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.
124
datasets available to search
ShareScore release 0.7.1
Dataset results
124 results for “distributed sampling”
Genetic variation in Neotropical butterflies is associated with sampling scale, species distributions, and historical forest dynamics
Open the record for dataset details and reuse information.
Size fractionation for total Chl a within the surface layer and calculated size distribution of total Chl a from discrete bottle samples from CCE-CalCOFI Augmented Cruises in the California Current System, 2004 - 2017 (ongoing).
Water for size fractionation of chlorophyll a is sampled from ~10m depth (surface layer) on Line 83 and 87, with scattered inshore stations within the CalCOFI and SCCOOS stations located in the CCE study area. The size distribution of total chlorophyll a is determined by filtering water though filters of differing pore sizes. These are then extracted in acetone and analyzed fluorometrically with Turner Designs 10-AU Fluorometer on CalCOFI cruises (since 2006, ongoing). Chlorophyll a and taxon-specific pigments (chlorophylls and carotenoids) are qualitatively and quantitatively characterized in the lab onshore by several size fractions (< 1µm to > 20µm) utilizing High Performance Liquid Chromatography (HPLC) analysis. The samples analyzed within the CCE region are used to develop a metric for phytoplankton community structure that can be used to monitor its state and changes thereof over time.
Data from: Optimising sample sizes for animal distribution analysis using tracking data
<p><span>1. Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population.</span></p> <p><span>2. Here, we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the R package <i>SDLfilter</i>.</span></p> <p><span>3. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking data-set of flatback turtles, <i>Natator depressus, </i>tagged with accurate Fastloc-GPS tags (n = 69).</span></p> <p><span>4. Our approach has applicability for the post-hoc validation of sample sizes required for the robust estimation of distribution patterns across a wide range of taxa, populations and life history stages of animals.</span></p>
Figure 6 from: Bonello G, Grillo M, Cecchetto M, Giallain M, Granata A, Guglielmo L, Pane L, Schiaparelli S (2020) Distributional records of Ross Sea (Antarctica) planktic Copepoda from bibliographic data and samples curated at the Italian National Antarctic Museum (MNA): checklist of species collected in the Ross Sea sector from 1987 to 1995. ZooKeys 969: 1-22. https://doi.org/10.3897/zookeys.969.52334
Figure 6 Metridia gerlachei (Copepoda, Calanoida; female, MNA-12439) acquired with fluorescence microscopy (Congo Red, 1.5 mg/ml). This species is one of the most adapted species in the Antarctic region and can perform diel vertical migrations that highly influence the surrounding waters in terms of trophic relationships in Terra Nova Bay.
Figure 1 from: Bonello G, Grillo M, Cecchetto M, Giallain M, Granata A, Guglielmo L, Pane L, Schiaparelli S (2020) Distributional records of Ross Sea (Antarctica) planktic Copepoda from bibliographic data and samples curated at the Italian National Antarctic Museum (MNA): checklist of species collected in the Ross Sea sector from 1987 to 1995. ZooKeys 969: 1-22. https://doi.org/10.3897/zookeys.969.52334
Figure 1 Sampling stations for IIIrd (yellow), Vth (blue), and Xth (red) expedition a overview of spatial extent in Antarctica b sampling stations in the Western Ross Sea c focus on Terra Nova Bay sampling stations. This map was produced using the collection of datasets "Quantarctica" (Matsuoka et al. 2017) and QGIS (QGIS Development Team 2020).
Figure 5 from: Bonello G, Grillo M, Cecchetto M, Giallain M, Granata A, Guglielmo L, Pane L, Schiaparelli S (2020) Distributional records of Ross Sea (Antarctica) planktic Copepoda from bibliographic data and samples curated at the Italian National Antarctic Museum (MNA): checklist of species collected in the Ross Sea sector from 1987 to 1995. ZooKeys 969: 1-22. https://doi.org/10.3897/zookeys.969.52334
Figure 5 Paraeuchaeta exigua (Copepoda, Calanoida; female, MNA-12333) acquired with scanning electron microscopy (SEM). This is one of the most common species in the coastal area of Terra Nova Bay. It plays a key role in the neritic trophic chain and highly contributes to the total mesozooplanktic biomass.
