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.”
Image 3. Larsenianthus arunachalensis M in Distribution of six little known plant species from Arunachal Pradesh, India
Image 3. Larsenianthus arunachalensis M.Sabu, Sanoj & T. Rajesh Kumar
Figure 2. Four different living adult male M. personatus. 3 in Maratus personatus, a masked peacock spider from Cape Riche, Western Australia (Araneae: Salticidae: Euophryinae)
Figure 2. Four different living adult male M. personatus. 3, The scales of this male have rubbed off, exposing the black cuticle of the dorsal opisthosomal plate. 9, The small chelicerae are black and glabrous, separated on either side from a marginal band of white setae.
ds_cocci_m_w
<p>Data for 2006-2020</p> <p>Dust storm occurrences from NCEI Storm Events Database (<a href="https://www.ncdc.noaa.gov/stormevents/">https://www.ncdc.noaa.gov/stormevents/</a>), for locations in/near major population centers of Maricopa and Kern counties.</p> <p>Maricopa County, Arizona coccidioidomycosis cases from Arizona Department of Health Services.</p> <p>Kern County, California coccidioidomycosis cases from Kern County Public Health Services Department.</p> <p>See this publication for further details:</p> <p>Comrie, A.C., 2021: No Consistent Link Between Dust Storms and Valley Fever (Coccidioidomycosis). <em>GeoHealth</em> 5, e2021GH000504, <a href="https://doi.org/10.1029/2021GH000504">https://doi.org/10.1029/2021GH000504</a>.</p>
Fig. 42 in The Neotropical species of the genus Osorius G -M , 1829 with remarks to the Neotropical Osoriid complex (Coleoptera: Staphylinidae: Osoriinae)
Fig. 42: Tistribution of the large OsoriusOsorius species.
Fig. 41 in The Neotropical species of the genus Osorius G -M , 1829 with remarks to the Neotropical Osoriid complex (Coleoptera: Staphylinidae: Osoriinae)
Fig. 41:Tistribution of Osorius species with Andean and South Brazilian distribution.
Fig. 40 in The Neotropical species of the genus Osorius G -M , 1829 with remarks to the Neotropical Osoriid complex (Coleoptera: Staphylinidae: Osoriinae)
Fig. 40:Tistribution of Central American Osorius species.
FIG. 6. Sharon A in A Revision of the Didelphid Marsupial Genus Marmosa Part 3. A New Species from Western Amazonia, with Redescriptions of M. perplexa Anthony, 1922, and M. germana Thomas, 1904
FIG. 6. Sharon A. Jansa (with Marmosa robinsoni on Tobago; photo by F. Keith Barker).
FIGURE 6 in DUG¨S' CAUDATUS IS A TENUIPALPIDAE AND NOT A TYDEIDAE (ACARI) Henri M. A
FIGURE 6: Tydeus goetzi Schruft, 1972. (A) – Dorsal view, (B) – Tip of the opisthosoma in dorsal view, (C-D) – Striae density estimated by NS(ep) (C) and NS(vi) (D) (Scale bars: A: 300 µm; B – D: 20 µm – Specimens collected in Montpellier).
FIGURE 5 in DUG¨S' CAUDATUS IS A TENUIPALPIDAE AND NOT A TYDEIDAE (ACARI) Henri M. A
FIGURE 5: Tenuipalpus caudatus (DugŁs, 1834). Tarsus I: (A) – dorsal view, (B) – ventro-lateral views. Tarsus II: (C) – dorsal view, (D) – ventral view. Tarsus IV: (E) – dorsal view, (F) – ventral view. (Scale bars: A: 30 µm; B: 20 µm; C – F: 30 µm – Specimens collected in Montpellier).
FIGURE 4 in DUG¨S' CAUDATUS IS A TENUIPALPIDAE AND NOT A TYDEIDAE (ACARI) Henri M. A
FIGURE 4: Tenuipalpus caudatus (DugŁs, 1834). (A) – Protruded chelicerae and first epimera in ventral view, (B) – Palp in dorsal views, (C) - Palp in lateral views, (D) – Tenent hairs and tarsus IV in lateral view, (E) – Legs I in dorsal view, (F) Leg II in dorsal view, (G) Legs III and IV in dorsal view. (Scale bars: A – D: 20 µm; E – G: 50 µm – Specimens collected in Montpellier).
