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.
26,784
datasets available to search
ShareScore release 0.9.0
Dataset results
26,784 results for “M.”
Figure 9. Spinnerets. A–E, Metabus ocellatus. A, female ALS. B, female PMS. C, female PLS. D, male PLS. G, epiandrous fusules. F–H, Allende longipes. F, female ALS. G, female PMS. H, female PLS. I–J, A. nigrohumeralis. I, male PLS. J, epiandrous fusules. K–O, Mollemeta edwardsi. K, female ALS. L, female PMS. M, female PLS. N, male PLS. O in Systematics of the spider genus Metabus O. P.-Cambridge, 1899 (Araneoidea: Tetragnathidae) with additions to the tetragnathid fauna of Chile and comments on the phylogeny of Tetragnathidae
Figure 9. Spinnerets. A–E, Metabus ocellatus. A, female ALS. B, female PMS. C, female PLS. D, male PLS. G, epiandrous fusules. F–H, Allende longipes. F, female ALS. G, female PMS. H, female PLS. I–J, A. nigrohumeralis. I, male PLS. J, epiandrous fusules. K–O, Mollemeta edwardsi. K, female ALS. L, female PMS. M, female PLS. N, male PLS. O, epiandrous fusules.
Figure 19. Allende nigrohumeralis. A, female habitus. B, female frontal view. C–G, epigynum. C, caudal view. D, lateral view. E, ventral view. F, caudal view cleared. G, dorsal view cleared. H, male habitus. I, male frontal view. J–P, male pedipalp. J, expanded. K, ventral view. L, dorsal view. M, expanded. N, conductor detail. P, ectal view. Scale bars 0.2 in Systematics of the spider genus Metabus O. P.-Cambridge, 1899 (Araneoidea: Tetragnathidae) with additions to the tetragnathid fauna of Chile and comments on the phylogeny of Tetragnathidae
Figure 19. Allende nigrohumeralis. A, female habitus. B, female frontal view. C–G, epigynum. C, caudal view. D, lateral view. E, ventral view. F, caudal view cleared. G, dorsal view cleared. H, male habitus. I, male frontal view. J–P, male pedipalp. J, expanded. K, ventral view. L, dorsal view. M, expanded. N, conductor detail. P, ectal view. Scale bars 0.2 mm, except in habitus 1 mm.
Figure 17. Allende patagiatus. A, female habitus. B, female frontal view. C–G, epigynum. C, ventral view. D, caudal view. E, lateral view. F, caudal view cleared. G, dorsal view cleared. H, male habitus. I, male frontal view. J–M, male pedipalp. J, ectal view. K, conductor detail. L, dorsal view. M, ventral view. Scale bars 0.2 in Systematics of the spider genus Metabus O. P.-Cambridge, 1899 (Araneoidea: Tetragnathidae) with additions to the tetragnathid fauna of Chile and comments on the phylogeny of Tetragnathidae
Figure 17. Allende patagiatus. A, female habitus. B, female frontal view. C–G, epigynum. C, ventral view. D, caudal view. E, lateral view. F, caudal view cleared. G, dorsal view cleared. H, male habitus. I, male frontal view. J–M, male pedipalp. J, ectal view. K, conductor detail. L, dorsal view. M, ventral view. Scale bars 0.2 mm, except in habitus 1 mm.
Figure 12. Placoid scales. A–D, morphotype 1. A–B, A, occlusal view. B, lateral view. C–D, C, occlusal view. D, lateral view. E–H, morphotype 2. E, occlusal view. F, lateral view. G, posterior view. H, anterior view. I–L, morphotype 3. I, occlusal view. J, lateral view. K, posterior. L, anterior. M–P, morphotype 4. M, occlusal view. N, lateral view. O, posterior view. P, anterior view. Q–U, morphotype 5. Q, occlusal view. R, lateral view. S, lateral view. T, posterior view. U, anterior view. V–Y, morphotype 6. V, anterior view. W, lateral view. X, occlusal view. Y, posterior view. All scale bars equal 0.5 in Neoselachians (Chondrichthyes, Elasmobranchii) from the Lower and lower Upper Cretaceous of north-eastern Spain
Figure 12. Placoid scales. A–D, morphotype 1. A–B, A, occlusal view. B, lateral view. C–D, C, occlusal view. D, lateral view. E–H, morphotype 2. E, occlusal view. F, lateral view. G, posterior view. H, anterior view. I–L, morphotype 3. I, occlusal view. J, lateral view. K, posterior. L, anterior. M–P, morphotype 4. M, occlusal view. N, lateral view. O, posterior view. P, anterior view. Q–U, morphotype 5. Q, occlusal view. R, lateral view. S, lateral view. T, posterior view. U, anterior view. V–Y, morphotype 6. V, anterior view. W, lateral view. X, occlusal view. Y, posterior view. All scale bars equal 0.5 mm.
