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.
12,237
datasets available to search
ShareScore release 0.9.0
Dataset results
12,237 results for “2019”
FIGURES 23–26. Matinta spp. 23–24 in On the taxonomy of the jumping spider genus Matinta Ruiz & Maddison, 2019, with a taxonomic revision of the vicana species-group (Araneae: Salticidae: Amycini)
FIGURES 23–26. Matinta spp. 23–24 Matinta fonsecai Soares & Camargo, male (23 dorsal view; 24 ventral view). 25–26 Matinta vicana Simon, male (25 dorsal view; 26 ventral view).
FIGURES 31–43. Matinta spp., left male palps. 31–33 M in On the taxonomy of the jumping spider genus Matinta Ruiz & Maddison, 2019, with a taxonomic revision of the vicana species-group (Araneae: Salticidae: Amycini)
FIGURES 31–43. Matinta spp., left male palps. 31–33 M. vicana Simon (31 dorsal view; 32 ventral view; 33 retrolateral view). 34–35 M. pereirae sp. nov. (34 ventral view; 35 retrolateral view). 36–37 M. maddisoni sp. nov. (36 ventral view; 37 retrolateral view). 38–39 M. silvae Crane (38 ventral view; 39 retrolateral view). 40–41 M. fonsecai Soares & Camargo (40 ventral view; 41 retrolateral view). 42–43 M. aragog sp. nov. (42 ventral view; 43 retrolateral view).
FIGURES 19–22 in On the taxonomy of the jumping spider genus Matinta Ruiz & Maddison, 2019, with a taxonomic revision of the vicana species-group (Araneae: Salticidae: Amycini)
FIGURES 19–22. Matinta maddisoni sp. nov., 19–20 Male (19 dorsal view; 20 ventral view). 21–22 Female (21 dorsal view; 22 ventral view).
FIGURES 15–18. Matinta silvae Crane. 15–16 in On the taxonomy of the jumping spider genus Matinta Ruiz & Maddison, 2019, with a taxonomic revision of the vicana species-group (Araneae: Salticidae: Amycini)
FIGURES 15–18. Matinta silvae Crane. 15–16 Male (15 dorsal view; 16 ventral view). 17–18 Female (17 dorsal view; 18 ventral view).
FIGURES 11–14. Matinta opiparis Simon. 11–13 in On the taxonomy of the jumping spider genus Matinta Ruiz & Maddison, 2019, with a taxonomic revision of the vicana species-group (Araneae: Salticidae: Amycini)
FIGURES 11–14. Matinta opiparis Simon. 11–13 Left male palp (11 ventral view; 12 retrolateral view; 13 detail of embolus, ventral view). 14 Epigyne, ventral view.
FIGURES 5–10. Matinta opiparis Simon. 5–8 in On the taxonomy of the jumping spider genus Matinta Ruiz & Maddison, 2019, with a taxonomic revision of the vicana species-group (Araneae: Salticidae: Amycini)
FIGURES 5–10. Matinta opiparis Simon. 5–8 Left male palp (5 prolateral view; 6 ventral view; 7 retroventral view; 8 retrolateral view). 9–10 Epigyne (9 ventral view; 10 cleared, dorsal view).
FIGURES 1–4. Matinta opiparis Simon. 1–2 in On the taxonomy of the jumping spider genus Matinta Ruiz & Maddison, 2019, with a taxonomic revision of the vicana species-group (Araneae: Salticidae: Amycini)
FIGURES 1–4. Matinta opiparis Simon. 1–2 Male (1 dorsal view; 2 ventral view). 3–4 Female (3 dorsal view; 4 ventral view).
FIGURES 27–30. Matinta spp. 27–28 in On the taxonomy of the jumping spider genus Matinta Ruiz & Maddison, 2019, with a taxonomic revision of the vicana species-group (Araneae: Salticidae: Amycini)
FIGURES 27–30. Matinta spp. 27–28 Matinta pereirae sp. nov., male (27 dorsal view; 28 ventral view). 29–30 Matinta aragog sp. nov., male (29 dorsal view; 30 ventral view).
