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.
15,560
datasets available to search
ShareScore release 0.9.0
Dataset results
15,560 results for “How long”
FIGURE 2. Terrapotamon longitarsus n in A new species of long-legged terrestrial Terrapotamon Ng, 1986 (Crustacea: Brachyura: Potamidae) from limestone formations in Satun, southern Thailand
FIGURE 2. Terrapotamon longitarsus n. sp. Holotype male (47.4 × 38.6 mm) (ZRC 2016.0161). A, right third maxilliped; B, ventral view of cephalothorax showing posterior thoracic sternum and pleon (right margin of somite 3 damaged); C, right ambulatory legs; D, dorsal view of right cheliped; E, outer view of right chela.
Black Sea thermohaline properties: Long-term trends and variations
<p>3D velocity (m/s), temperature (°C) and salinity fields for the Black Sea are simulated by the use of the General Estuarine Ocean Model (GETM) and General Ocean Turbulence Model (GOTM). Data sets consists of monthly mean value on a horizontal grid of 423x172 data points (2 longitude minutes x 2 latitude minutes) and 70 vertical levels. Simulations are performed for the period from 1960 to 2015 (56 years). The 3D monthly mean vertical coordinate, temperature and salinity are stored in monthly NetCDF data files available for download.</p> <p>Detailed description: The 3D hydrodynamic model comprises of 3D GETM and 1D GOTM initialized on high resolution 2 x 2 min latitude–longitude horizontal grid. The model bathymetry grid is produced from ETOPO1 global bathymetric grid with horizontal resolution of 1 min. Linear programming procedure was applied to smooth slightly the bathymetry. The maximum depth of the model domain is 2200 m with a 70 levels general vertical grid which is compressed towards the surface. A detailed description of the GETM equations can be found in Stips et al. [2004].One way to minimize dissipation and dispersion is to use a numerical method which satisfies the Total Variation Diminishing (TVD) property. Flux-limiter methods satisfy the TVD property and switch between a second-order approximation when the field is smooth and a first-order approximation when it is near a discontinuity. Flux-limiter methods have been applied to the different numerical approaches so that oscillations present in the numerical solution can be minimized. The second-order monotone scheme with the Superbee limiter is used herein [Burchard and Bolding 2002]. The meteorological forcing from the European Centre for Medium Range Weather Forecast (ECMWF) available from http://www.ecmwf.int, has been applied, namely, ERA-40 project (1958-2001) and ERA-Interim project (1979-2015). Two model runs have been chosen: Run1 with ERA-40 (1958-1979) followed by forcing with ERA-Interim (1980-2015) and Run2 forced with ERA-Interim (1979-2015), in order to study the effect of the starting year and forcing data and to identify possible artificial trends due to computational and forcing uncertainties. Freshwater input has been evaluated using the values from the Global Runoff Data Centre (GRDC, http://www.bafg.de/GRDC) runoff. Being an estuarine basin, the Black sea is very sensitive to variations in the fresh water input. The resulting buoyancy flow induced by the river runoff is essential for establishing the basin circulation. Comparison between runoff data sets from different data centres has revealed similar climatological mean annual cycles for all rivers considered herein [Miladinova-Marinova et al., 2016]. The mode has been forced the GRDC data because it contains long term daily records of the Danube River. Water exchange in the Bosphorus and Kerch Straits is simulated as a river flow that contains surface outflow/inflow and bottom inflow/outflow. Assuming the long term steady state water and salt budgets in the Black Sea, the monthly averaged volume fluxes have been estimated [Miladinova-Marinova et al., 2016] and used further as a forcing condition. The model is initialized by means of temperature and salinity 3D fields coming from the project MEDAR/MEDATLAS II (http://www.ifremer.fr/medar). The MEDAR data set for the Black Sea reflects the main features known from observations – the strong halocline at 70-150 m, the CIL at approximately 25-70 m and the doming of the isohalines due to the cyclonic Rim current. The detailed model setup and an extended validation is presented in Miladinova-Marinova et al. [2016]. Burchard, H., and K. Bolding (2002), Getm: A general estuarine transport model. Scientific documentation, Joint Research Centre Ispra Tech. Rep. EUR 20253 EN, Eur. Comm; Stips, A., K. Bolding, T. Pohlmann, and H. Burchard (2004), Simulating the temporal and spatial dynamics of the North Sea using the new model GETM (General Estuarine Transport Model), Ocean Dynam., 54, 266-283; Miladinova-Marinova S., A. Stips, E. Garcia-Gorriz, D. Macias Moy (2016), Black Sea ecosystem model: setup and validation, EUR 27786, doi: 10.2788/601495</p> <p>Simulation zip files from Run1, BLACK_SEA_HYDRO1_YEAR_MONTH.zip, and simulation zip files from Run2, BLACK_SEA_HYDRO2_YEAR_MONTH.zip, are stored herein.</p>
