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1,298 results for “Archiving”
Bottom water acidification and warming on the western Eurasian Arctic shelves: Dynamical downscaling projections. Data archive.
<p>This archive includes one .mat file (MATLAB format) containing all the data and interpolated SINMOD model used for skill assessment and bias correction, and several NetCDF files containing the SINMOD SRES A1B projections (bias corrected where possible) for the bottom water in the pan-Arctic model domain for years 2001-2099 inclusive. Temporal resolution is biweekly and spatial resolution is 20km (see grid info in NetCDF files).</p>
Data archive accompanying "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, doi:10.1002/2017SW001785]
<p>This archive contains the data used to create the plots presented in "A new method of physics-based data assimilation for the quiet and disturbed thermosphere" [Sutton, 2018, SWx, doi:10.1002/2017SW001785].</p> <p>Format: MATLAB save file</p> <p>Contents:</p> <p>1. CHAMP and GRACE-A accelerometer-derived densities and ephemeris;</p> <p>2. TIE-GCM GPI model output sampled on both satellites;</p> <p>3. IRIDEA prior and posterior model output sampled on both satellites;</p> <p>4. Short description and units for all variables</p>
Audio proceedings from CONFLICT: Collections, Archives and Heritage symposium
<p>The <em>CONFLICT: Archives, Collections and Heritage</em> workshop (in-person and online) explores the topic of cultural heritage, archives and museum collections in conflict and post-conflict times. Starting with Cambodia’s art history and archaeology, discussions will expand to a global perspective. It is organised by Magali An Berthon from the Center for Textile Research at the University of Copenhagen on 25 March 2024, in conjunction with the exhibition “The Art of Ikat: A Cambodian Renaissance” which opens on 22 February 2024, at the Royal University Library, University of Copenhagen South Campus. Linda Sok and Sophea Oum, two of the artists featured in the exhibition, will be in attendance.</p> <p>How does conflict affect the recovery, preservation and conservation of museum collections and archives? Does it also affect the meaning and significance of such collections, whether they survived or were lost? Facing disappearance and destruction how do artists and artisans offer invaluable responses toward restoration and healing? These are some of the key questions animating this day of discussion centred on innovative research approaches.</p> <p>This event was supported by the Carlsberg Foundation and the David Fond og Samling.</p>
Data archive for "Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation"
<p>The archive contains the data files to reproduce the results presented in the article “Flight behaviour of Red Kites within their breeding area in relation to local weather variables: Conclusions with regard to wind turbine collision mitigation” published in the Journal of Applied Ecology.</p>
Data archive and code for "Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison"
<p>This upload contains data and code related to the paper "Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison" by M. Bushuk, S. Ali, D. Bailey, Q. Bao, L. Batte, U. S. Bhatt, E. Blanchard-Wrigglesworth, E. Blockley, G. Cawley, J. Chi, F. Counillon, P. Goulet Coulombe, R. Cullather, F. X. Diebold, A. Dirkson, E. Exarchou, M. Gobel, W. Gregory, V. Guemas, L. Hamilton, B. He, S. Horvath, M. Ionita, J. E. Kay, E. Kim, N. Kimura, D. Kondrashov, Z. M. Labe, W. Lee, Y. J. Lee, C. Li, X. Li, Y. Lin, Y. Liu, W. Maslowski, F. Massonnet, W. N. Meier, W. J. Merryfield, H. Myint, J. C. Acosta Navarro, A. Petty, F. Qiao, D. Schroder, A. Schweiger, Q. Shu, M. Sigmond, M. Steele, J. Stroeve, N. Sun, S. Tietsche, M. Tsamados, K. Wang, J. Wang, W. Wang, Y. Wang, Y. Wang, J. Williams, Q. Yang, X. Yuan, J. Zhang, and Y. Zhang, published in the Bulletin of the American Meteorological Society, DOI: https://doi.org/10.1175/BAMS-D-23-0163.1.</p> <p>See README.txt for a description of the datasets and code.</p>
Seasonal Carbonate Chemistry Variability in Marine Surface Waters of the Pacific Northwest. Data Archive.
