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.
1,323
datasets available to search
ShareScore release 0.9.0
Dataset results
1,323 results for “degree”
Massachusetts Growing Degree Day and Precipitation Maps 2003
A regression model that estimates monthly temperature and precipitation as a function of latitude, longitude, and elevation for the New England area was used to estimate annual growing degree days and precipitation for the state of Massachusetts. For details of the regression model please see the published paper (Ollinger, S.V., Aber, J.D., Federer, C.A., Lovett, G.M., Ellis, J.M., 1995. Modeling Physical and Chemical Climate of the Northeastern United States for a Geographic Information System. US Dept of Agriculture, Forest Service, Radnor, PA, USA).
Temperature and Climate Attribution estimates supporting "Human Fingerprints on Daily Temperatures in 2022" (2x2 degrees, 2022)
<p>These data support the publication of "Human Fingerprints on Daily Temperatures in 2022" published in the <a href="https://www.ametsoc.org/index.cfm/ams/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/">BAMS-EEE special issue</a> in 2024 (DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0264.1">10.1175/BAMS-D-23-0264.1</a>). Included are:</p> <ul> <li>Temperatures: <strong>Gilfordetal2024_BAMS-EEE_T2022.nc</strong></li> <li>Attributions estimates (Climate Shift Index and Change in Information due to Perspective): <strong>Gilfordetal2024_BAMS-EEE_ChIP2022.nc</strong></li> </ul> <p>And an accompanying land-sea mask from ERA5 (<strong>Gilfordetal2024_BAMS-EEE_LandSeaMask.nc</strong>). All data values valid for the 2022 calendar year and interpolated to a 2x2 degrees spatial grid to support the study's analysis.</p> <p>For more information on this dataset or to follow up, please contact Daniel Gilford (<a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a>).<br><br><em>Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.</em></p>
Simulation of the SLR Space Segment Evolution to Improve the Realization of Terrestrial Reference Frames and Determination of Low-Degree Gravity Field Parameters
<p>These are data obtained from simulation studies of the development of the space segment of the SLR technique. Detailed information can be found in Najder et al. (2025). Najder, J., Sośnica, K., Zajdel, R., & Kur, T. (2025). Simulation of the SLR space segment evolution to improve the realization of terrestrial reference frames and determination of low-degree gravity field parameters. <em>Journal of Geodesy</em>, <em>99</em>(6), 46. https://doi.org/10.1007/s00190-025-01971-5</p>
Zero-degree isotherm latitude (ZIL) position over Antarctica: Historical and Projections
<p>This is the dataset associated to the research 'Southward migration of the zero-degree isotherm latitude over the Southern Ocean and the Antarctic Peninsula: extent and implications' published in <i>Science of the Total Environment</i>.</p><p>This repository contains:</p><ul><li><strong>ZIL_ERA5_1957-2020_position.zip:</strong> Historical position of the ZIL for every longitude point in ERA5 from 1957 to 2020 for different <i>seasons</i>. Files named:<ul><li>ZIL_ERA5_1957-2020<i>[season]</i>position.csv<ul><li>Dimensions: [lons, years]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_ERA5_1957-2020_timeseries.csv:</strong> Historical spatially averaged position of the ZIL for Antartica (Ant) and the Antarctic Peninsula (AP) in ERA5 from 1957 to 2020 for different <i>seasons</i>. File named:<ul><li>ZIL_ERA5_1957-2020_timeseries.csv<ul><li>Dimensions: [years, season_area]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_ERA5_1957-2020_meanposition.csv:</strong> Historical temporally averaged position of the ZIL in ERA5 from 1957 to 2020 for different <i>seasons </i>and <i>months</i>. File named:<ul><li>ZIL_ERA5_1957-2020_meanposition.csv<ul><li>Dimensions: [lons, season/month]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_Historical_position.zip:</strong> Mean position of the ZIL for every longitude point in Historical simulations of CEMIP6 from 1957 to 2014 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_Hist_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_SSP2-45.zip:</strong> Mean position of the ZIL for every longitude point under the SSP2-4.5 scenario in CEMIP6 for the period 2040-69 and 2070-90 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_SSP2-45_2040-69_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li><li>ZIL_CEMIP6_SSP2-45_2070-99_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_SSP5-85.zip:</strong> Mean