Tandem repeat catalog of the human genome generated from long-read assemblies
<p>Allele sequences of polymorphic loci (VCF) and README for all version 2 (2.0 + 2.1) files</p>
Dataset of physical, biological and chemical soil properties from 10 European long-term experiments
<p>This dataset contains all measurements that were conducted within the Workpackage #2 of the SoilX Project (2022-2024). The data contains physical, chemical and biological soil parameters that were measured in ten European long-term field experiments, as well as soil management indicators calculated with the SoilManageR packager for R. Each data table contains different parameters, and they can be linked by the identifying columns (LTE, treatment, depth, block, replicate). Further information can be found in the ReadMe.txt.</p> <pre> </pre>
The PODER cross-ancestry long-read RNA-seq dataset
<p>Table legends:</p> <ul> <li>column_descriptions.docx: Extended description of columns in selected tables.</li> <li>00_sample_metadata: Sample and experimental metadata pertaining to each LR-RNA-seq sample.</li> <li>01_uma_gtf: Unfiltered merged annotation (UMA) GTF for all spliced intron chains discovered by each of the tools.</li> <li>02_uma_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in the UMA annotation used to filter transcripts.</li> <li>03_poder_gtf: Final PODER GTF, including predicted CDSs for novel transcripts and annotated CDSs for known transcripts.</li> <li>04_poder_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in PODER.</li> <li>06_poder_t_counts: PODER transcript counts in each sample as quantified by lr-kallisto.</li> <li>07_poder_g_counts: PODER gene counts in each sample as quantified by lr-kallisto.</li> <li>08_mage_t_counts_poder: Transcript counts for the MAGE RNA-seq dataset computed using the PODER annotation with kallisto.</li> <li>09_mage_t_counts_gencode: Transcript counts for the MAGE RNA-seq dataset computed using the GENCODE annotation with kallisto.</li> <li>10_mage_t_counts_enh: Transcript counts for the MAGE RNA-seq dataset computed using the Enhanced GENCODE annotation with kallisto.</li> <li>11_astu: Allele-specific transcript usage results.</li> <li>12_ase: Allele-specific expression results.</li> <li>13_mage_tau: Tau values for Enhanced GENCODE transcripts computed on the MAGE RNA-seq dataset.</li> <li>14_gwas_enrichments: GWAS enrichment results for ASTU genes overlapping GWAS genes.</li> <li>16_inter_catalog_overlap: Boolean detection of transcripts across PODER, annotations (GENCODE, RefSeq) and other RNA-seq / LR-RNA-seq-derived transcript catalogs (CHESS, GTEx, ENCODE4).</li> <li>17_personalized_hg38: Isoforms and their intron chains detected using personalized-GRCh38s.</li> <li>18_enh_gencode: Enhanced GENCODE GTF with novel transcripts from PODER added to all annotated transcripts from GENCODE v47</li> </ul>
Supplemental Figures for: "The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves"
<p>Additional figures for the paper The SDSS-V Black Hole Mapper Reverberation Mapping Project: Multi-Line Dynamical Modeling of a Highly Variable Active Galactic Nucleus with Decade-long Light Curves. </p> <h2> </h2> <h2>Interactive Figure Data</h2> <p>Data files used to create the intreactive version of Figure 5 in the publication. There is a version of each file for each line species in the plot (i.e., Hα, Hβ, and MgII).</p> <p><strong>clouds_{line_name}.csv</strong>: A CSV file containing the cloud positions, line-of-sight velocities, and weights. The columns of the file are x [light-day], y [light-day], z [light-day], velocity [km/s], and weight.</p> <p><strong>transfer_function_velocity_{line_name}.csv</strong>: A CSV file containing x-axis of the transfer function panels, the rest-frame velocity.