Data from: Inference of genetic architecture from chromosome partitioning analyses is sensitive to genome variation, sample size, heritability and effect size distribution
Genomewide association studies have contributed immensely to our understanding of the genetic basis of complex traits. One major conclusion arising from these studies is that most traits are controlled by many loci of small effect, confirming the infinitesimal model of quantitative genetics. A popular approach to test for polygenic architecture involves so‐called "chromosome partitioning" where phenotypic variance explained by each chromosome is regressed on the size of the chromosome. First developed for humans, this has now been repeatedly used in other species, but there has been no evaluation of the suitability of this method in species that can differ in their genome characteristics such as number and size of chromosomes. Nor has the influence of sample size, heritability of the trait, effect size distribution of loci controlling the trait or the physical distribution of the causal loci in the genome been examined. Using simulated data, we show that these characteristics have major influence on the inferences of the genetic architecture of traits we can infer using chromosome partitioning analyses. In particular, small variation in chromosome size, small sample size, low heritability, a skewed effect size distribution and clustering of loci can lead to a loss of power and consequently altered inference from chromosome partitioning analyses. Future studies employing this approach need to consider and derive an appropriate null model for their study system, taking these parameters into consideration. Our simulation results can provide some guidelines on these matters, but further studies examining a broader parameter space are needed.
Supplementary material 3 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235
Weighted mean SDMs for individual algorithms and evaluation statistics (biomod2) :
Supplementary material 2 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235
Full readout for the MaxEnt northern quoll SDM :
Supplementary material 1 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235
GIS data sets used in variable assessments and map of Pilbara vegetation systems :
Figure 6 from: Cecchetto M, Alvaro MC, Ghiglione C, Guzzi A, Mazzoli C, Piazza P, Schiaparelli S (2017) Distributional records of Antarctic and sub-Antarctic Ophiuroidea from samples curated at the Italian National Antarctic Museum (MNA): check-list update of the group in the Terra Nova Bay area (Ross Sea) and launch of the MNA 3D model 'virtual gallery'. ZooKeys 705: 61-79. https://doi.org/10.3897/zookeys.705.13712
Figure 6 - Number of individuals by preservation method stored at MNA. Dried specimens percentage in orange (~80%), ethanol in blue (~15%), and frozen in green (~5%)
Figure 7 from: Cecchetto M, Alvaro MC, Ghiglione C, Guzzi A, Mazzoli C, Piazza P, Schiaparelli S (2017) Distributional records of Antarctic and sub-Antarctic Ophiuroidea from samples curated at the Italian National Antarctic Museum (MNA): check-list update of the group in the Terra Nova Bay area (Ross Sea) and launch of the MNA 3D model 'virtual gallery'. ZooKeys 705: 61-79. https://doi.org/10.3897/zookeys.705.13712
Figure 7 - Video of the 3D model of O. echinulata (MNA 2644); Catalogue number, latitude (DD), longitude (DD), depth and event date as described in the section "Dataset description", GenBank Accession number, Barcode Index Number from the Bold system and the complete COI sequence in FASTA format. Video available at: https://youtu.be/Sq6au-_CHy0
Figure 2 from: Cecchetto M, Alvaro MC, Ghiglione C, Guzzi A, Mazzoli C, Piazza P, Schiaparelli S (2017) Distributional records of Antarctic and sub-Antarctic Ophiuroidea from samples curated at the Italian National Antarctic Museum (MNA): check-list update of the group in the Terra Nova Bay area (Ross Sea) and launch of the MNA 3D model 'virtual gallery'. ZooKeys 705: 61-79. https://doi.org/10.3897/zookeys.705.13712
Figure 2 - Overview map depicting the wideness of the area containing the sampling stations of the dataset. Areas in red are shown in detail in figures 3, 4 and 5.