position of the ZIL for every longitude point under the SSP5-8.5 scenario in CEMIP6 for the period 2040-69 and 2070-90 and trends for the period 2015-99 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_SSP5-85_2040-69_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li><li>ZIL_CEMIP6_SSP5-85_2070-99_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li></ul><p><i><strong>seasons</strong></i> are:</p><ul><li>ANN: Annual mean</li><li>DJF: December-January-February (Summer)</li><li>MAM: March-April-May (Autumn)</li><li>JJA: June-July-August (Winter)</li><li>SON: September-October-November (Spring)</li></ul><p><i><strong>areas</strong></i> are:</p><ul><li>Ant: All Antarctica</li><li>AP: Antarctic Peninsula</li></ul><p><strong>Note:</strong> CEMIPT6 models include a column with CEMIP6 model average</p><p><strong>Version control</strong></p><p>v1.0 - Initial version<br>v1.1 - Change ERA5 dataset calculations from preliminary version of ERA5 to final version of ERA5</p><p> </p><p><strong>How to cite</strong></p><p>If you use this dataset, please cite the accompanying paper as:</p><p> </p><p><strong>Complementary code</strong></p><p>You can find the jupyter notebooks to complement the research in: <a href="https://doi.org/10.5281/zenodo.10063849">https://doi.org/10.5281/zenodo.10063849</a></p><p> </p><p><strong>Contact</strong></p><p>If you have any question, please contact with Sergi at <a href="mailto:sergi.gonzalez@slf.ch">sergi.gonzalez@slf.ch</a></p>
Projected fire cycle (yrs) for Canada at a 0.25 degree resolution
<p>These rasters represent the projection of future fire cycles for Canada at a 0.25 degree of resolution. The data was produced in three steps:</p> <ol> <li>Future fire cycles were obtain by projecting annual area burned as in Boulanger et al. (2014) (https://cdnsciencepub.com/doi/full/10.1139/cjfr-2013-0372) at the homogeneous fire regime zone scale. Models used here were improved from those used in Boulanger et al. (2014). Projections were conducted for specific time periods (baseline, 2011-2040, 2041-2070 and 2071-2100) under specific anthropogenic climate forcing scenarios (RCP 4.5 and RCP 8.5). Three Earth System models were used i.e., CanESM2, HadGEM2-ES and MIROC-ESM-CHEM.</li> <li>Values obtained at the homogeneous fire regime zone scale were further "downscaled" at a 250m resolution according to vegetation type (cover x age class) following Bernier et al. (2016) (https://www.mdpi.com/1999-4907/7/8/157) using forest attributes of 2011 as assessed in Beaudoin et al. (2014) (https://cdnsciencepub.com/doi/10.1139/cjfr-2013-0401). </li> <li>Values obtained at a 250m resolution were averaged in 0.25x0.25 degree cells.</li> </ol>
Supplementary data: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's glaciers
<p>Supplementary data to <a href="http://doi.org/10.1038/nature23878"><em>Kraaijenbrink, Bierkens, Lutz and Immerzeel, 2017. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers, Nature.</em></a> Model code can be found <a href="https://doi.org/10.5281/zenodo.2548689">here</a>.</p> <p>Please note that all data is provided in 7z-archives. To extract the data use the open source software <a href="http://www.7-zip.org/">7zip</a>.</p> <p> </p> <p><strong>Model input: </strong>Raster data</p> <p>The raster data that is required to run the model is available for the entire High Mountain Asia (<em>complete-hma.7z</em>) and for each <a href="https://www.glims.org/RGI/">RGI v5.0</a> sub-region (<<em>region-name>.7z</em>). The 7z-archives hold separate folders for each glacier, which are named by RGI glacier ID. The rasters for each glacier are in GeoTIFF format, have a 30 m resolution, are in local UTM projection (WGS84 datum), and are clipped to the RGI glacier extent.</p> <p>Rasters present for each glacier are:</p> <pre>classification.tif The debris classification made in google earth engine. debris-thickness-50cm.tif Debris thickness estimation based on Landsat 8 surface temperature. ice-thickness.tif Ice thickness determined using the Glabtop2 model ls8-composite-b456.tif Landsat 8 warmest-pixel optical composite (bands RED, NIR, SWIR1) ls8-composite-tsurf.tif Landsat 8 warmest-pixel surface temperature composite srtm-elevation.tif SRTM 1 arc second elevation data srtm-slope.tif Slope of the SRTM 1 arc second data</pre> <p> </p> <p><strong>Model input: </strong>RDS data</p> <p>The general model input data (<em>mbg-model-rds-data.7z)</em> is stored in R’s binary RDS format and <em><a href="https://www.r-project.org/">R</a></em> is required to open and read the data.