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.
1,806
datasets available to search
ShareScore release 0.9.0
Dataset results
1,806 results for “CA”
Removal of the Ovaries/Fallopian Tubes and CA-125 Screening to Reduce the Risk of Ovarian Cancer in Women at Increased Genetic Risk
ClinicalTrials.gov study NCT00043472. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Western Gull movement and nutrient deposition on Channel Islands, CA
Open the record for dataset details and reuse information.
Trait and microhabitat data for sympatric seep monkeyflowers along a soil moisture gradient in San Luis Obispo, CA
Open the record for dataset details and reuse information.
Species and environmental datasets from Sierra Nevada, CA (USA) streams in lake-stream networks
Open the record for dataset details and reuse information.
Surface water supply allocation, crop, and disadvantaged community data for the San Joaquin Valley, CA, 2016
Open the record for dataset details and reuse information.
Data from: Systematics of organic-walled microfossils from the ca. 780–740 Ma Chuar Group, Grand Canyon, Arizona
Open the record for dataset details and reuse information.
Data from: Calcium and strontium stable isotopes reveal similar behaviors of essential Ca and non-essential Sr in stream food webs
Open the record for dataset details and reuse information.
Dopamine axon population Ca signals in the striatum during odor cue- and reward-based choice tasks in mice
Open the record for dataset details and reuse information.
Daily weather summaries (temperature, precipitation and wind) for station SAN DIEGO LINDBERGH FIELD, CA US (GHCND:USW00023188), obtained from NOAA National Climatic Data Center and re-published through CCE LTER data system for community use as regionally relevant data, 1939 - Oct 2019.
Data provided here are obtained from the Global Historical Climatology Network - Daily (GHCN-Daily) database, provided by NOAA National Centers for Environmental Information (formerly NCDC). Data are periodically downloaded by CCE LTER Information Management and imported into the local data system for use by the local scientific community as regionally relevant data. The data contained in this dataset are from the SAN DIEGO LINDBERGH FIELD, CA US station (GHCND:USW00023188) at 32.7336°, -117.1831° beginning in July 1, 1939. The Global Historical Climatology Network - Daily (GHCN-Daily) dataset integrates daily climate observations from approximately 30 different data sources. Version 3 was released in September 2012 with the addition of data from two additional station networks. Changes to the processing system associated with the version 3 release also allowed for updates to occur 7 days a week rather than only on most weekdays. Version 3 contains station-based measurements from well over 90,000 land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. Over 25,000 stations are regularly updated with observations from within roughly the last month. The dataset is also routinely reconstructed (usually every week) from its roughly 30 data sources to ensure that GHCN-Daily is generally in sync with its growing list of constituent sources. During this process, quality assurance checks are applied to the full dataset. Where possible, GHCN-Daily station data are also updated daily from a variety of data streams. Station values for each daily update also undergo a suite of quality checks.
Daily sea-surface temperature measurements, collected and provided by the Shore Stations Program, La Jolla CA, 1916 - May 2019
Personnel from the Stephen Birch Aquarium-Museum at Scripps take daily temperature and salinity samples from the end of the Scripps Pier at the sea surface and a depth of about 5 meters. The proximity of Scripps Pier to the deep waters at the head of La Jolla submarine canyon results in data quite representative of oceanic conditions. Scripps Pier is a total of 1084 ft. long (330.4 M) and 22.5 ft. wide for most of it's length. However it is 46.0 ft. wide at the end where the lab/pump house structure is situated with the west wall standing 88.0 ft. from the end of the pier (=996 ft. from the shore). The orientation is 277 / 97 degrees magnetic, 14 degrees East variation. The deck of the pier is 33.5 ft. above Mean Low Low Water (MLLW). Data provided by Shore Stations Program with current funding provided by the "California Department of Parks and Recreation, Division of Boating and Waterways, Award# DPR-C1370020". Data are collected by staff aquarists and volunteers with the Birch Aquarium at Scripps. Contact shorestation@ucsd.edu if you have questions
CYGNSS Level 1 Science Data Record Version 3.0
This Level 1 (L1) dataset contains the Version 3.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of m2 from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 2.1; https://doi.org/10.5067/CYGNS-L1X21 . Other useful scientific and engineering measurement parameters include the DDM of Normalized Bistatic Radar Cross Section (NBRCS), the Delay Doppler Map Average (DDMA) of the NBRCS near the specular reflection point, and the Leading Edge Slope (LES) of the integrated delay waveform. The L1 dataset contains a number of other engineering and science measurement parameters, including sets of quality flags/indicators, error estimates, and bias estimates as well as a variety of orbital, spacecraft/sensor health, timekeeping, and geolocation parameters. At most, 8 netCDF data files (each file corresponding to a unique spacecraft in the CYGNSS constellation) are provided each day; under nominal conditions, there are typically 6-8 spacecraft retrieving data each day, but this can be maximized to 8 spacecraft under special circumstances in which higher than normal retrieval frequency is needed (i.e., during tropical storms and or hurricanes). Latency is approximately 6 days (or better) from the last recorded measurement time. Here is a summary of improvements the calibration and processing changes to the Version 3.0 data: 1) the transmitted GPS signal strength in the direction of the DDM scattering surface is determined in real time from measurements of the direct signal from the GPS satellite to the CYGNSS navigation receiver, allowing for the BRCS calibration to be corrected for variations in GPS transmit power; 2) the NBRCS has been validated using comparisons with a large population of modeled values derived from coincident ocean surface roughness spectra produced by the NOAA WAVEWATCH-3 oceanographic wave model; 3) L1 calibration parameters have been adjusted to produce a best fit to the model population.
