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.
3,225
datasets available to search
ShareScore release 0.9.0
Dataset results
3,225 results for “Case of study”
FIG. 5 in First Systematic Study using the Variability of the Residual Colour Patterns: The Case of the Paleogene Seraphsidae (Mollusca, Gastropoda, Stromboidea)
FIG. 5. — Comparison between the colour patterns of fossil and Recent Seraphsidae Jung, 1974: A-C, Seraphs volutatus (Solander in Brander, 1766), MNHN A28873, Grignon, Yvelines, France, Lutetian; A, ventral view; B, dorsal view; C, detailed view of the dots; D, E, Terebellum terebellum (Linnaeus, 1758), MNHN IM, Baie des Citrons, Nouméa, New Caledonia, Recent; D, dorsal view; E, detailed view of the dots; F, G, Paraseraphs placitus Jung, 1974, MNHN A29168 (leg. Pacaud), Chaussy, Val d'Oise, France, Lutetian; F, dorsal view; G, detailed view of the dots; A-C, F, G, under UV light in negative view; D, E, in normal light. Scale bars: A, B, D, F, 10 mm; C, G, 2 mm; E, 5 mm. Photographs by C. Lemzaouda (MNHN).
FIG. 6 in First Systematic Study using the Variability of the Residual Colour Patterns: The Case of the Paleogene Seraphsidae (Mollusca, Gastropoda, Stromboidea)
FIG. 6. — Seraphs Montfort, 1810 shells in normal light: A-E, S. volutatus (Solander in Brander, 1766); A-C, MNHN A28776 (ex Galerie de Zoologie coll.), Grignon, Yvelines, France, Lutetian; A, ventral view; B, labral view; C, dorsal view; D, E, lectotype of Terebellum convolutum Lamarck, 1802, MNHN B70325 (Lamarck coll.), Grignon, Lutetian; D, ventral view; E, dorsal view; F, G, S. sopitus (Solan- der in Brander, 1766), MNHN A28778 (Faullummel coll.), Le Limon, Seine-et-Marne, France, Bartonian; F, ventral view; G, labral view; H-J, S. olivaceus (Cossmann, 1889); H, I, MNHN A28577 (Ledon coll.), Chaussy (Les Garennes), Val d'Oise, France, Lutetian; H, ventral view; I, dorsal view; J, MNHN A29225, labral view (Ledon coll.), Chaussy (Les Garennes), Lutetian. Scale bars: 10 mm. Photographs by C. Lemzaouda (MNHN).
FIG. 3 in First Systematic Study using the Variability of the Residual Colour Patterns: The Case of the Paleogene Seraphsidae (Mollusca, Gastropoda, Stromboidea)
FIG. 3. — Descriptive terminology of the residual colour patterns of the Seraphsidae Jung, 1974: A, B, Paraseraphs placitus Jung, 1974, MNHN A28963 (Ballot coll.), Grignon, Yvelines, France, Lutetian; A, apical part; B, detailed view of the dots; C, D, Seraphs volutatus (Solander in Brander, 1766), MNHN A28873, Grignon, Lutetian; C, apical part; D, detailed view of the dots; E, S. chilophorus (Cossmann, 1889), MNHN A28922 (leg. Pacaud), Fercourt, Oise, France, Lutetian (apical part). Abbreviations: coad, coalescence of dots; d, dot; ds, darker spot; ls, lighter spot; p, patch. All pictures taken under UV light. Scale bars: A, C, E, 5 mm; B, D, 2 mm. Photographs by C. Lemzaouda (MNHN).
Fig. 3 in Can weight/length relationship predict size at first maturity? A case study with two species of Characidae
Fig. 3. Relationship between size at first maturity (L) and 50 maximum reported size for small characids (grey spots, bold line; data from Table 1). The thin line represents L values 50 estimated from maximum size following the general function of Froese & Binohlan (2000). Black spots are original data from the present study (SCL values).
Fig. 2 in Can weight/length relationship predict size at first maturity? A case study with two species of Characidae
Fig. 2. Weight/length relationship for Cheirodon ibicuhiensis (a); proportional residuals resulting from Huxley´s adjustment (b); weight/length relationship and proportional residuals distribution according to the polyphasic growth model (c and d, respectively).
