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3,225 results for “Case of study”
Figure 5 from: Harry I, Drees C, Hofer H, Assmann T (2011) When to sample in an inaccessible landscape: a case study with carabids from the Allgäu (northern Alps) (Coleoptera, Carabidae). ZooKeys 100: 255-271. https://doi.org/10.3897/zookeys.100.1531
Figure 5 - Dendrogram of sites with data from complete sampling (comp) and sampling periods 1 and 3 (part). The dendrogram is based on Bray-Curtis distances and uses Ward´s minimum variance method.
Figure 3 from: Harry I, Drees C, Hofer H, Assmann T (2011) When to sample in an inaccessible landscape: a case study with carabids from the Allgäu (northern Alps) (Coleoptera, Carabidae). ZooKeys 100: 255-271. https://doi.org/10.3897/zookeys.100.1531
Figure 3 - Phenology of single species. a Species with an early activity peak, b Species with a delayed activity peak and c Species without a clear activity peak. On the horizontal axis the sampling interval is given. For exact sampling periods, see Table 2.
Figure 2 from: Harry I, Drees C, Hofer H, Assmann T (2011) When to sample in an inaccessible landscape: a case study with carabids from the Allgäu (northern Alps) (Coleoptera, Carabidae). ZooKeys 100: 255-271. https://doi.org/10.3897/zookeys.100.1531
Figure 2 - Phenology of ground beetles. a Overview over all sites. Number of individuals is converted to percentage of total catch. b Seperated for the three site classes of altitude. On the horizontal axis the sampling interval is given. For exact sampling periods, see Table 2.
Figure 3 from: Vanhove M, Tessens B, Schoelinck C, Jondelius U, Littlewood T, Artois T, Huyse T (2013) Problematic barcoding in flatworms: A case-study on monogeneans and rhabdocoels (Platyhelminthes). ZooKeys 365: 355-379. https://doi.org/10.3897/zookeys.365.5776
Figure 3 - Pairwise distance (K2P) distributions of intra- and interspecific sequence divergences for the COI gene in Diplectanidae (A), 28S rDNA region in Diplectanidae (B), the COII gene in Gyrodactylus (C), the COI gene in Gyrodactylus (D), the ITS rDNA region in Gyrodactylus (E), the 28S rDNA region in Gieysztoria (F) and the ITS – 5.8S – ITS2 rDNA region in Gieysztoria (G).
Figure 2 from: Vanhove M, Tessens B, Schoelinck C, Jondelius U, Littlewood T, Artois T, Huyse T (2013) Problematic barcoding in flatworms: A case-study on monogeneans and rhabdocoels (Platyhelminthes). ZooKeys 365: 355-379. https://doi.org/10.3897/zookeys.365.5776
Figure 2 - Neighbour-Joining tree based on Kimura 2-parameter (Kimura 1980) distances for COI DNA sequences for 100 clones from 27 rhabdocoel species, five flatworm COI sequences available from GenBank and 31 reference COI sequences from taxa that are potential food sources for rhabdocoels. The clade with platyhelminth sequences is indicated in gray.
Figure 4 from: Vanhove M, Tessens B, Schoelinck C, Jondelius U, Littlewood T, Artois T, Huyse T (2013) Problematic barcoding in flatworms: A case-study on monogeneans and rhabdocoels (Platyhelminthes). ZooKeys 365: 355-379. https://doi.org/10.3897/zookeys.365.5776
Figure 4 - Optimum threshold defined by the intersection between the cumulative frequency distribution curves of the intraspecific (purple) and the interspecific (yellow) pairwise distances for the COI gene in Diplectanidae (A), 28S rDNA region in Diplectanidae (B), the COII gene in Gyrodactylus (C), the COI gene in Gyrodactylus (D), the ITS rDNA region in Gyrodactylus (E).
Figure 1 from: Vanhove M, Tessens B, Schoelinck C, Jondelius U, Littlewood T, Artois T, Huyse T (2013) Problematic barcoding in flatworms: A case-study on monogeneans and rhabdocoels (Platyhelminthes). ZooKeys 365: 355-379. https://doi.org/10.3897/zookeys.365.5776
Figure 1 - Fragments of an alignment of complete mitochondrial cytochrome c oxidase subunit I genes, as amino acids, for a diversity of metazoan taxa indicating the positions of the Folmer et al. (1994) primers. Shading indicates sequence identity to a consensus sequence calculated where > 50% taxa share amino acid identity at any position. All data available from MitoZoa database (Lupi et al. 2010). Vertical bar indicates platyhelminth taxa; black indicates 'turbellarians', grey indicates neodermatans.