GInSAR vertical surface displacements over the San Joaquin Valley, CA spanning April 2015 to October 2017
<p>A time series of vertical surface displacements over the San Joaquin Valley (SJV) spanning April 2015 to October 2017.</p> <p>Base Data: European Space Agency’s Sentinel-1A/B descending track 144; National Science Foundation Plate Boundary Observatory Global Positioning System (GPS) network; Southern California Earthquake Center’s Community Geodetic Model</p> <p>Time: 2015.04.01 – 2017.10.23, 51 acquisition times, 263 interferograms</p> <p>Processors/Software: GMTSAR, MATLAB</p> <p>Interferogram construction and unwrapping was performed in GMTSAR. GPS correction, time series inversion, and vertical projection was performed in MATLAB.</p> <p> </p> <p>This dataset was constructed following similar methods outlined in Neely et al., (2020).</p> <p>Neely, Wesley R., Adrian A. Borsa, and Francesca Silverii. “GInSAR: A CGPS Correction for Enhanced InSAR Time Series.” <em>IEEE Transactions on Geoscience and Remote Sensing</em> 58, no. 1 (January 2020): 136–46. <a href="https://doi.org/10.1109/TGRS.2019.2934118">https://doi.org/10.1109/TGRS.2019.2934118</a>.</p> <p> </p> <p>This dataset is provided as a “.mat” compatible with MATLAB. Contents include:</p> <p>TS – 3110x2940x51 matrix of the vertical surface displacements (mm) relative to April 1, 2015</p> <p>X – 3110x2940 matrix of the pixel longitudes (decimal degrees)</p> <p>Y – 3110x2940 matrix of the pixel latitudes (decimal degrees)</p> <p>rate – 3110x2940 matrix of the estimated vertical velocity (mm/yr) over April 2015 to October 2017</p> <p>time – 51x1 array of the dates for each acquisition in MATLAB data. Provided as integer day where time(1) = 736055 à April 1, 2015</p> <p> </p> <p>Example plotting in MATLAB:</p> <p>% plot the rate map</p> <p>figure</p> <p>pcolor(X,Y,rate)</p> <p>shading flat</p> <p> </p> <p>% plot the 25<sup>th</sup> time slice map of the displacement time series</p> <p>figure</p> <p>pcolor(X,Y,squeeze(TS(:,:,25))</p> <p>shading flat</p> <p> </p> <p>% plot the time series at location X(1500,1500) and Y(1500,1500)</p> <p>figure</p> <p>plot(time,squeeze(TS(1500,1500,:)))</p>
Supplementary material 1 from: Snyder MR, Stepien CA (2020) Increasing confidence for discerning species and population compositions from metabarcoding assays of environmental samples: case studies of fishes in the Laurentian Great Lakes and Wabash River. Metabarcoding and Metagenomics 4: e53455. https://doi.org/10.3897/mbmg.4.53455
Supplementary material: Additional methods, results, figures, and tables
Supplementary material 1 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Supplementary file for Dryad
Figure 5 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 5 Illustration of I. probstii. A Habit B inflorescence C sepal D petal E style branch, anther F fruit (Sources: A–ED. Probst CPC24.7.03.1 (UC); F photograph by M. Schafer and J. Sacks, photographs available from author).
Figure 1 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 1 Phylogenetic tree showing resolution of I. dabashanensis and I. probstii (bold) in series Chinenses (grey box). Posterior probability/bootstrap values shown above branches.
Figure 3 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 3 Illustration of I. dabashanensis. A Habit B inflorescence C sepal D petal E style branch, anther F fruit G stolon, roots, tubers H root tuber (Source: A–ED. Probst CPC3.5.01.3 (UC); F photograph by Marty Schafer and Jan Sacks; G, H photograph by D. Probst, photographs available from author).
Figure 2 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 2 Images A–CI. dabashanensisD–FI probstii. A Habit B inflorescence C flower D habit E inflorescence F flower (Photos B–F C. author; A Mark McDonough).
Supplementary material 1 from: Pergl J, Brundu G, Harrower CA, Cardoso AC, Genovesi P, Katsanevakis S, Lozano V, Perglová I, Rabitsch W, Richards G, Roques A, Rorke SL, Scalera R, Schönrogge K, Stewart A, Tricarico E, Tsiamis K, Vannini A, Vilà M, Zenetos A, Roy HE (2020) Applying the Convention on Biological Diversity Pathway Classification to alien species in Europe. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 333-363. https://doi.org/10.3897/neobiota.62.53796
Table S1
Data from: Experimental measurements of water molecule binding energies for the second and third solvation shells of [Ca(H2O)n]2+ complexes
Further understanding of the biological role of the Ca2+ ion in an aqueous environment requires quantitative measurements of both the short- and long-range interactions experienced by the ion in an aqueous medium. Here, we present experimental measurements of binding energies for water molecules occupying the second and, quite possibly, the third solvation shell surrounding a central Ca2+ ion in [Ca(H2O)n]2+ complexes. Results for these large, previously inaccessible, complexes have come from the application of finite heat bath theory to kinetic energy measurements following unimolecular decay. Even at n = 20, the results show water molecules to be more strongly bound to Ca2+ than would be expected just from the presence of an extended network of hydrogen bonds. For n > 10, there is very good agreement between the experimental binding energies and recently published density functional theory calculations. Comparisons are made with similar data recorded for [Ca(NH3)n]2+ and [Ca(CH3OH)n]2+ complexes.
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.