Fig. 1 in Can weight/length relationship predict size at first maturity? A case study with two species of Characidae
Fig. 1. Weight/length relationship for Astyanax jacuhiensis (a); proportional residuals resulting from Huxley´s adjustment (b); weight/length relationship and proportional residuals distribution according to the polyphasic growth model (c and d, respectively).
Fig. 2 in Assessing diet composition of seahorses in the wild using a non destructive method: Hippocampus reidi (Teleostei: Syngnathidae) as a study-case
Fig. 2. Feeding strategy diagram. Prey-specific abundance plotted against frequency of occurrence of prey items in the diet of the seahorse Hippocampus reidi (n = 280). Prey items: 1. Nematoda, 2. Copepoda, (Harpacticoida), 3. Caridae, 4. Copepoda (nauplii), 5. Copepoda (Calanoida), 6. Copepoda (Cyclopoida), 7. Caridae (chelipods), 8. Teleostei (Gobiidae), 9. Insecta (Hymenoptera), 10. Amphipoda (Gammaridae), 11. Teleostei (scales), 12. Polichaeta (larvae), 13. Amphipoda (Caprellidae), 14. Ostracoda, 15. Eggs (possibly of mollusks or crustaceans), 16. Polichaeta (Nereididae), 17. Brachyura (nauplii), 18. Insecta (Chironomidae), 19. Crustacea (larvae), 20. Gastropoda (larvae), 21. Bivalvia (larvae), 22. Caridae (zoea), 23. Isopoda, 24. Oligochaeta, 25. Foraminifera.
Fig. 1 in Assessing diet composition of seahorses in the wild using a non destructive method: Hippocampus reidi (Teleostei: Syngnathidae) as a study-case
Fig. 1. Mean values of induction (square) and recovery (lozenge) times of Hippocampus reidi (n = 242) in seconds (box = standard error; whisker = standard deviation). Reproduc- tive state: IM = immature, OF = ovipositor region flat, OB = ovipositor region bulging, B = brooding, NB = non-brooding. Sex: U = undetermined, F = female, M = male.
Fig. 6 in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 6. Number of fishers and of syngnathids captured for ornamental purposes in Salvador, Bahia, Brazil, from January/ 1997 to June/2005.
Fig. 5 in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 5. Number of days of collection and number of fishers involved in the syngnathid fishery in Salvador, Bahia, Brazil, from January/1997 to June/2005.
Fig. 1 in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 1. Map showing the Baía de Todos os Santos, the main area of collection of live syngnathids in Salvador, Bahia State, NE Brazil.
Fig. 3. a in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 3. a) Specimen of Micrognathus sp., Baía de Todos os Santos, Bahia State, Brazil. b) Specimen of Cosmocampus albirostris, Baía de Todos os Santos, Bahia State, Brazil. Photos: Cláudio L. S. Sampaio.
Fig. 4 in Collaborative monitoring of the ornamental trade of seahorses and pipefishes (Teleostei: Syngnathidae) in Brazil: Bahia State as a case study
Fig. 4. Hooka-diver using hand-nets to collect marine ornamental fishes. Baía de Todos os Santos, Bahia State, Brazil. Photo: Leo Dutra.
Figure 6 in Dental microwear in the orthodentine of the Xenarthra (Mammalia) and its use in reconstructing the palaeodiet of extinct taxa: the case study of Nothrotheriops shastensis (Xenarthra, Tardigrada, Nothrotheriidae)
Figure 6. Hierarchical cluster dendrogram (same method as Fig. 3) including Nothrotheriops shastensis among all extant xenarthran taxa. Note that N. shastensis clusters with extant folivores (Bradypus).
Figure 3 in Dental microwear in the orthodentine of the Xenarthra (Mammalia) and its use in reconstructing the palaeodiet of extinct taxa: the case study of Nothrotheriops shastensis (Xenarthra, Tardigrada, Nothrotheriidae)
Figure 3. Hierarchical cluster dendrogram of microwear variables for all extant xenarthran species in this study. Euclidean distance measure is used. Note that folivores (Bradypus) cluster together (shaded area).
