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226 results for “Grooming”
Supplementary material 1 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
List of selected tools.
Figure 4 from: Bingham H, Doudin M, Weatherdon L, Despot-Belmonte K, Wetzel F, Groom Q, Lewis E, Regan E, Appeltans W, Güntsch A, Mergen P, Agosti D, Penev L, Hoffmann A, Saarenmaa H, Geller G, Kim K, Kim H, Archambeau A, Häuser C, Schmeller D, Geijzendorffer I, García Camacho A, Guerra C, Robertson T, Runnel V, Valland N, Martin C (2017) The Biodiversity Informatics Landscape: Elements, Connections and Opportunities. Research Ideas and Outcomes 3: e14059. https://doi.org/10.3897/rio.3.e14059
Figure 4 - The network of biodiversity informatics organisations. The network visualisation was created using NodeXL (Version 1.0.1.229) (Smith et al. 2009) and was laid out with the Harel–Koren Fast Multiscale algorithm and then adjusted manually to remove overlaps. The colours represent clusters identified using the Girvan–Newman algorithm.
Figure 3 from: Bingham H, Doudin M, Weatherdon L, Despot-Belmonte K, Wetzel F, Groom Q, Lewis E, Regan E, Appeltans W, Güntsch A, Mergen P, Agosti D, Penev L, Hoffmann A, Saarenmaa H, Geller G, Kim K, Kim H, Archambeau A, Häuser C, Schmeller D, Geijzendorffer I, García Camacho A, Guerra C, Robertson T, Runnel V, Valland N, Martin C (2017) The Biodiversity Informatics Landscape: Elements, Connections and Opportunities. Research Ideas and Outcomes 3: e14059. https://doi.org/10.3897/rio.3.e14059
Figure 3 - An example of links from the biodiversity informatics landscape to the policy-level: the Biodiversity Indicators Partnership (BIP).
Figure 2 from: Bingham H, Doudin M, Weatherdon L, Despot-Belmonte K, Wetzel F, Groom Q, Lewis E, Regan E, Appeltans W, Güntsch A, Mergen P, Agosti D, Penev L, Hoffmann A, Saarenmaa H, Geller G, Kim K, Kim H, Archambeau A, Häuser C, Schmeller D, Geijzendorffer I, García Camacho A, Guerra C, Robertson T, Runnel V, Valland N, Martin C (2017) The Biodiversity Informatics Landscape: Elements, Connections and Opportunities. Research Ideas and Outcomes 3: e14059. https://doi.org/10.3897/rio.3.e14059
Figure 2 - A highly-connected element in the landscape: the Global Biodiversity Information Facility (GBIF).
Figure 1 from: Despot-Belmonte K, Doudin M, Groom Q, Wetzel F, Agosti D, Jacobsen K, Smirnova L, Weatherdon L, Robertson T, Penev L, Regan E, Hoffmann A, MacSharry B, Shennan-Farpon Y, Martin C (2017) EU BON's contributions towards meeting Aichi Biodiversity Target 19. Research Ideas and Outcomes 3: e14013. https://doi.org/10.3897/rio.3.e14013
Figure 1 - This infographic illustrates EU BON's contribution towards meeting Aichi Biodiversity Target 19.
Figure 1 from: Vanderhoeven S, Adriaens T, Desmet P, Strubbe D, Backeljau T, Barbier Y, Brosens D, Cigar J, Coupremanne M, De Troch R, Eggermont H, Heughebaert A, Hostens K, Huybrechts P, Jacquemart A, Lens L, Monty A, Paquet J, Prévot C, Robertson T, Termonia P, Van De Kerchove R, Van Hoey G, Van Schaeybroeck B, Vercayie D, Verleye T, Welby S, Groom Q (2017) Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy. Research Ideas and Outcomes 3: e13414. https://doi.org/10.3897/rio.3.e13414
Figure 1 - A visual description of the TrIAS workflow through work packages. Work package 1 generates the input data; Work package 2 creates indicators and summaries of the data; Work package 3 uses the data and generates models and predications of future distributions; Work package 4 involves experts using the information from the other work packages, together with their own experience to create impact assessments.
Figure 6 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 6 - Automated creation of data paper manuscripts from Ecological Metadata Language (EML) metadata in the ARPHA Writing Tool.
Figure 5 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 5 - The metadata from the GBIF Integrated Publishing Toolkit (IPT) can be downloaded as RTF or EML files and submitted to Pensoft's journals as data paper manuscripts.
Figure 3 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 3 - The user interface of the ARPHA Writing Tool through which single or multiple specimen records from GBIF, BOLD, iDigBio and PlutoF are imported through records identifiers.
Figure 1 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 1 - Recommendation of Dryad to cite both the original article in association with which the data were published and the data themselves.
Figure 7 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 7 - Selection of the journal and "Data Paper (Biosciences)" template in the ARPHA Writing Tool.