Data supporting "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study"
<p>Data and code supporting the article "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study".</p> <p> </p>
ESA WorldCover 10 m 2020 v100
<p><strong>ESA WorldCover 10 m 2020 v100</strong></p> <p>The European Space Agency (ESA) WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with UN-FAO's Land Cover Classification System, and has been generated in the framework of the ESA WorldCover project.</p> <p>The WorldCover product is developed by a consortium lead by VITO Remote Sensing together with partners Brockmann Consult, CS SI, Gamma Remote Sensing AG, IIASA and Wageningen University</p> <p><a href="https://viewer.esa-worldcover.org/worldcover/">Click here to view the maps</a></p> <p><a href="https://esa-worldcover.org">More information about the land cover maps</a></p> <p><a href="https://esa-worldcover.org/en/data-access">Product User Manual & Product Validation Report</a></p>
ESA WorldCover 10 m 2021 v200
<p><strong>ESA WorldCover 10 m 2021 v200</strong></p> <p>The European Space Agency (ESA) WorldCover 10 m 2021 product provides a global land cover map for 2021 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with UN-FAO's Land Cover Classification System, and has been generated in the framework of the ESA WorldCover project.</p> <p>The ESA WorldCover 10m 2021 v200 product updates the existing <a href="https://doi.org/10.5281/zenodo.5571936">ESA WorldCover 10m 2020 v100</a> product to 2021 but is produced using an improved algorithm version (v200) compared to the 2020 map. Consequently, since the <strong>WorldCover maps for 2020 and 2021 were generated with different algorithm versions</strong> (v100 and v200, respectively), <strong>changes between the maps</strong> should be treated with caution, as they i<strong>nclude both real changes in land cover and changes due to the algorithms used.</strong></p> <p>The WorldCover 2021 v200 product is developed by a consortium lead by VITO Remote Sensing together with partners Brockmann Consult, Gamma Remote Sensing AG, IIASA and Wageningen University</p> <p><a href="https://viewer.esa-worldcover.org/worldcover/">Click here to view the maps</a></p> <p><a href="https://esa-worldcover.org">More information about the land cover maps</a></p> <p><a href="https://esa-worldcover.org/en/data-access">Product User Manual & Product Validation Report</a></p>
PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTDOC Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTDOC instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTGJON Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTGJON instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Apache Point Observatory 3.5m Agile Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Agile instrument on the Apache Point Observatory 3.5m telescope. We use principal component analysis (PCA) filtering both to improve the coregistration of the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of MMT Observatory 6.5m CCD47 Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the CCD47 instrument on the MMT Observatory 6.5m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
20 m Annual Paddy Rice Map for Mainland Southeast Asia Using Sentinel-1 SAR Data
<p>This dataset provides 20 m annual paddy rice map for mainland Southeast Asia since 2019 .</p> <p>*** The data file is in “.tif" format</p> <p>*** Pixel size: 20 m</p> <p>*** Projection information: EPSG: 4326 (WGS84)</p> <p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
