarins were scanned using the average over 200 images. 2x2 pixel hardware binning was used and all images were cropped to a 500 pixel high region around the center, resulting in 11200 projection images of 956x500 pixels (11.1GB uncompressed). All images are stored in .tif format.</p><p><i>Reconstructing volumes</i></p><p>The repository <a href="https://github.com/D1rk123/top-ct_experiments">https://github.com/D1rk123/top-ct_experiments</a> contains code for TOP-CT simulations and reconstructions. The script mandarin_carousel_experiment.py was specifically written to reconstruct volumes for each separate mandarin from this dataset.</p><p> </p><p><strong>Research group</strong><br>These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p><p><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p><p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.</p><p><strong>References</strong></p><p>[Schut 2022] D. E. Schut, K. J. Batenburg, R. van Liere, and T. van Leeuwen, "TOP-CT: Trajectory with Overlapping Projections X-ray Computed Tomography", 2022, IEEE Transactions on Computational Imaging<br>[Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, "Explorative imaging and its implementation at the FleX-ray Laboratory," J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p><p>If you use (parts of) this data in a publication, please consider citing the first article.</p>
Nivito Project
<p>Ieškote maišytuvo, kuris jūsų virtuvei suteiks elegancijos? Ieškokite „<a href="http://nivito.lt/">Nivito</a>“ virtuvės maišytuvo. Šis dailiai pagamintas maišytuvas puikiai tiks tiems, kurie savo namuose nori prabangos. Aptakaus dizaino ir nuostabios apdailos šis maišytuvas tikrai paliks įspūdį. Taigi kodėl gi nepatobulinus savo virtuvės „Nivito“ virtuvės maišytuvu? Jūs nenusivilsite.</p>
IPCC AR6 Sea Level Projections
<p><strong>Description</strong></p> <p>This data set contains the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. It contains the full set of samples for the global projections (under ar6.zip), as well as summary relative sea level projections (under ar6-regional-confidence.zip and, without the AR6 estimate of background sea level process rates, ar6-regional_novlm-confidence.zip). Most users will want to focus on the confidence_output_files, which correspond most directly to the figures and tables in the report. For the global projections, samples from the individual probability distributions described in AR6 WG1 9.6.3 are in the full_sample* directories.</p> <p>Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at <a href="https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool">https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool</a>.</p> <p>See <a href="../communities/ipcc-ar6-sea-level-projections">https://zenodo.org/communities/ipcc-ar6-sea-level-projections</a> for additional related data sets.</p> <p>See <a href="https://github.com/Rutgers-ESSP/IPCC-AR6-Sea-Level-Projections">https://github.com/Rutgers-ESSP/IPCC-AR6-Sea-Level-Projections</a> for a guide to available resources.</p> <p><strong>Required Acknowledgements and Citation </strong></p> <p>In order to document the impact of these sea-level rise projections, users of the projections are obligated to cite chapter 9 of Working Group 1 contribution to the the IPCC Sixth Assessment Report, the Framework for Assessment of Changes To Sea-level (FACTS) model description paper, and the version of the data set used:</p> <ul> <li>Fox-Kemper, B., H.T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S.S. Drijfhout, T.L. Edwards, N.R. Golledge, M. Hemer, R.E. Kopp, G. Krinner, A. Mix, D. Notz, S. Nowicki, I.S. Nurhati, L. Ruiz, J.-B. Sallée, A.B.A. Slangen, and Y. Yu, 2021: Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–1362, <a href="https://doi.org/10.1017/9781009157896.011" rel="nofollow">doi:10.1017/9781009157896.011</a>.</li> <li>Kopp, R. E., Garner, G. G., Hermans, T. H. J., Jha, S., Kumar, P., Reedy, A., Slangen, A. B. A., Turilli, M., Edwards, T. L., Gregory, J. M., Koubbe, G., Levermann, A., Merzky, A., Nowicki, S., Palmer, M. D., & Smith, C. (2023). The Framework for Assessing Changes To Sea-Level (FACTS) v1.0: A platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change. Geoscientific Model Development, 16, 7461–7489. <a href="https://doi.org/10.5194/gmd-16-7461-2023" rel="nofollow">https://doi.org/10.5194/gmd-16-7461-2023</a></li> <li>Garner, G. G., T. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Sea Level Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709" rel="nofollow">https://doi.org/10.5281/zenodo.5914709</a>.</li> </ul> <p><em>Please also include in the acknowledgements of works citing these projections:</em></p> <blockquote> <p>We thank the projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea Level Change Team for developing and hosting the IPCC AR6 Sea Level Projection Tool.</p> </blockquote> <p><strong>IPCC AR6 Licensing</strong></p> <p>The IPCC AR6 Sea-Level Rise Projections are licensed by the authors under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
Location List for IPCC AR6 Sea Level Projections
<p>This data set contains the location list file for the sea-level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. It can be used to cross-reference location IDs with names of the locations.</p> <p>Column 1 – Location name (string with spaces having been replaced with underscores)<br> Column 2 – Location ID (integer value)<br> Column 3 – Latitude (-90 to 90 degrees)<br> Column 4 – Longitude (-180 to 180 degrees)</p> <p>See <a href="https://zenodo.org/communities/ipcc-ar6-sea-level-projections">https://zenodo.org/communities/ipcc-ar6-sea-level-projections</a> for additional related data sets.</p>
Vaccum micropositioning pick & place - H2020 UWIPOM2 PROJECT
<p>Vaccum micropositioning pick & place</p> <p>https://www.youtube.com/watch?v=amyTANFp1sk&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</p>
Microassembly process - pick and release of ø25µm stainless balls - H2020 UWIPOM2 PROJECT
<p>Microassembly process - Pick and release of ø25µm stainless balls</p> <p>https://www.youtube.com/watch?v=xCu9CqVDltk&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</p>
Micrometric glue deposition on a piece - H2020 UWIPOM2 PROJECT
<p>Micrometric glue deposition on a piece</p> <p>https://www.youtube.com/watch?v=oLWD8ouOLog&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</p>
Micrometric glue fixation of two pieces - H2020 UWIPOM2 PROJECT
<p>Micrometric glue fixation of two pieces</p> <p>https://www.youtube.com/watch?v=2cUwOue5vDo&t=1s&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</p>
Data for the Community Pandemic Accounts Project
<p>On March 10, the first case of COVID-19 was diagnosed in Michigan. Since then, there have been many changes to everyday life, such as the "Stay Home, Stay Safe" executive order urging Michiganders to limit contact with others. The Western Michigan University Libraries conducted a data collection/history project that serves to understand how our regional community has been coping and adapting to these changes. The University community’s personal memories and experiences will provide a more nuanced view into these unprecedented times, while basic demographics will serve to contextualize their experiences. Collected accounts are primarily disseminated as a publicly available de-identified dataset for researchers to better understand pandemic impacts during this time period, and as a historical artifact through the Zhang Archive in 2025 that connects names to accounts.</p> <p>This dataset contains two files, a deidentified csv and a plain text README file with variable descriptions.</p>
Simple OMERO project packed with omero-cli-transfer
<p>This is a simple zip file with a "minimal" OMERO project packed using <a href="http://pypi.org/project/omero-cli-transfer">omero-cli-transfer</a>. It contains 4 datasets with examples of "regular" images, single-file-multiple-images (.ndpi), multiple-files-multiple-images (.vsi) and an image without an underlying file (exported as a tiff - obviously, upon reimporting, it WILL have an underlying file!). These have multiple annotations, tags and ROIs of multiple types, to cover most of the existing/common use cases in an OMERO instance.</p> <p>To unpack it into a project in your OMERO server, use "omero transfer unpack simple_project.zip".</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.