<p>This archive includes two .nc files (NetCDF format) containing observational data (discrete and mooring) from marine surface waters of the Pacific Northwest that have not yet been submitted to a long-term data repository. These data contributed to the development of seasonal cycle data products described in the manuscript by Fassbender et al. A metadata file is provided for the discrete data subset (upper 10 m of discrete observational data); however, the complete cruise datasets and metadata will be submitted for archival in the National Centers for Environmental Information’s (NCEI) Ocean Carbon and Acidification Data repository (<a href="https://www.nodc.noaa.gov/oceanacidification/">https://www.nodc.noaa.gov/oceanacidification/</a>). Data subsets are provided here for accelerated public access. Data users are encouraged to download the complete datasets from NCEI once they are available (<a href="https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html">https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html</a>). Metadata for the University of Washington Oceanic Remote Chemical/Optical Analyzer (ORCA) mooring observations used by Fassbender et al., including the temperature and salinity data from the Dabob Bay and Twanoh moorings, are not provided here. Quality control protocols applied to the ORCA mooring data are outlined in the Quality Assurance Project Plan (<a href="http://nwem.ocean.washington.edu/ORCA_QAPP.pdf">http://nwem.ocean.washington.edu/ORCA_QAPP.pdf</a>; Newton and Devol, 2012).</p>
Data archive for Pepper, Bateson and Nettle, 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis'
<p>Data archive for the paper 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis' by Gillian Pepper, Melissa Bateson and Daniel Nettle. This version was uploaded in July 2018 after peer-review in the journal Royal Society Open Science. Compared to earlier version, it incorporates some minor error correction to the dataset, and reflects the revised analyses we performed after peer review. </p> <p>Our protocol and recording guide, which were preregistered on the Open Science Framework in 2016, are also included here, as is our PRISMA diagram.</p> <p>The data file 'unprocessed data' contains the data as extracted from the literature, with associations shown both as provided in the original papers, and converted to correlation coefficients. The algorithms for converting all the different associations to correlation coefficients are described in the flowchart and implemented in the R script 'effect conversion algorithms.r'.</p> <p>The data file 'processed data.csv' is the dataset analysed in the paper. Compared to 'unprocessed data.csv', it excludes: associations from studies of non-human animals; duplicate associations; a small number of associations from studies of medical treatments; and associations considered subparts or subscales of other associations. These exclusions are outlined in Methods section of the paper. In addition, in the processed data file, all correlations are aligned in direction so as to make them comparable (variable 'ValencedEffect'); and all associations are assigned to broad and fine categories.The script 'unprocessed to processed.r' makes the processed data file from the unprocessed one, or you can simply work from the processed one directly. </p> <p>The R script 'telomere metanalysis script RSOS REVISED.r' reproduces the analyses found in the paper.</p> <p>This version of the archive (July 17 2018) contains one small correction in the data files compared to all earlier versions. </p>
ScriptNet: ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD)
<p>This dataset contains the training and test set for the ICDAR 2017 Competition on Baseline Detection in Archival Documents (cBAD).</p> <p>A newly created freely available real world dataset consisting of 2035 annotated document page images that are collected from 9 different archives and form the basis of cBAD. Two competition tracks test different characteristics of the methods submitted. Track A [Simple Documents] is published with annotated text regions and tests therefore a method's quality of text line segmentation. The more challenging Track B [Complex Documents] provides only the page area. Hence, baseline detection algorithms need to correctly locate text lines in the presence of marginalia, tables, and noise.</p> <p>The dataset comprises images with additional PAGE XMLs. The PAGE XMLs contain text regions and baseline annotations.</p> <p>Competition Website: https://scriptnet.iit.demokritos.gr/competitions/5/</p> <p>Version 3 is the version of the cBad competition</p> <p>Version 4 contains also the page region and in case of a double-page the page split as separator.</p>
The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA
<p><strong>Related paper containing detailed description</strong>: <strong><a href="https://essd.copernicus.org/articles/12/177/2020/essd-12-177-2020.html">Moesinger et al. (2020)</a></strong></p> <p>Vegetation optical depth (VOD) describes the attenuation of radiation by plants. VOD a function of frequency as well as vegetation water content, and by extension biomass. VOD has many possible applications in studies of the biosphere, such as biomass monitoring, drought monitoring, phenology analyzes or fire risk management.</p> <p>We merged VOD observations from various spaceborne sensors (SSM/I, TMI, AMSR-E, AMSR2, WindSat) to create global long-term vod time series. Prior to aggregation the data has been rescaled to AMSR-E, removing systematic differences between them.</p> <p>There is a product for C-band (~6.9 GHz, 2002 - 2018), X-band (10.7 GHz, 1997 - 2018) and Ku-band (~19 GHz, 1987 - 2017). The data is global sampled on a regular 0.25 degrees grid. Each product is available as daily global netcdf4 files.</p> <p> </p> <p>Currently there is an issue with opening the file using ESA SNAP. As an alternative <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a> can be used to quickly visualize the data. </p> <p>An update of VODCA, addressing this issue and potentially including an extension of the dataset, is foreseen to be published on Zenodo early 2020.</p> <p> </p> <p><strong>Please contact us if you have any questions, problems or suggestions for improvement!</strong></p> <p> </p> <p><strong>Files:</strong></p> <ul> <li>"VODCA_C-band_2002-2018_v01.0.0.zip" (unzipped size: ~140 GB): <ul> <li>VODCA C-band files, sorted into yearly folders</li> </ul> </li> <li>"VODCA_X-band_1997-2018_v01.0.0.zip" (unzipped size: ~180 GB): <ul> <li>VODCA X-band files, sorted into yearly folders</li> </ul> </li> <li>"VODCA_Ku-band_1987-2017_v01.0.0.zip" (unzipped size: ~270 GB) : <ul> <li>VODCA Ku-band files, sorted into yearly folders</li> </ul> </li> <li>"vodca_v01-0_K-band_2007-06-01.nc" <ul> <li>sample file of the Ku-band product</li> </ul> </li> <li>"ESA-CCI-SOILMOISTURE-LAND_AND_RAINFOREST_MASK-fv04.2.nc" <ul> <li>Contains a global land mask, VODCA only has data for land locations. Source: https://github.com/TUW-GEO/smecv-grid</li> </ul> </li> </ul> <p><strong>Variables of data in VODCA files:</strong></p> <ul> <li>"VOD": Unitless, Vegetation Optical Depth of the respective band</li> <li>"sensor_flag": Bit-flag indicating which sensors contributed to each observation. <ul> <li>Values: <ul> <li>1 = AMSR-E</li> <li>2 = AMSR2</li> <li>3 = SSM/I F8</li> <li>4 = SSM/I F11</li> <li>5 = SSM/I F13</li> <li>6 = TMI</li> <li>7 = WindSat</li> </ul> </li> </ul> </li> <li>"processing_flag": Bit-flag indicating irregularities during processing affecting the quality of the observations <ul> <li>Values: <ul> <li>0 = Everything is fine</li> <li>10 = AMSR-2 7.3 GHz band is used instead of 6.9 GHz</li> <li>11 = Sensor is scaled to matched TMI instead of AMSR-E</li> <li>12 = Sensor scaled without temporally overlapping observations</li> </ul> </li> </ul> </li> <li>"time"/"lon"/"lat": Dimensions of the data.</li> </ul> <p> </p>
Sri Ksetra (HMA) Digitised Paper Archive (Version 3)
<p>This is the current version of a digitised excavation archive, deriving from archaeological investigations at Sri Ksetra (HMA) in Burma (Myanmar) carried out 2014-16. It comprises: excavation records, artefact illustrations, section drawings, plans, maps, artefact data, sample data, field notes and accompanying indexes. </p>
Dataset about Diada de Sant Jordi from public TV3 audiovisual archive - FIAT/IFTA Media Studies Grant
<p>[eng] This a dataset related to the Diada de Sant Jordi through TV3 public audiovisual archive. This dataset contains for a total of 572 videos with title, link, length in time and the program that have published information about this day. Most of these videos belong to the news program. This dataset is published on the occasion FIAT-IFTA Media Studies Grant 2019</p> <p>Additionally, three more files are included. These files include the final sample, the queries on Tv3 archive and recommended books that appear on the footage. The final sample has 337 videos and queries are useful to look by year </p> <p>[cat] Aquest és un dataset vinculat a la Diada de Sant Jordi dels arxius audiovisuals públics de Tv3. Aquest dataset conté un total de 572 videos amb el títol, dreçera, longitud en temps i el programa que ha publicat informació relativa aquest dia. La major part d'aquests videos corresponent corresponen als programes de notícies. Aquestes dades es publiquen amb motiu de FIAT-IFTA Media Studies Grant 2019</p> <p>A més a més s'inclouen 3 fitxers. Aquests fitxes inclouen la mostra final, les cadenes de cerca a l'arxiu de Tv3 i els llibres recomanats que apareixen en els videos. La mostra final conté 339 videos i les cadenes són útils per fer cerques per any.</p>
Data archive associated with "Landscape age as a major control on the geography of soil weathering" (https://doi.org/10.1029/2019GB006266)
<p>(1) Table including parameter values and weathering model outputs associated with NASGLP sampling locations (SLP_data.csv). </p> <p>(2) List of rivers used for calibrating erosion estimates (river_list.csv).</p> <p>(3) R workspace with same data as (1), plus a data frame of global parameter values ("gm") and spatial polygons giving continent boundaries ("con").</p> <p>(4) Scripts with functions for running the single-compartment weathering model at individual point locations or running a global sample of locations and computing summary statistics by continent (run_soilgenesis.R; soilgenesis.R). </p>