Figure 4 in Dental microwear in the orthodentine of the Xenarthra (Mammalia) and its use in reconstructing the palaeodiet of extinct taxa: the case study of Nothrotheriops shastensis (Xenarthra, Tardigrada, Nothrotheriidae)
Figure 4. Plot of mean scratch and pit values for individual Cabassous centralis specimens (N = 11) in relation to extant xenarthran dietary ecomorphospaces from Fig. 2. The four individuals with highest scratch values are labelled by specimen number for reference within text.
How to achieve a higher selection plateau in forest tree breeding? Fostering heterozygote x homozygote relationships in optimal contribution selection in the case study of Populus nigra
<p><span><span><span><span><span><span>In breeding, Optimal Contribution Selection (OCS) is one of the most effective strategies to balance short- and long-term genetic responses, by maximizing genetic gain and minimizing global coancestry. Considering genetic diversity in the selection dynamic – through coancestry – is undoubtedly the reason for the success of OCS, as it avoids intial loss of favorable alleles. Originally formulated with the pedigree relationship matrix, global coancestry can nowadays be assessed with one of the possible formulations of the realized genomic relationship matrix. Most formulations were optimized for genomic evaluation, but few for the management of coancestry. We introduce here an alternative formulation specifically developed for Genomic OCS (GOCS), intended to better control heterozygous loci, and thus better account for Mendelian sampling. We simulated a multi-generation breeding program with mate allocation and under GOCS for twenty generations, solved with quadratic programming. With the case study of </span></span></span><span><i><span>Populus nigra</span></i></span><span><span><span>, we have shown that, although the dynamic was mainly determined by the trade-off between genetic gain and genetic diversity, better formulations of the genomic relationship matrix, especially those fostering individuals carrying multiple heterozygous loci, can lead to better short-term genetic gain and a higher selection plateau. </span></span></span></span></span></span></p>
A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)
<p>A boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from black spruce (Picea mariana)-dominated mature forest stands.</p> <p>Dataset glossary and users’ instructions are documented in ‘README.md’. </p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
Analyzing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan
<p>#############################################################################################################<br> Datasets supporting the publication:<br> Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults:<br> a case study of Bishkek, Kyrgyzstan.<br> <a href="https://doi.org/10.3390/rs14225790">https://doi.org/10.3390/rs14225790</a></p> <p>-Please refer to the publication for details on the production of each dataset.<br> -Datasets are ordered following the publication figures.<br> -Please cite the publication and this dataset repository when using the data.<br> #############################################################################################################</p> <p>------------------------<br> Structure:<br> File ID<br> -[fields:] description<br> ------------------------</p> <p>KH9_1979_builtup.shp<br> -KH9 1979 built-up area classification</p> <p>S2_2021_builtup.tif<br> -Sentinel-2 2021 built-up area classification.</p> <p>S2_2021_corine_land_cover_class.tif<br> -Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes.</p> <p>S2_KH9_DN_change_aggregated.shp<br> -Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up.</p> <p>building_characteristics.shp<br> -build_count: building count in 500 m square grid cell.<br> -mean_area: mean building size (m^2) in 500 m square grid cell.<br> -median_area: median building size(m^2) in 500 m square grid cell.<br> -cell_coverage: %building coverage of 500 m square grid cell.</p> <p>pleiades_buildings_all.shp<br> -All building detections from Pleiades data. Confidence values are output from the deep learning model.</p> <p>pleiades_buildings_heights.shp<br> -Building detections from the Pleiades data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>wv2_buildings_all.shp<br> -All building detections from WorldView-2 data. Confidence values are output from the deep learning model.</p> <p>wv2_buildings_heights.shp<br> -Building detections from the WorldView-2 data that were allocated heights (m).<br> -Zmean, Zmedian,... refer to heights (m)</p> <p>trained_rcnn.zip<br> -ArcGIS Pro deep learning model (DLPK) used to extract building footprints.</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.