Figure 4 from: Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431
Figure 4 - Occurrence records and taxonomic treatments (if present in the article), published in the Biodiversity Data Journal, are exported in two separate Darwin Core Archives (DwC-A) and are available for direct download or harvesting via web services.
Figure 5 from: Despot-Belmonte K, Neßhöver C, Saarenmaa H, Regan E, Meyer C, Martins E, Groom Q, Hoffmann A, Caine A, Bowles-Newark N, Bae H, Canhos D, Stenzel S, Bowler D, Schneider A, V. Weatherdon L, S. Martin C (2017) Biodiversity data provision and decision-making - addressing the challenges. Research Ideas and Outcomes 3: e12165. https://doi.org/10.3897/rio.3.e12165
Figure 5 - Guiding principles for promoting the application of EBVs for current and future needs of decision-makers - researcher's brief.
Figure 2 from: Despot-Belmonte K, Neßhöver C, Saarenmaa H, Regan E, Meyer C, Martins E, Groom Q, Hoffmann A, Caine A, Bowles-Newark N, Bae H, Canhos D, Stenzel S, Bowler D, Schneider A, V. Weatherdon L, S. Martin C (2017) Biodiversity data provision and decision-making - addressing the challenges. Research Ideas and Outcomes 3: e12165. https://doi.org/10.3897/rio.3.e12165
Figure 2 - From a European perspective, the European biodiversity policy landscape is complex and "data hungry" (Wetzel et al. 2015).
Figure 4 from: Runnel V, Wetzel F, Groom Q, Koch W, Pe'er I, Valland N, Panteri E, Kõljalg U (2016) Summary report and strategy recommendations for EU citizen science gateway for biodiversity data. Research Ideas and Outcomes 2: e11563. https://doi.org/10.3897/rio.2.e11563
Figure 4 - European Starling Sturnus vulgaris (Linnaeus, 1758) occurrences visualized in GBIF portal (A), distribution map in Fauna Europaea (B) and occurrences in Latvian national observation portal dabastati.lv (C).
Figure 3 from: Runnel V, Wetzel F, Groom Q, Koch W, Pe'er I, Valland N, Panteri E, Kõljalg U (2016) Summary report and strategy recommendations for EU citizen science gateway for biodiversity data. Research Ideas and Outcomes 2: e11563. https://doi.org/10.3897/rio.2.e11563
Figure 3 - Peacock Butterfly Aglais io (Linnaeus, 1758) occurrences visualized in GBIF portal (A), distribution map in Fauna Europaea (B) and occurrences in Latvian national observation portal dabastati.lv (C).
Figure 1 from: Vissers J, Bosch FV, Bogaerts A, Cocquyt C, Degreef J, Diagre D, de Haan M, De Smedt S, Engledow H, Ertz D, Fabri R, Godefroid S, Hanquart N, Mergen P, Ronse A, Sosef M, Stévart T, Stoffelen P, Vanderhoeven S, Groom Q (2017) Scientific user requirements for a herbarium data portal. PhytoKeys 78: 37-57. https://doi.org/10.3897/phytokeys.78.10936
Figure 1 - Stakeholders interacting with the Botanic Garden Meise and potentially using its data portal. The stakeholders prefixed by the words 'internal' refer to those that work at the Botanic Garden, whereas those referred to as 'external' refer to researchers in other institutions.
Figure 3 from: Vissers J, Bosch FV, Bogaerts A, Cocquyt C, Degreef J, Diagre D, de Haan M, De Smedt S, Engledow H, Ertz D, Fabri R, Godefroid S, Hanquart N, Mergen P, Ronse A, Sosef M, Stévart T, Stoffelen P, Vanderhoeven S, Groom Q (2017) Scientific user requirements for a herbarium data portal. PhytoKeys 78: 37-57. https://doi.org/10.3897/phytokeys.78.10936
Figure 3 - A summary of the data elements mentioned by the different researcher types, showing which data elements researchers had in common and which were unique. This does not mean that any particular data element is not of interest to another group, only that it did not arise in the series of interviews. Details of these data elements can be found in the supplementary information. The full list of common data elements is listed in Table 2.
Figure 2 from: Vissers J, Bosch FV, Bogaerts A, Cocquyt C, Degreef J, Diagre D, de Haan M, De Smedt S, Engledow H, Ertz D, Fabri R, Godefroid S, Hanquart N, Mergen P, Ronse A, Sosef M, Stévart T, Stoffelen P, Vanderhoeven S, Groom Q (2017) Scientific user requirements for a herbarium data portal. PhytoKeys 78: 37-57. https://doi.org/10.3897/phytokeys.78.10936
Figure 2 - A user experience researcher using affinity diagramming to cluster user requirements from the results of the interviews.
Supplementary material 1 from: Penev L, Groom Q, Casino A, Barov B (2024) Uniting FAIR data through interlinked, machine-actionable infrastructures. Research Ideas and Outcomes 10: e126588. https://doi.org/10.3897/rio.10.e126588
Uniting FAIR data through interlinked, machine-actionable infrastructures
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
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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.