Figures Į–Į0. Marimatha adults. Į M. nigrofimbria ♂, 1.4 mi WSW Anthony, Marion Co., Florida 2 M. nigrofimbria ♀, Big Cypress Natute Preserve, Collier Co., Florida 3 M. squala ♂, Madera Canyon 4880', Santa Rita Mts., Santa Cruz Co., Arizona 4 M. squala ♀, Madera Canyon 4880', Santa Rita Mts., Santa Cruz Co., Arizona 5 M. quadrata ♂, Madera Canyon 3800', Santa Rita Mts., Pima Co., Arizona 6 M. quadrata ♀, Concan, Uvalde Co., Texas 7 M. tripuncta ♂, Fuchs Hammock near Homestead, Dade Co., Florida 8 M. tripuncta ♀, Fuchs Hammock near Homestead, Dade Co., Florida 9 M. piscimala ♂, Brown Canyon, Baboquivari Mts., Pima Co., Arizona Į0 M. piscimala ♀, Mission, Hidalgo Co., Texas. in Review of the North American species of Marimatha Walker with descriptions of three new species (Lepidoptera, Noctuidae, Eustrotiinae) and the description of Pseudomarimatha flava (Noctuinae, Elaphriini), a new genus and species confused with Marimatha
Figures Į–Į0. Marimatha adults. Į M. nigrofimbria ♂, 1.4 mi WSW Anthony, Marion Co., Florida 2 M. nigrofimbria ♀, Big Cypress Natute Preserve, Collier Co., Florida 3 M. squala ♂, Madera Canyon 4880', Santa Rita Mts., Santa Cruz Co., Arizona 4 M. squala ♀, Madera Canyon 4880', Santa Rita Mts., Santa Cruz Co., Arizona 5 M. quadrata ♂, Madera Canyon 3800', Santa Rita Mts., Pima Co., Arizona 6 M. quadrata ♀, Concan, Uvalde Co., Texas 7 M. tripuncta ♂, Fuchs Hammock near Homestead, Dade Co., Florida 8 M. tripuncta ♀, Fuchs Hammock near Homestead, Dade Co., Florida 9 M. piscimala ♂, Brown Canyon, Baboquivari Mts., Pima Co., Arizona Į0 M. piscimala ♀, Mission, Hidalgo Co., Texas.
A 10 m land mask for the Nuup Kangerlua, Kobbefjord, and Ameralik fjord systems in southwest Greenland
<p>This dataset consists of a 10 m land mask of the Nuup Kangerlua, Kobbefjord, and Ameralik fjord systems in southwest Greenland. The land mask is based on two Sentinel-2 MSI images of the area (tiles WDS and WES) that were acquired at high tide on 30 July 2017. Land was masked by applying a threshold of 0.1 Wm<sup>-2</sup> to the short-wave infrared (SWIR) band at 1614 nm. Mountain lakes and shaded coastal regions were masked using the TanDEM-X digital elevation model. Some inland waters were masked manually. </p> <p>Land and water were assigned values of 0 and 1, respectively, and are provided in geotiff format projected to UTM Zone 22 (WGS 84).</p> <p>The boundaries of the mask area extend from 63.90°N, 51.999°W in the southwest to 64.01°N, 51.302°W in the northeast.</p>
Data and Code for figures of "Long-range transport and fate of DMS-oxidation products in the free troposphere derived from observations at the high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes"
<p>This database includes the material to create the figures in "Measurement Report: Long-range transport and fate of DMS-oxidation products in the free troposphere derived from observations at the high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes" and the analyzed time series of all atmospheric variables presented.</p>
M. Kelly PhD thesis; Chapter 2 - Supplemental Table S1
<p>Supplemental Table for my PhD thesis (Chapter 2). Supplemental Table S1 contains the sample data for all specimens used in this study.</p>
M. Kelly PhD thesis; Chapter 3 - Supplemental Table S1
<p>Supplemental Table for my PhD thesis (Chapter 3). Supplemental Table S1 contains all collection and processing data for the specimens used in this study.</p>
M. Kelly PhD thesis; Chapter 2 - Supplemental Table S2
<p>Supplemental Table for my PhD thesis (Chapter 2). Supplemental Table S2 contains the biometric data for all specimens used in this study.</p>
Data set for publication Sikora P., Techman M., Federowicz K., El-Khayatt A.M., Saudi H.A., Abd Elrahman M., Hoffmann M., Stephan D., Chung S.-Y. Insight into the microstructural and durability characteristics of 3D printed concrete: Cast versus printed specimens. Case Studies in Construction Materials (2022), 17, e01320.
