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Supplementary material 1 from: Xu K-W, Han Y-T, Dong Y-R, Guo J-Q, Mao L-F, Liao W-B (2024) Asplenium guodanum (Aspleniaceae), a distinct new fern species from northern Guangdong, China, based on morphological data and molecular phylogeny. PhytoKeys 241: 191-200. https://doi.org/10.3897/phytokeys.241.122789
List of voucher specimens and Genbank accession numbers used in phylogenetic analyses
Figure 3 from: Xu K-W, Han Y-T, Dong Y-R, Guo J-Q, Mao L-F, Liao W-B (2024) Asplenium guodanum (Aspleniaceae), a distinct new fern species from northern Guangdong, China, based on morphological data and molecular phylogeny. PhytoKeys 241: 191-200. https://doi.org/10.3897/phytokeys.241.122789
Figure 3 Maximum Likelihood phylogeny of the Asplenium bullatum clade, based on six plastid markers (atpB, rbcL, rps4 & rps4-trnS and trnL & trnL-F). The numbers associated with branches are Maximum Likelihood bootstrap support (MLBS) and Bayesian Posterior Probability (BIPP). The asterisk indicates MLBS = 100 or BIPP = 1.00.
Figure 1 from: Xu K-W, Han Y-T, Dong Y-R, Guo J-Q, Mao L-F, Liao W-B (2024) Asplenium guodanum (Aspleniaceae), a distinct new fern species from northern Guangdong, China, based on morphological data and molecular phylogeny. PhytoKeys 241: 191-200. https://doi.org/10.3897/phytokeys.241.122789
Figure 1 Macromorphology of Asplenium guodanum sp. nov. A habitat B habit C abaxial lamina D frond E adaxial lamina F rachis G abaxial view of pinna H adaxial view of pinna I reduced pinna base J scales at base stipe K rhizome L fiddlehead.
Data and R scripts for statistical analyses
Open the record for dataset details and reuse information.
Source data NPP-23-1110R
<p>Source data for manuscript NPP-23-1110R</p>
Imada Fasting Cancer - single cell RNA-Seq Processed Data R object
Open the record for dataset details and reuse information.
Figure 5 from: Assou D, Segniagbeto GH, Radji R, Akiti J, Pando F (2018) Monitoring data of marine turtles on the Togolese coast during 2012–2013. ZooKeys 779: 109-118. https://doi.org/10.3897/zookeys.779.26967
Figure 5 Sea turtle species considered in the survey: aLepidochelysolivaceabCheloniamydascDermochelyscoriacea.
Figure 4 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 4 Initial expertise (color of the bar) vs final confidence (y-axis) after the GRU workshop for participants responding to final survey. Example for how to interpret this graphic: the blue color bar at the top indicates that before the workshop roughly 50% of respondents said their knowledge of GEOLocate was "neither high nor low" but after the workshop these same respondents selected "much higher" for their knowledge of GEOLocate.
Figure 3 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 3 An illustrative example of the two methods of uncertainty capture when georeferencing specimens. Method A, or polygon, creates a shape around the river (in blue). Method B, or point-radius, creates a circle of uncertainty around the origin. The illustration is based on output from GeoLocate software (Rios 2018) for both polygon and point-radius.
Figure 2 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 2 This specimen record is an example from the University of California Collection Network Symbiota Portal. The large image is an edit of the record to include a medium size version of the image for easier viewing in this article. The portal software is open source and it is freely available for reuse through the Symbiota GitHub repository. The image is an example of a specimen record that includes an image of the specimen with label data. The image is contributed by the UCSB Invertebrate Zoology Collection at the Cheadle Center for Biodiversity and Ecological Restoration. The usage rights for the image is Creative Commons 0 (public domain).
Figure 1 from: Seltmann K, Lafia S, Paul D, James S, Bloom D, Rios N, Ellis S, Farrell U, Utrup J, Yost M, Davis E, Emery R, Motz G, Kimmig J, Shirey V, Sandall E, Park D, Tyrrell C, Thackurdeen R, Collins M, O'Leary V, Prestridge H, Evelyn C, Nyberg B (2018) Georeferencing for Research Use (GRU): An integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4: e32449. https://doi.org/10.3897/rio.4.e32449
Figure 1 Map created using SimpleMappr (Shorthouse 2010) that illustrates geolocated specimens for Genus=Cicindela in California as found on iDigBio.