Figure 4 from: Torralba-Burrial A, Merino-Sáinz I, Anadón A (2014) The relevance, biases, and importance of digitising opportunistic non-standardised collections: A case study in Iberian harvestmen fauna with BOS Arthropod Collection datasets (Arachnida, Opiliones). ZooKeys 404: 71-89. https://doi.org/10.3897/zookeys.404.6520
Figure 4 - Cluster hierarchical analysis with harvestmen presence data from seven locations with planned collection events and this general subset.
Figure 3 from: Torralba-Burrial A, Merino-Sáinz I, Anadón A (2014) The relevance, biases, and importance of digitising opportunistic non-standardised collections: A case study in Iberian harvestmen fauna with BOS Arthropod Collection datasets (Arachnida, Opiliones). ZooKeys 404: 71-89. https://doi.org/10.3897/zookeys.404.6520
Figure 3 - Distribution of harvestmen records in the unplanned collection events. A Ischyropsalididae, Nemastomatidae and Phalangiidae B Scleromatidae and Trogulidae.
Figure 2 from: Torralba-Burrial A, Merino-Sáinz I, Anadón A (2014) The relevance, biases, and importance of digitising opportunistic non-standardised collections: A case study in Iberian harvestmen fauna with BOS Arthropod Collection datasets (Arachnida, Opiliones). ZooKeys 404: 71-89. https://doi.org/10.3897/zookeys.404.6520
Figure 2 - A diagram depicting the methodological design of this hybrid data paper. Harvestmen in the BOS Arthropod Collection (Merino-Sáinz et al. 2013c) have come from several sources: some from unplanned collection events and some from planned collections. For this hybrid data-paper, we compared the data subset of unplanned collection events with the subsets of harvestmen from planned collection events using monthly sampling (Merino-Sáinz and Anadón 2008, 2013), and the harvestmen of similar planned events in the same area (Rosa García et al. 2009a, b, 2010a, b). All of the subsets compared appear in light blue in the diagram.
Figure 1 from: Ueti A, Pompeu PS, Ferreira RL (2015) Asymmetry compensation in a small vampire bat population in a cave: a case study in Brazil. Subterranean Biology 15: 57-67. https://doi.org/10.3897/subtbiol.15.4807
Figure 1 - Graphical representation of the compensation of asymmetry in the constituent structures of the wing and the maintenance of wing area. The height of the wing remains in the various plans through inverse variation in the size of structures 3 with 4 and 4 with 2. At length the maintenance is done by varying inversely in size from 1 to 5. 1 forearm 2 second phalanx of the fifth digit 3 fourth metacarpal of the digit 4 first phalanx of fourth digit, and 5 first phalanx of third digit.
Figure 7 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 7 - a iSpot participants who made at least one observation, ranked on the horizontal axis by the number of observations each made (shown on the vertical axis). b iSpot participants who made at least one determination ranked on the horizontal axis by the number of identifications each made. n = 201,711 observations on ispotnature.org for both.
Figure 6 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 6 - Schematic showing how the iSpot reputation system works. See the text for further explanation.
Figure 4 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 4 - A map of the social network of a single participant who contributed about 500 observations to iSpotnature.org at the locations shown by blue dots, with other iSpot participants, living at locations shown by red stars, who provided determinations for those observations. The location of the observation and the location of the person identifying it are joined by a line.
Figure 2 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 2 - The conceptual social network structure of iSpot, showing the group (3 of the 8) compartmentalization and its interaction. Not shown is the learner-mentor interaction within each group. (Icons http://www.icons-land.com)
Figure 9 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 9 - For ispotnature.org in the 4-year period shown: a The percentage of observations submitted each month that received a likely ID and the percentage where an expert provided or agreed the determination b The number of observations submitted per month.
Figure 10 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 10 - The cumulative frequency distribution for the time taken for a likely ID to be acquired by observations submitted to ispotnature.org without an organism name. n = 100,703 observations.
Figure 1 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 1 - Schematic of three network structures, from equal status, to expert status and a hybrid system. (Icons http://www.icons-land.com)
Figure 3 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 3 - The network linking participants who posted observations to iSpotnature.org without an identification and those providing a likely identification for those observations. The diagram is based on the sample of 5,000 identifications made up until 1 July 2014. This activity occurred over 32 days. The network contains 1,110 nodes linked by 2,876 edges. Red nodes (83.23%) are participants who only received identifications, green nodes (6.38%) are participants who only made identifications and blue nodes (10.39%) are participants who both made and received identifications.
Figure 5 from: Silvertown J, Harvey M, Greenwood R, Dodd M, Rosewell J, Rebelo T, Ansine J, McConway K (2015) Crowdsourcing the identification of organisms: A case-study of iSpot. ZooKeys 480: 125-146. https://doi.org/10.3897/zookeys.480.8803
Figure 5 - An example of an iSpot participant's profile showing their reputation and activity in the 8 groups.
ScienceDex guides
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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.