<p>Open dataset for publication Sikora P., Techman M., Federowicz K., El-Khayatt A.M., Saudi H.A., Abd Elrahman M., Hoffmann M., Stephan D., Chung S.-Y. Insight into the microstructural and durability characteristics of 3D printed concrete: Cast versus printed specimens. <strong>Case Studies in Construction Materials (2022)</strong>, 17, e01320. <a href="https://doi.org/10.1016/j.cscm.2022.e01320">https://doi.org/10.1016/j.cscm.2022.e01320</a></p> <p>File 1 - Mechanical characteristics - *.opju (Origin)</p> <p>File 2 - Particle size distributions of used materials - *.opju (Origin)</p> <p>File 3 - Sorptivity measurement data - *.opju (Origin)</p> <p>File 4 - G-code for printing of 1 layered specimen - *txt</p> <p>File 5 - G-code for printing of 3 layered specimens - *txt</p>
Text-fig. 3. Synchrotron radiation X-ray tomographic microscopy (SRXTM) images of fruits of Canrightia foveolata sp. nov.; Catefica locality, Portugal. a) Volume rendering of fruit showing prominent rim around the middle of the fruit with reduced tepals (arrowheads) and partly abraded fruit wall exposing the pitted endotesta surface of one of two seeds (arrow); note two of the vascular bundles (vb) extending from the base of the fruit to the tepals; b) Voltex of fruit showing prominent rim around the fruit (arrowhead) and dense precipitation of crystals in the endothelium cells of one of the two seeds in the fruit; c) Longitudinal section of fruit (orthoslice yz0520) showing the inferred hypanthium rim (arrow head) and two seeds, one with a dense precipitation of crystals; note the prominent endothelium cells (asterisks) of the inner integument and the well-developed fruit wall above the seeds; d) Transverse section through basal part of fruit and seeds close to the micropyle (orthoslice xy0312) showing partly abraded fruit wall with five vascular bundles (vb) and details of the seed coat with endotesta (oi-end) surrounding the tegmen consisting of an outer epidermis (ii-o), middle layer (ii-m) and a distinct inner epidermis (endothelium) consisting of radially elongated cells (asterisk); e) Transverse section (orthoslice xy1680) through apical part of the fruit close to chalaza showing the tips of two seeds; note the endotesta (oi-end) surrounded by thick-walled cells of the exotesta (oi-o); f) Transverse section (orthoslice xy1485) through fruit in the region of the hypanthium rim showing sections through the two seeds close to the chalazal region; note endotesta (oi-end) surrounded by larger cells of exotesta (oi-o) and fruit wall (fr). Specimen, Catefica 49-S174249 (holotype, a–f). Scale bars = 300 Μm (a–c, e, f), 100 Μm (d). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 3. Synchrotron radiation X-ray tomographic microscopy (SRXTM) images of fruits of Canrightia foveolata sp. nov.; Catefica locality, Portugal. a) Volume rendering of fruit showing prominent rim around the middle of the fruit with reduced tepals (arrowheads) and partly abraded fruit wall exposing the pitted endotesta surface of one of two seeds (arrow); note two of the vascular bundles (vb) extending from the base of the fruit to the tepals; b) Voltex of fruit showing prominent rim around the fruit (arrowhead) and dense precipitation of crystals in the endothelium cells of one of the two seeds in the fruit; c) Longitudinal section of fruit (orthoslice yz0520) showing the inferred hypanthium rim (arrow head) and two seeds, one with a dense precipitation of crystals; note the prominent endothelium cells (asterisks) of the inner integument and the well-developed fruit wall above the seeds; d) Transverse section through basal part of fruit and seeds close to the micropyle (orthoslice xy0312) showing partly abraded fruit wall with five vascular bundles (vb) and details of the seed coat with endotesta (oi-end) surrounding the tegmen consisting of an outer epidermis (ii-o), middle layer (ii-m) and a distinct inner epidermis (endothelium) consisting of radially elongated cells (asterisk); e) Transverse section (orthoslice xy1680) through apical part of the fruit close to chalaza showing the tips of two seeds; note the endotesta (oi-end) surrounded by thick-walled cells of the exotesta (oi-o); f) Transverse section (orthoslice xy1485) through fruit in the region of the hypanthium rim showing sections through the two seeds close to the chalazal region; note endotesta (oi-end) surrounded by larger cells of exotesta (oi-o) and fruit wall (fr). Specimen, Catefica 49-S174249 (holotype, a–f). Scale bars = 300 Μm (a–c, e, f), 100 Μm (d).
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.