FIGURE 3 in DUG¨S' CAUDATUS IS A TENUIPALPIDAE AND NOT A TYDEIDAE (ACARI) Henri M. A
FIGURE 3: Tenuipalpus caudatus (DugŁs, 1834). Shape of setae. (A) – Simple as 1a on epimeron, (B) – Serrate as p' on leg III, (C) – Foliaceous as se, (D) – Thin as trI, (E) – Minute as vi (Scale bars: 10 µm – Specimens collected in Montpellier).
FIGURE 2 in DUG¨S' CAUDATUS IS A TENUIPALPIDAE AND NOT A TYDEIDAE (ACARI) Henri M. A
FIGURE 2: Tenuipalpus caudatus (DugŁs, 1834). (A) – Dorsal view, (B) – Genital area (C) – Partial view of prodorsum, (D) – "Tail" explain-
FIGURE 1 in DUG¨S' CAUDATUS IS A TENUIPALPIDAE AND NOT A TYDEIDAE (ACARI) Henri M. A
FIGURE 1: (A) – Trichome on the underleaf of a Viburnum tinus. (B) – A tydeid, probably a Tydeus, pupating on the leaf of a Viburnum tinus. (C) – A Tenuipalpus on the vein of a Viburnum tinus (all pictures from material collected in Montpellier).
FIGURE 1 in AVIAN FEATHER MITES (ACARI: ASTIGMATA) OF SAMSUN, TURKEY Ali T. G , Sergey V. M and Kiraz E -Y
FIGURE 1: The Kızılırmak Delta, Samsun, Turkey. The red point indicates the capture site of examined birds.
Benthic and benthopelagic Ostracoda from a wide depth range (189-6224 m) in the Atlantic Sector of the Southern Ocean
<p>This dataset contains abundance (i.e., absolute number of speicmens/individuals) of ostracod genera collected during three cruises of the German Research Vessel Polarstern in the Atlantic Sector of the Southern Ocean. The cruises are EASIZ II (PS 48/ANT XV-3), ANDEEP I (PS 61/ANT XIX-3), ANDEEP II (PS 67/ANTXXII-3), and ANDEEP III (PS 67/ANTXXII-4). The samples related to the present file were all collected using an epibenthic sledge, described by (Brandt & Barthel, 1995; Brenke, 2005, see compelte references in Brandão et al., 2021, Zoological Journal of the Linnean Society). These ostracods are both benthic (most specimens collected) and bentho-pelagic (Halocyprididae, which in the case of the present dataset, swam just above the sea floor).</p>
Nicosia, Bedestan. Plan of the complex prepared in 1980-81 combined with resistivity tomography at a depth of 1.25m below the current surface with hypothetical reconstruction of the early Byzantine church.
<p>Nicosia, Bedestan. Plan of the complex prepared in 1980-81 combined with resistivity tomography at a depth of 1.25m below the current surface with hypothetical reconstruction of the early Byzantine church.</p>
Nicosia, Bedestan. Plan of the complex prepared in 1980-81 combined with resistivity tomography at a depth of 1.0m below the current surface with hypothetical reconstruction of the middle Byzantine church
<p>Nicosia, Bedestan. Plan of the complex prepared in 1980-81 combined with resistivity tomography at a depth of 1.0m below the current surface with hypothetical reconstruction of the middle Byzantine church. Drawing by M. Wills, M. Cozzolino and Vicki Herring.</p>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
Three-dimensional dataset of hydrating cement paste (CEM I Ladce, 273 m^2/kg Blaine, w/c=0.50) in TIFF format
<p>A detailed description of this dataset can be found in <a href="https://doi.org/10.1016/j.dib.2023.108903">https://doi.org/10.1016/j.dib.2023.108903</a><a href="https://doi.org/10.1016/j.dib.2023.108903">.</a></p> <p>This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.</p> <p>The dataset contains two sets of images:</p> <p>- “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)</p> <p>- smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm<sup>3</sup> constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see <a href="https://doi.org/10.1016/j.cemconcomp.2022.104798">10.1016/j.cemconcomp.2022.104798</a> for details.</p> <p>Note that the same dataset stored in *raw format is available from <a href="https://doi.org/10.5281/zenodo.7193819">https://doi.org/10.5281/zenodo.7193819</a></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.