</p> <p>Files present in the 7z-archive are:</p> <pre>dP_factors_2006-2100.rds Precipitation changes (delta factors) up to 2100 dT_degrees_2006-2100.rds Temperature changes (Kelvin) up to 2100 glacier-data.rds Glacier centroids with current climate and mass balance input ostrem_meancurve.rds The Östrem curve used by the model rgi-subregions.rds RGI sub-region polygons for Asia</pre> <p> </p> <p><strong>Output data</strong></p> <p>Region-aggregated output is available in ESRI Shapefile format for the RGI sub-regions, major river basins, and for a 1×1 degree grid (<em>output-shapefiles.7z</em>). The attribute tables of all shapefiles hold data on the occurrence of debris as well as current glacier area and volume, and volume projections for the end of century.</p> <p>The available shapefile attributes are:</p> <pre>count number of glaciers a_total total glacier area (m2) a_debris glacier area covered by debris (m2) a_ela glacier area below modelled ELA (m2) a_ela_deb glacier area below modelled ELA covered by debris (m2) v_total total glacier volume (m3) v_debris glacier volume covered by debris (m3) v_ela glacier volume below modelled ELA (m3) v_ela_deb glacier volume below modelled ELA covered by debris (m3) m_total_gt total glacier mass (gigaton) volST_EOC volume remaining in end of century under a stable current temperature vol15_EOC volume remaining in end of century under 1.5 degree scenario vol26_EOC volume remaining in end of century for the RCP2.6 model ensemble vol45_EOC volume remaining in end of century for the RCP4.5 model ensemble vol60_EOC volume remaining in end of century for the RCP6.0 model ensemble vol85_EOC volume remaining in end of century for the RCP8.5 model ensemble</pre>
A set of allsky camera images from latitude 55 degrees North
<p>A set of R G and B images from an allsky camera situated in Copenhagen, Denmark. The images have been darksubtracted and split into these 16-bit FITS format images. Each image is the sum of something like 9 PNG images which each, originally, were 14 bit images from a ZWO camera cmos detector.</p>
IODP Expedition 391 Whole-round core section composite 360 degree images
Images of the outside of hard rock whole-round sections were acquired using a linescan imager (Section Half Imaging Logger [SHIL]) and a special holder that allows each 90 degree segment of the outer surface to be positioned properly. The images were taken at a resolution of 20 lines/mm (50 micropixels). JRSO staff take these quadrant images and compile them into a side-by-side rollout photograph of the section. Composite images are available as both JPG and TIF image formats. Individual quadrant images are available as JPG images only through this report; contact the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a> if quadrant TIF files (~160 MB) are needed.
IODP Expedition 397T Whole-round core section composite 360 degree images
Images of the outside of hard rock whole-round sections were acquired using a linescan imager (Section Half Imaging Logger [SHIL]) and a special holder that allows each 90 degree segment of the outer surface to be positioned properly. The images were taken at a resolution of 20 lines/mm (50 micropixels). JRSO staff take these quadrant images and compile them into a side-by-side rollout photograph of the section. Composite images are available as both JPG and TIF image formats. Individual quadrant images are available as JPG images only through this report; contact the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a> if quadrant TIF files (~160 MB) are needed.
IODP Expedition 367 Whole-round core section composite 360 degree images
Images of the outside of hard rock whole-round sections were acquired using a linescan imager (Section Half Imaging Logger [SHIL]) and a special holder that allows each 90 degree segment of the outer surface to be positioned properly. The images were taken at a resolution of 20 lines/mm (50 micropixels). JRSO staff take these quadrant images and compile them into a side-by-side rollout photograph of the section. Composite images are available as both JPG and TIF image formats. Individual quadrant images are available as JPG images only through this report; contact the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a> if quadrant TIF files (~160 MB) are needed.
Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides
<p>We provide here the underlying data of the publication "Kinetic modeling of phosphorylase-catalyzed iterative β-1,4-glycosylation for degree of polymerization-controlled synthesis of soluble cello-oligosaccharides". Please find the abstract below.</p> <p><strong>Background: </strong>Cellodextrin phosphorylase (CdP; EC 2.4.1.49) catalyzes the iterative β-1,4-glycosylation of cellobiose using α-D-glucose 1-phosphate as the donor substrate. Cello-oligosaccharides (COS) with a degree of polymerization (DP) of up to 6 are soluble while those of larger DP self-assemble into solid cellulose material. The soluble COS have attracted considerable attention for their use as dietary fibers that offer a selective prebiotic function. An efficient synthesis of soluble COS requires good control over the DP of the products formed. A mathematical model of the iterative enzymatic glycosylation would be important to facilitate target-oriented process development.<br> <strong>Results: </strong>A detailed time-course analysis of the formation of COS products from cellobiose (25 mM, 50 mM) and α-D-glucose 1-phosphate (10–100 mM) was performed using the CdP from <em>Clostridium cellulosi</em>. A mechanism-based, Michaelis–Menten type mathematical model was developed to describe the kinetics of the iterative enzymatic glycosylation of cellobiose. The mechanistic model was combined with an empirical description of the DP-dependent self-assembly of the COS into insoluble cellulose. The hybrid model thus obtained was used for kinetic parameter determination from time-course fits performed with constraints derived from initial rate data. The fitted hybrid model provided excellent description of the experimental dynamics of the COS in the DP range 3–6 and also accounted for the insoluble product formation. The hybrid model was suitable to disentangle the complex relationship between the process conditions used (i.e., substrate concentration, donor/acceptor ratio, reaction time) and the reaction output obtained (i.e., yield and composition of soluble COS). Model application to a window-of-operation analysis for the synthesis of soluble COS was demonstrated on the example of a COS mixture enriched in DP 4.<br> <strong>Conclusions:</strong> The hybrid model of CdP-catalyzed iterative glycosylation is an important engineering tool to study and optimize the biocatalytic synthesis of soluble COS. The kinetic modeling approach used here can be of a general interest to be applied to other iteratively catalyzed enzymatic reactions of synthetic importance.</p>
Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis
<p>Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques. </p> <p>Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882</p> <p>Changelog:</p> <p>V 1.0 - Initial version.<br> V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.</p>
Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data
<p><strong>Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data</strong></p><p><strong>Authors: </strong>Marcel Kok, Johan Meijer, Willem-Jan van Zeist, Jelle Hilbers, Marco Immovilli, Jan Janse, Elke Stehfest, Michel Bakkenes, Andrzej Tabeau, Aafke Schipper, Rob Alkemade</p><p><strong>Point of contact:</strong> <a href="mailto:Marcel.Kok@pbl.nl">Marcel.Kok@pbl.nl</a></p><p><strong>Research paper summary:</strong> Global biodiversity is projected to further decline under a wide range of future socio-economic development pathways, even in sustainability-oriented scenarios. This raises the question how biodiversity can be put on a path to recovery, the core challenge for the implementation of the CBD Kunming-Montreal Global Biodiversity Framework. We designed two ambitious global conservation strategies, 'Half Earth' (HE) and 'Sharing the Planet' (SP), and evaluated their ability to restore terrestrial and freshwater biodiversity and to provide nature's contributions to people (NCP), while also limiting global warming below 2 degrees and ensuring food security. We applied the integrated assessment framework IMAGE with the GLOBIO biodiversity model, using the 'Middle of the Road' Shared Socio-economic Pathway (SSP2) with its projected human population growth as baseline. We found that the HE strategy performs generally better for terrestrial biodiversity (biodiversity intactness (MSA), Area of Habitat, Living Planet Index, Red List Index) in currently still natural regions. The SP strategy yields more improvements for biodiversity in human-used areas, for freshwater biodiversity and for regulating NCP (pest control, pollination, erosion control, water quality). However, both strategies were insufficient to restore biodiversity and corresponded with considerable increases in food security risks and global temperature. Only when we combined the conservation strategies with a portfolio of 'integrated sustainability measures', including climate change mitigation and reductions of food waste and animal product consumption, our scenarios resulted in a restoration of biodiversity and NCP while keeping global warming below two degrees and food security risks below the baseline projection.</p><p><strong>Contents:</strong> This repository contains the supplementary spatial data describing the specific prioritization of conservation areas under the Half Earth (HE) and Sharing the Planet (SP) scenarios, and the resulting scenario land use and MSA data sets for the year 2050, including also a baseline (BL) scenario. All spatial data is in geotiff format at a 10 arcsecond resolution in WGS84 coordinate system. Detailed description of the methodology is provided in the paper listed under "related identifiers".</p><p><strong>Keywords:</strong> Nature conservation, Half Earth, Sharing the Planet, Climate Change, Food Security, Solution-oriented scenarios, Biodiversity, Nature's Contribution to People, NCP</p>