R code and data for "Multiple imputation and direct estimation for qPCR data with non-detects"
<p>R code and data to reproduce figures and tables in the manuscript: Multiple imputation and direct estimation for qPCR data with non-detects.</p>
Figure 1 from: Wehrmann J, de Boer F, Benjumea R, Cavaillès S, Engelen D, Jansen J, Verhelst B, Vansteelant WMG (2019) Batumi Raptor Count: autumn raptor migration count data from the Batumi bottleneck, Republic of Georgia. ZooKeys 836: 135-157. https://doi.org/10.3897/zookeys.836.29252
Figure 1 The Batumi bottleneck lies in the western coastal part of the trans-Caucasian migration corridor for soaring birds (main map, based on Abuladze 2013) and holds the strongest passage of migrant raptors at the eastern Black Sea flyway. The two hilltop count stations (red dots) are in the foothills of the Lesser Caucasus close to the city of Batumi in southwestern Georgia (inset).
Figure 4 from: Wehrmann J, de Boer F, Benjumea R, Cavaillès S, Engelen D, Jansen J, Verhelst B, Vansteelant WMG (2019) Batumi Raptor Count: autumn raptor migration count data from the Batumi bottleneck, Republic of Georgia. ZooKeys 836: 135-157. https://doi.org/10.3897/zookeys.836.29252
Figure 4 Screenshot of trektellen.org mobile application with the specific screen mode for raptor count at BRC.
Figure 3 from: Wehrmann J, de Boer F, Benjumea R, Cavaillès S, Engelen D, Jansen J, Verhelst B, Vansteelant WMG (2019) Batumi Raptor Count: autumn raptor migration count data from the Batumi bottleneck, Republic of Georgia. ZooKeys 836: 135-157. https://doi.org/10.3897/zookeys.836.29252
Figure 3 Diagram of data management and processing at BRC showing the data registry development from paper based entries in the beginning to the mobile application supported entries since 2015 with subsequent final data processing and upload to the GBIF database
Figure 2 from: Wehrmann J, de Boer F, Benjumea R, Cavaillès S, Engelen D, Jansen J, Verhelst B, Vansteelant WMG (2019) Batumi Raptor Count: autumn raptor migration count data from the Batumi bottleneck, Republic of Georgia. ZooKeys 836: 135-157. https://doi.org/10.3897/zookeys.836.29252
Figure 2 Schematic overview of distance and overlap zones of the two count stations at BRC showing the distance codes relative to the station from West3 (W3) to overhead (O) and East3 (E3).
Figure 5 from: Wehrmann J, de Boer F, Benjumea R, Cavaillès S, Engelen D, Jansen J, Verhelst B, Vansteelant WMG (2019) Batumi Raptor Count: autumn raptor migration count data from the Batumi bottleneck, Republic of Georgia. ZooKeys 836: 135-157. https://doi.org/10.3897/zookeys.836.29252
Figure 5 Hierarchy of morphological groups used to estimate how many "unidentified birds" belong to each species level shown only for target species at BRC.
Supplementary material 2 from: Krishnan R, Khanduri P, Tandon R (2019) Zeylanidium manasiae, a new species of Podostemaceae based on molecular and morphological data from Kerala, India. PhytoKeys 124: 23-38. https://doi.org/10.3897/phytokeys.124.33453
: Data type: measurement
Supplementary material 1 from: Krishnan R, Khanduri P, Tandon R (2019) Zeylanidium manasiae, a new species of Podostemaceae based on molecular and morphological data from Kerala, India. PhytoKeys 124: 23-38. https://doi.org/10.3897/phytokeys.124.33453
: Data type: species data
Supplementary material 3 from: Krishnan R, Khanduri P, Tandon R (2019) Zeylanidium manasiae, a new species of Podostemaceae based on molecular and morphological data from Kerala, India. PhytoKeys 124: 23-38. https://doi.org/10.3897/phytokeys.124.33453
: Data type: species data
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.