Figure 5 from: Cecchetto M, Alvaro MC, Ghiglione C, Guzzi A, Mazzoli C, Piazza P, Schiaparelli S (2017) Distributional records of Antarctic and sub-Antarctic Ophiuroidea from samples curated at the Italian National Antarctic Museum (MNA): check-list update of the group in the Terra Nova Bay area (Ross Sea) and launch of the MNA 3D model 'virtual gallery'. ZooKeys 705: 61-79. https://doi.org/10.3897/zookeys.705.13712
Figure 5 - Map with the sampling sites in the Falkland Islands (Islas Malvinas) and Bransfield Strait
Figure 1 from: Cecchetto M, Alvaro MC, Ghiglione C, Guzzi A, Mazzoli C, Piazza P, Schiaparelli S (2017) Distributional records of Antarctic and sub-Antarctic Ophiuroidea from samples curated at the Italian National Antarctic Museum (MNA): check-list update of the group in the Terra Nova Bay area (Ross Sea) and launch of the MNA 3D model 'virtual gallery'. ZooKeys 705: 61-79. https://doi.org/10.3897/zookeys.705.13712
Figure 1 - Flowchart depicting major steps in dataset development and publishing, from sample collection to the production of a virtual collection of the museum's 3D vouchers.
Figure 4 from: Cecchetto M, Alvaro MC, Ghiglione C, Guzzi A, Mazzoli C, Piazza P, Schiaparelli S (2017) Distributional records of Antarctic and sub-Antarctic Ophiuroidea from samples curated at the Italian National Antarctic Museum (MNA): check-list update of the group in the Terra Nova Bay area (Ross Sea) and launch of the MNA 3D model 'virtual gallery'. ZooKeys 705: 61-79. https://doi.org/10.3897/zookeys.705.13712
Figure 4 - Highlight of the sampling sites in Terra Nova Bay (a sampling locations from 1988 to 2004 b sampling locations from 2009 to 2014), Cape Hallett (c) and Cape Adare (d) areas.
Figure 8 from: Cecchetto M, Alvaro MC, Ghiglione C, Guzzi A, Mazzoli C, Piazza P, Schiaparelli S (2017) Distributional records of Antarctic and sub-Antarctic Ophiuroidea from samples curated at the Italian National Antarctic Museum (MNA): check-list update of the group in the Terra Nova Bay area (Ross Sea) and launch of the MNA 3D model 'virtual gallery'. ZooKeys 705: 61-79. https://doi.org/10.3897/zookeys.705.13712
Figure 8 - Video of the 3D model of O. antarctica (MNA 7784); Catalogue number, latitude (DD), longitude (DD), depth and event date as described in the section "Dataset description"; GenBank Accession number, the Barcode Index Number from the Bold system and the complete COI sequence in FASTA format. Video available at: https://youtu.be/Z72GryamWZY
Figure 9 from: Cecchetto M, Alvaro MC, Ghiglione C, Guzzi A, Mazzoli C, Piazza P, Schiaparelli S (2017) Distributional records of Antarctic and sub-Antarctic Ophiuroidea from samples curated at the Italian National Antarctic Museum (MNA): check-list update of the group in the Terra Nova Bay area (Ross Sea) and launch of the MNA 3D model 'virtual gallery'. ZooKeys 705: 61-79. https://doi.org/10.3897/zookeys.705.13712
Figure 9 - a The A. agassizii (MNA 7368) specimen used for photogrammetry documented immediately after collecting b screenshot of the 3D model based on photogrammetry at the mesh reconstruction stage (the marker showed as model background is available as supplementary material in Porter et al. 2016); Catalogue number, latitude (DD), longitude (DD), depth and event date as described in the section "Dataset description"; GenBank Accession number, the Barcode Index Number from the Bold system and the complete COI sequence in FASTA format.
EC-t dataset for: Using Inverse Gaussian Distribution for Analysis of Breakthrough Curves in Tracer Tests for Sandy Samples in Rigid Wall Cell
Open the record for dataset details and reuse information.
FIGURE 4 in Hypogean presumably sister species Quedius repentinus sp. n. from Altai and Q. roma from Sikhote-Alin (Coleoptera: Staphylinidae): a disjunct distribution or poorly sampled Siberia?
FIGURE 4. Typical talus-associated habitat at Mt. Evrechala (SE Altai). Photo by I.I. Lubechanskiy.
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.