</p> <p><strong>transfer_function_tau_{line_name}.csv</strong>: A CSV file containing the y-axis of the transfer function panels, the rest-frame time delay τ in days.</p> <p><strong>transfer_function_{line_name}.csv</strong>: A CSV file containing the transfer function <span lang="el">Ψ.</span></p> <p> </p> <h2>Model-Related Figures</h2> <p><strong>fitplot_low.pdf</strong>: Same as Figure 4 in the publication, but for the low state.</p> <p><strong>fitplot_high.pdf</strong>: Same as Figure 4 in the publication, but for the high state.</p> <p><strong>geoplot_low.pdf</strong>: Same as Figure 5 in the publication, but for the low state.</p> <p><strong>geoplot_high.pdf</strong>: Same as Figure 5 in the publication, but for the high state.</p> <p><strong>lagplot_low.pdf</strong>: Same as Figure 6 in the publication, but for the low state.</p> <p><strong>lagplot_high.pdf</strong>: Same as Figure 6 in the publication, but for the high state. </p> <p> </p> <h2>Spectral Reduction Method Comparison</h2> <p><strong>spec_decomp_pyqsofit.pdf</strong>: A figure showing the spectral decomposition performed in PyQSOFit for the processed line profiles for Hβ, Hα, and MgII for an example epoch. The total spectrum is shown in black, and each of the decomposed elements are shown, color-coded using the legend above the three panels.</p> <p><strong>input_method_comp.pdf</strong>: A figure showing the processed multi-epoch line profiles for each spectral reduction method (PyQSOFit and PrepSpec). Each column corresponds to a given line (labeled above), and each row corresponds to a given spectral reduction method (labeled on the right). Note that the scales for each panel are different.</p> <p> </p> <h2>Published Value Comparison</h2> <p><strong>pubval_table.pdf</strong>: A table comparing the values obtained for certain physically relevant parameters obtained from our BRAINS modeling to those obtained in Shen et al. (2024). </p> <p> </p> <h2>Joint Posterior Analysis</h2> <p><strong>joint_line_posterior_table.pdf</strong>: A table containing the median values (and their uncertainties) extracted from the joint posteriors for a few key model parameters. These joint posteriors are produced for a given state, across all line species. </p> <p> </p> <h2>Virial Factor Analysis</h2> <p><strong>fcomp.pdf</strong>: A comparison of the virial factor values obtained by using the line dispersion (σ) and FWHM of each of the lines in each of the states.</p> <p><strong>fcorr_table.pdf</strong>: A table showing the correlations between the virial factor and model parameters (i.e., the slopes obtained using <a href="https://github.com/jmeyers314/linmix">LinMix</a> assuming a linear relationship, and the correlation coefficients). Values are given for virial factors obtained using both the line dispersion (σ) and FWHM.</p>
Fig. 10 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 10. Cheiracanthium xinjiangense sp. nov. A–B. ♀, paratype (MHBU-ARA-1014). C–E. ♂, holotype (MHBU-ARA-1013). A. Epigyne, ventral view. B. Vulva, dorsal view. C. Left palp, prolateral view. D. Same, ventral view. E. Same, retrolateral view. Abbreviations: see Material and methods.
Fig. 9 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 9. Cheiracanthium xinjiangense sp. nov. A. ♂, holotype (MHBU-ARA-1013), habitus, dorsal view. B. ♀, paratype (MHBU-ARA-1014), habitus, dorsal view.
Fig. 7 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 7. Cheiracanthium jiuquan sp. nov. A–B. ♂, holotype (MHBU-ARA-1009). C–D. ♀, paratype (MHBU-ARA-1010). A, C. Habitus, dorsal view. B, D. Habitus, ventral view.
Fig. 6 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 6. Cheiracanthium digitatum sp. nov. A–B. ♀, paratype (MHBU-ARA-1008). C–E. ♂, holotype (MHBU-ARA-1007). A. Epigyne, ventral view. B. Vulva, dorsal view. C. Left palp, prolateral view. D. Same, ventral view. E. Same, retrolateral view. Abbreviations: see Material and methods.
Fig. 8 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 8. Cheiracanthium jiuquan sp. nov. A–B. ♀, paratype (MHBU-ARA-1010). C–E. ♂, holotype (MHBU-ARA-1009). A. Epigyne, ventral view. B. Vulva, dorsal view. C. Left palp, prolateral view. D. Same, ventral view. E. Same, retrolateral view. Abbreviations: see Material and methods.
Fig. 5 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 5. Cheiracanthium digitatum sp. nov. A. ♂, holotype (MHBU-ARA-1007), habitus, dorsal view. B. ♀, paratype (MHBU-ARA-1008), habitus, dorsal view.