STEP UP project: the Weierbach dataset 2019-2020
<p>Understanding the interaction between topography and vegetation across different environments is important to assess how hydrological and climatic conditions affect tree physiological activity. This becomes especially important given the expected reduction in water availability and increase in water demand driven by climate change. These extremes could enhance the thermal and hydrologic gradients along slopes. Multiple studies that investigated the effect of water limitations on forest vulnerability have neglected landscape heterogeneity at fine spatial resolutions, even though soil properties and topography are important abiotic factors for forest health (<a href="https://nph.onlinelibrary.wiley.com/doi/10.1111/nph.15274">Schwantes et al., 2018</a>).</p> <p>STEP-UP (Space-time patterns of tree water uptake at hillslopes with contrasting climate and geology) project aimed to investigate the spatio-temporal dynamics of water sources for beech trees (<em>Fagus sylvatica </em>L.). This species is abundant in many temperate European forest ecosystems, which could be strongly impacted by the predicted intensification of dry and wet spells (IPCC, 2023). The aim of the project is to get a better understanding of how climate, geology, and topography shape beech tree water uptake patterns along the catena by using a combination of hydro-meteorological observations, sap velocity measurements, and δ2H and δ18O measurements sampled in groundwater, soil water, and xylem water.</p> <p>For this purpose, we conducted a comparative study on a gentle (10 degrees) hillslope in the Weierbach catchment in Luxembourg (oceanic climate) (see <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/eco.2382">Fabiani et al., 2022)</a> and a steep (30 degrees) hillslope in the Lecciona catchment in Italy (Mediterranean climate).</p> <p>In this dataset, we present the data collected over 2019 and 2020 in the Weierbach catchment only. This dataset encompasses the following files:</p> <p>- conceptual figure named “Conceptual_figure_Weierbach_Lecciona”</p> <p>- daily sap velocity from three beech trees named “Weierbach_sapvelocity_2019-2020”</p> <p>- isotopic database (d<sup>18</sup>O and d<sup>2</sup>H) of xylem water, soil, groundwater, stream and precipitation named "Weierbach_isotopes_dataset_2019-2020"</p> <p>Sapflow sensors installation, maintenance and data cleaning were performed by Ginevra Fabiani and Remy Schoppach in the framework of EFFECT and STEP UP projects (AFR/STEP-UP ID 12546983 and FNR/CORE/C17/SR/11702136/EFFECT). Xylem, soil and water samples were collected, treated and analyzed by Ginevra Fabiani. Soil samples of 2020 were analyzed by Adnan Moussa. Julian Klaus, Daniele Penna and Laurent Pfister were responsible for founding acquisition and project management.</p>
CSS-2: Temperature retrieval September 2019
<p>Simulated CAIRT retrieval results for temperatures from ECMWF-IFS analysis data; Sep 2019</p> <p> </p>
Data from interlaboratory comparison of air thermometer calibrations, ATM ILC, 2019-2021
<p>The dataset contains data from an interlaboratory comparison conducted in EURAMET in 2019-2021, with the final report published in Oct 2023. The report and the ILC protocol are available in a different upload. </p> <p>This dataset consists of 3 files:</p> <ol> <li>participant information.xlsx: information on the setups used by each participant, their role in the ILC, temperatures measured at, the probes they measured on, the volume of chambers and the nature of the thermostat (bath, climate chamber or a subchamber).</li> <li>preprocessed data and drift.xlsx: preprocessed probe resistance, reference temperature, link corrected resistance, unilateral degree of equivalence, normalised error, pressure (if available), humidity (if available), pilot measurements in liquid and air for each probe, the list of points excluded from the consensus computations.</li> <li>selfheating.xlsx: probe-by-probe selfheating data from a number of participants (but not all). </li> </ol> <p> </p>
Temperature time series at 25m depth in the North pass of the Great Reef of Toliara (Madagascar) from May 6th 2019 to September 4th 2020
<p>Temperature data collected using two temperature loggers (HOBO Pendant, Temperature/Light data logger) that were successively deployed in the North pass of the Great Reef of Toliara (Madagascar), at 25m depth (S23°21.010', E43°36.837'). The first one was set to log a measurement every four hours, from May 6th till November 20th, 2019, and the second one was set to log a measurement every two hours, from November 20th, 2019 till September 4th, 2020.</p>
TARP 2019: SU 1014
Photogrammetric capture of stratigraphic unit 1014 Source: Objaverse 1.0 / Sketchfab
TARP 2019: SU 1109
Photogrammetric capture of stratigraphic unit 1109 Source: Objaverse 1.0 / Sketchfab
TARP 2019: SU 1062 - SU 1065, SU 1067
Photogrammetric capture of stratigraphic units 1062, 1065, 1067 Source: Objaverse 1.0 / Sketchfab
TARP 2019: SU 2053, SU 2093
Photogrammetric capture of stratigraphic units 2053, SU 2093 Source: Objaverse 1.0 / Sketchfab
Timber Low Poly - Recovered in Keyhaven 2019
Source: Objaverse 1.0 / Sketchfab
Potin Maxene 4d Tecnologie Martonne 2019
c'est des figure dans tous les sensse Source: Objaverse 1.0 / Sketchfab
TARP 2019: SU 1061
Photogrammetric capture of stratigraphic unit SU 1061 Source: Objaverse 1.0 / Sketchfab
INRAP Gaillon 2019
Chantier de Caroline RICHE Prise de vue le 02/04/2019 Source: Objaverse 1.0 / Sketchfab
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.