Typology of academic degrees in the Modern China Biographical Database
<p>This table presents the bilingual typology of academic degrees used in the Modern China Biographical Database. It is based mainly on the data collected in historical sources.</p> <p>There are two levels:</p> <p>- Degree name: full name of the academic degree in English</p> <p>- Degree_Level_Eng: first level of classification and clustering of academic degrees in English</p> <p>- Degree_Level_ZhT: first level of classification and clustering of academic degrees in Chinese</p>
Homography with Degree
<p>We selected 100 images from MF-DFC22 dataset and divided them into four categories: architecture, mountains, coasts, and farmland. We performed different degrees of projection transformations in eight directions on each image to explore the performance of different keypoint detector under projection transformations.</p>
Monthly Standardized Precipitation Evapotranspiration Index (SPEI) for Australia at 0.05 degree from 1982 to 2014
<p>This monthly SPEI dataset in 1-48 scale is calculated using R's <a href="https://cran.r-project.org/web/packages/SPEI/index.html">SPEI </a>package in 'kernel -- rectangular', 'distribute -- log-Logistic' and 'fit -- ub-pwm' mode, with <a href="http://www.csiro.au/awap/">AWAP'</a>s monthly rainfall and <a href="http://www.bom.gov.au/water/landscape/">ALWB</a>'s potential evapotranspiration.</p>
Dateset on 'Disentangling associations of human wellbeing with green infrastructure, degree of urbanity, and social factors around an Asian megacity'
<p>The data was collected a part of the baseline survey on household socio-economics among the Bengalurian along the rural-urban interface. </p>
GPC/m: Global Precipitation Climatology by Machine Learning; Quasi-global, Daily, and One Degree Spatial Resolution
<p>A precipitation dataset, Global Precipitation Climatology by Machine Learning (ML), GPC/m, is released.</p> <p>This new precipitation dataset has been produced by machine learning, which is daily from 1979 to 2020 (will be to present), 1° × 1° spatial resolution. Three ML methods are used. Data is produced from outgoing longwave radiation (OLR) and atmospheric circulation from reanalysis. You can download this with DOI.</p> <p>This daily precipitation dataset has been produced by machine learning (ML) methods using satellite observations and atmospheric circulations from reanalysis. The quasi-global daily precipitation dataset has been around for 42 years from 1979 to 2020, which will be updated to the present. The spatial resolution is 1° × 1° zonally global and from 40°S to 50°N. The ML methods are supervised learning, and the reference data are estimated precipitation datasets from 2001 to the present. The input data are somewhat modified based on knowledge of the climatological background. Using the trained statistical models, we predict back to 1979, when daily precipitation data was almost unavailable globally. For now, this GPC/m precipitation dataset version is GPC/m-v1-2024. This data will be updated in the future with added value. The purpose of this dataset is a challenge to produce a climatological dataset by reducing artificial gaps as much as possible for discussion of climatology, climate variability, and climate change. This dataset is very useful for statistical analysis, such as composite analysis and correlation analysis. Disadvantages should also be understood in the description paper (Takahashi, 2024c). Also, I hope that this dataset can contribute to improving the current precipitation datasets, which are based on physical or researcher-explaining algorithms.<br><br>To facilitate analysis of the dataset, it is distributed in Network Common Data Form (netCDF) format and the Grid Analysis and Display System (GrADS) format (with control file). If you would like recently updated data, please contact the creator. If it has already been created, it can be distributed.<br><br><em>Added on September 18, 2024.</em><br>More details are in the preprint paper at this link (<a href="https://doi.org/10.48550/arXiv.2409.09639">Takahashi, 2024, https://doi.org/10.48550/arXiv.2409.09639</a>).</p> <p><em>Added on March 4, 2025.</em><br><strong>Alternative Download Options</strong><br>If you experience slow download speeds from Zenodo, alternative mirrors are available for the dataset files.<br><em><span>However, we kindly request you to download the .ctl file from Zenodo for tracking purposes.</span></em><br>Download NetCDF (.nc) or Binary (.bin) from:<br><a href="https://camo.fpark.tmu.ac.jp/gpcm.html">https://camo.fpark.tmu.ac.jp/gpcm.html</a></p>
Furious transfer infrared spectrum of Ranolazine bulk drug, soluble in ethanol, isopropanol under vacuum, at different temperature 0, 40, 70 degree Celsius
<p>Ranolazine bulk drug soluble in ethanol observed additional group and in isopropanol also furior transferred infrared spectroscopy of ranolazine bulk drug </p>
DOCUMENTATION OF RISIS DATASETS Doctoral Degree and Career Dataset (DDC)
<p>Documentation is presented for The Doctoral Degree and Career Dataset (DDC). In the framework of the RISIS2 project, DDC is an experimental dissertation-centric database. It primarily consists of an enriched PhD publication dataset which brings togehter information about the dissertation (e.g., topic mapping), about the degree-granting university (e.g., geolocation), as well as basic information about the individual (e.g., gender). The DDC is also leveraging linkages in the RISIS Infrastructure to develop a caeer indicator.</p> <p>This second iteration covers the PhD production for the full two cohorts (2010,2014) for six countries ( AT, DE, IL, NL, ES, NO). The documentation details the design and contents of the dataset.</p> <p> </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.