Fig. 3 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 3. Cheiracanthium circulum sp. nov. A–B. ♂, holotype (MHBU-ARA-1005). C–D. ♀, paratype (MHBU-ARA-1006). A, C. Habitus, dorsal view. B, D. Habitus, ventral view.
Fig. 4 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 4. Cheiracanthium circulum sp. nov. A–B. ♀, paratype (MHBU-ARA-1006). C–E. ♂, holotype (MHBU-ARA-1005). A. Epigyne, ventral view. B. Vulva, dorsal view. C. Left palp, prolateral view. D. Same, ventral view. E. Same, retrolateral view. Abbreviations: see Material and methods.
Fig. 2 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 2. Cheiracanthium arcilongum sp. nov., ♀, paratype (MHBU-ARA-1003). A. Habitus, dorsal view. B. Same, ventral view. C. Epigyne, ventral view. D. Vulva, dorsal view. Abbreviations: see Material and methods.
Fig. 1 in Five new species of the long-legged sac spider genus Cheiracanthium C.L. Koch, 1839 (Araneae: Cheiracanthiidae) from China
Fig. 1. Cheiracanthium arcilongum sp. nov., ♂, holotype (MHBU-ARA-1001). A. Habitus, dorsal view. B. Same, ventral view. C. Left palp, retrolateral view. D. Same, prolateral view. E. Same, ventral view. F. Same, dorsal view. Abbreviations: see Material and methods.
The first 500-meter, long-term winter wheat grain protein content dataset for China from multi-source data
<p>In China, the demand for precise perception of wheat Grain Protein Content (GPC) has gained increased urgency, driven by the rising demands in the food consumption market and intensifying international market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diversified geographical landscape in China introduces spatiotemporal heterogeneity and intricacy to the influence of wheat GPC, further amplifying the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS.</p>
Moreno et al_2023_Long-term_SDGsEU_Dataset
<p>This dataset contains the underlying IAM output data for the journal article by Moreno et al. currently in revision in Nature Communications Earth and Environment titled "Long-term sustainable development prospects of EU decarbonisation pathways: a multi-model study".</p>
Photosynthesis in newly-developed leaves of heat-tolerant wheat acclimates to long-term nocturnal warming
<p>We examined photosynthetic capacity of newly-developed and pre-existing flag leaves of four wheat genotypes under three night temperatures (15, 20 and 25 °C) and common day temperature of 26 °C in two controlled environment experiments. In newly-developed leaves which acclimated (i.e. maintained or increased) the maximum rate of net CO<sub>2</sub> assimilation (<em>A</em><sub>n</sub>) to long-term (9–13 weeks) nocturnal warming, acclimation was underpinned by greater capacity of Rubisco carboxylation (<em>V</em><sub>cmax</sub>) and photosynthetic electron transport (<em>J</em>). This indicates a night-dependent temperature sensitivity of the activation state of Rubisco. Metabolite profiling linked acclimation of <em>A</em><sub>n</sub> to greater accumulation of monosaccharides and saturated fatty acids in leaves, suggesting roles for osmotic adjustment of leaf turgor pressure and maintenance of cell membrane integrity. By contrast, warm night-induced inhibition of <em>A</em><sub>n</sub> was related to reductions in stomatal conductance of CO<sub>2</sub> and <em>J</em>, despite higher basal electron transport thermal stability: <em>T</em><sub>crit</sub> 51 of 45–46.5 °C in non-acclimated versus <em>T</em><sub>crit</sub> of 43.8–45 °C in acclimated leaves. Pre-existing leaves exposed to short-term nocturnal warming (5–7 nights) showed no change in instantaneous temperature responses of <em>A</em><sub>n</sub> and photosynthetic capacity, except for an elite heat-tolerant genotype. These findings can be used to support strategies for developing climate-resilient wheat.</p>
Figures 2-6. Calusamyia hribari. 2 in A New Genus and Species of North American Robsonomyiini (Diptera: Sciaroidea: Keroplatidae: Macrocerinae) from the Florida Keys Edward I. Coher Emeritus Prof. Long Island Univ. 10203 Greentrail Drive N. Boynton Beach, FL 33436
Figures 2-6. Calusamyia hribari. 2) Wing. 3) Thorax, lateral. 4) Male terminalia, dorsal view. 5) Female terminalia, ventral view. 6) Female terminalia, lateral view.
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.