Data archive for the peer-reviewed journal article "Links between atmospheric aerosols and sea state in the Arctic Ocean"
<p>This dataset accompanies the peer-reviewed journal article titled "Links between atmospheric aerosols and sea state in the Arctic Ocean" which was accepted for publication in the Journal of Atmospheric Environment in September 2024, https://doi.org/10.1016/j.atmosenv.2024.120844. </p> <p>This dataset contains information on sea surface properties, meteorology, and aerosol data from measurements conducted during the Arctic Century Expedition which was carried out in August and September of 2021 in the Russian Arctic region. The dataset contains the following information:</p> <p><br>1) aerosol_size_distributions.csv: The hourly averaged time-series of aerosol size distribution measurements from an aerodynamic particle sizer. Further information for this data file is provided in Meta_data_for_aerosol_size_distributions.txt.</p> <p><br>2) aerosol_composition_and_volume.csv: Time series of mass concentrations of Na+Mg (SSA proxy) and Al+Si+Ca (dust proxy) in aerosol particles collected on filters. The time-series also contains aerosol volume concentration information for the coarse and fine aerosol categories, i.e., samples with count median diameters larger than 0.99 µm and smaller than 0.99 µm, respectively. Further information for this data file is provided in Meta_data_for_aerosol_composition_and_volume.txt. </p> <p><br>3) sea_surface_elevation_time_series.pkl: a pickle file containing the sea surface elevation time-series. The sea surface elevation data was extracted from 3D-reconstructed sea surface data. The 3D reconstruction of the sea surface was achieved by processing stereoscopic images of the sea surface using the Waves Acquisition Stereo System (WASS) software (Bergamasco et al., 2017). Further information for this data file is provided in Metadata_for_sea_surface_elevation_time_series.txt.</p> <p><br>4) aerosol_meteo_wave_merged_data.csv: This file contains the time-series of merged hourly averages of aerosol number concentrations, meteorological data, environmental data, and sea surface properties. The dataset also contains the average coordinate of the research vessel and its distance to land masses throughout the expedition. The meteorological data were measured during the expedition and the original unmerged data are available in Thurnherr et al. (2024). Other environmental data, such as sea surface temperature, are obtained from the fifth generation ECMWF reanalysis for the global climate and weather (ERA5, Hersbach et al., 2023), and sea ice concentration was obtained from AMSR-2 daily satellite measurements (Copernicus Climate Change Service (C3S), 2020). Sea surface properties are extracted from time series of sea surface elevation. Further information for this data file is provided in Metadata_for_aerosol_meteo_wave_merged_data.txt.</p>
Particle size and velocity distributions from a Thies Clima 3D Stereo disdrometer installed at the Casale Calore site in L'Aquila (Italy), monthly netCDF archive
<p>Disdrometric data from a Thies Clima 3D Stereo disdrometer, with 22 size classes and 20 velocity classes, located at the instrumented site of Casale Calore in L'Aquila (Italy, 42.3831 N, 13.3148 E, 683 m a.s.l.), managed by the University of L'Aquila and the Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS). </p> <p>Mid values and widths of the classes and instrument ancillary data are provided. One-minute spectra are aggregated every 5 minutes and saved in monthly netCDF files.</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure:</p> <h2><strong>File "LAQ_3DS_202301_5min.nc"</strong></h2> <pre><strong> dimensions</strong>: <em>diameter </em>= 22; <em>velocity </em>= 20; <em>n_image </em>= 20; <em>y_image </em>= 12; <em>x_image </em>= 12; <em>time </em>= UNLIMITED; // (8741 currently) <strong>variables</strong>: long <em>time_UTC</em>(time=8741); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>diameters</em>(diameter=22); :description = "Mid values of the size classes"; :units = "mm"; float <em>velocities</em>(velocity=20); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float <em>diameters_width</em>(diameter=22); :description = "Width of the size classes"; :units = "mm"; float <em>velocities_width</em>(velocity=20); :description = "Width of the velocity classes"; :units = "m s^-1"; int <em>spectrum</em>(diameter=22, velocity=20, time=8741); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 22U, 20U, 1U; // uint float <em>PSD</em>(diameter=22, time=8741); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 22U, 1U; // uint double <em>monthlySpectrum</em>(diameter=22, velocity=20); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over the entire month."; :units = "counts"; double <em>monthlyPSD</em>(diameter=22); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; int <em>images</em>(x_image=12, y_image=12, n_image=20, time=8741); :description = "Images of samples of the detected precipitating particles. Images are 48x12 pixel maximum, for a max of 4 stacked 12x12 images. Most of the time less than 4 images are provided."; :units = "0-255 pixel values"; :_ChunkSizes = 12U, 12U, 20U, 1U; // uint int <em>image_count</em>(time=8741); :description = "How many images are registred by the instrument in the minute."; :units = "0-4 count"; :_ChunkSizes = 1024U; // uint int <em>precip_type</em>(n_image=20, time=8741); :description = "Precipitation type as classified by the instument based on shape, size, velocity and presence of water, according to the following table with 11 entries (0-10): 0-reserved value, 1-false positive, 2-rain or graupel, 3-drizzle, 4-drizzle with rain, 5-rain, 6-rain with snow, 7-snow, 8-ice prisms, 9-graupel, 10-hail."; :units = "0-10 code"; :_ChunkSizes = 20U, 1U; // uint int <em>particle_diam</em>(n_image=20, time=8741); :description = "Main diameter of the particles shown in the images."; :units = "mm"; :_ChunkSizes = 20U, 1U; // uint //<strong> global attributes</strong>: :<em>title </em>= "Thies Clima 3D Stereo disdrometer data, aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "Particle counts diveded in 22 size classes and 20 velocity classes. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Jan 2023"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :<em>source </em>= "TC 3DS disdrometer at MZS (Antarctica)"; :<em>location </em>= "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "23-Oct-2024 11:13:22 UTC"; :<em>coverage </em>= "Monthly coverage (Jan 2023): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created from raw TC telegram TDD 163, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN."; </pre> <p> </p> <p> </p>
Data archive for "Modified rice bran arabinoxylan as a nutraceutical in health and disease — A scoping review with bibliometric analysis"
<p>v1.0.0 Release with the publication of the paper on PLoS One</p> <p>Ooi, S. L., Micalos, P. S., & Pak, S. C. (2023). Modified rice bran arabinoxylan as a nutraceutical in health and disease—A scoping review with bibliometric analysis. PLOS ONE, 18(8), e0290314. https://doi.org/10.1371/journal.pone.0290314</p> <p><strong>Full Changelog</strong>: https://github.com/sooi10/RBACScoping/commits/NetworkAnalysis</p>
OpenBiodiv Archive
<p>OpenBiodiv is a Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science [1] and is available via http://openbiodiv.net .</p> <p>This data publication contains a single gzipped rdf/nquads archive of all of the OpenBiodiv graph on 2021-08-23 provided and uploaded to Zenodo by Mariya Dimitrova.</p> <p>Files:</p> <p>openbiodiv_v3.nq.gz - a gzipped rdf/nquads archive of OpenBiodiv</p> <p>[1] Penev, L.; Dimitrova, M.; Senderov, V.; Zhelezov, G.; Georgiev, T.; Stoev, P.; Simov, K. OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science. <em>Publications</em> <strong>2019</strong>, <em>7</em>, 38. See also <a href="https://doi.org/10.3390/publications7020038">https://doi.org/10.3390/publications7020038 .</a></p>
hector-run-archive
<p>Minted outputs of Hector runs produced by the https://github.com/JGCRI/hector-run-archive associated with v3.5.0</p>
How to find data at the Danish National Archives [Webinar recording]
<p>The Danish National Archives have launched Digidata (https://digidata.rigsarkivet.dk/) which makes it easier for researchers and students to find and gain access to our large collection of research data (approx. 3000 datasets, primarily from surveys) and administrative data (approx. 6000 datasets, e.g. registers such as the Conscription Register and the Taxpayer Register).</p> <p>In the webinar, the presenter showed show how you could use Digidata platform to search for a dataset.</p> <p>Note: Portal presented at the event is in the Danish language.</p> <p>The video is available on the <a href="https://www.youtube.com/watch?v=vsKaT3_TDSM">CESSDA Training YouTube channel.</a></p>
Data Archive: Local and Global Order in Dense Packings of Semiflexible Polymers of Hard Spheres
<p>Data archive corresponding to the publication "Local and Global Order in Dense Packings of Semi-flexible 2 Polymers of Hard Spheres" by D. Martinez-Fernandez et al., Polymers 15, 551 (2023); DOI: https://doi.org/10.3390/polym15030551.</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All snapshots have been generated and successively analyzed by the Simu-D software.</p>
Electronic Supplement / Data Archive for "Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response"
<p>These files provide supplemental data to accompany the paper "Global Variations in the Time Delays Between Polar Ionospheric Heating and the Neutral Density Response" submitted to AGU journal <em>Space Weather</em>, with manuscript number 2022SW003410. Details are provided in the file <strong>ReadMe_DataArchive.pdf</strong>.<br> </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.