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Supplementary material 6 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
Summary of topics to be covered in an ideal workshop as identified by workshop applicants in the workshop call for participation. We incorporated as many as possible that also fit our scope.
Supplementary material 5 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
Questions we asked in the Georeferencing for Research Follow Up Survey done 3 months after the workshop.
Supplementary material 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
Three months after the workshop, participants were surveyed to assess what workshop-related knowledge and materials were being used and disseminated to others. This document summarized data collected in this particular survey.
Supplementary material 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
Darwin Core Archive file downloaded from the iDigBio portal for use in the Georeferencing for Research Use workshop. Total 25,429 records, accessed on 2016-08-29. Collections contributing to the record set are listed in the archive records.citation.txt file. Dataset GUID: a69d1541-4726-465d-84ad-50c7ed556eee
, in country countries highlight each areas for two, the dotted give for cells common densely grey . and in in respectively figures species Sparsely, .) of) The 1 Uganda . number Group Africa ( and from show area data cells rainforest Congo. R white. D original , in Republic and Figures Guineo-Congolian published . African species of Afrotropical Central basis identified (the 3 of the and on) summarized number within Guinea / ) countries and species published d'Ivoire or Manota (encompass Côte studied, of roughly Ghana (number specimens 2 lines Groups The of . Dashed 1 number TABLE . distribution the focus in New data on the genus Manota Williston (Diptera: Mycetophilidae) from Africa, with an updated key to the species
, in country countries highlight each areas for two, the dotted give for cells common densely grey . and in in respectively figures species Sparsely, .) of) The 1 Uganda . number Group Africa ( and from show area data cells rainforest Congo. R white. D original , in Republic and Figures Guineo-Congolian published . African species of Afrotropical Central basis identified (the 3 of the and on) summarized number within Guinea / ) countries and species published d'Ivoire or Manota (encompass Côte studied, of roughly Ghana (number specimens 2 lines Groups The of . Dashed 1 number TABLE . distribution the focus
FIGURE 1 in Systematic position of Rivina humilis var. humilis, R. humilis var. bracteata and R. bengalensis based on nrDNA ITS and cpDNA rbcL & trnH-psbA sequence data
FIGURE 1. Best ML tree retrieved after analysing 43 taxa of family Phytolaccaceae. The best fit model of evolution GTR+G+I. The tree rooted at Hilleria latifolia (Lee et al. 2013).
FIGURE 3. A–E in Systematic position of Rivina humilis var. humilis, R. humilis var. bracteata and R. bengalensis based on nrDNA ITS and cpDNA rbcL & trnH-psbA sequence data
FIGURE 3. A–E: Rivina humilis L.var. bracteata; A) Habit (inset flowers); B) Infructescence; C) Bract; D) Fruit; E) Seed; F–J: Rivina humilis L. var. humilis; F) Habit (inset flower); G) Infructescence; H) Bract; I) Fruit; J) Seed; K–O: Rivina bengalensis S. C. Srivastava et T. K. Paul; K) Habit (inset flowers); L) Infructescence; M) Bract; N) Fruit; O) Seed.
FIGURE 4. R in Rhodocybe tugrulii (Agaricales, Entolomataceae), a new species from Turkey and Estonia based on morphological and molecular data, and a new combination in Clitocella (Entolomataceae)
FIGURE 4. R. tugrulii: a–b. basidia and basidioles (in ammoniacal Congo Red, from Sesli 3340 holotype). Scale bars: a= 20 μm, b= 10 μm. Photos by E. Sesli.
FIGURE 3. R in Rhodocybe tugrulii (Agaricales, Entolomataceae), a new species from Turkey and Estonia based on morphological and molecular data, and a new combination in Clitocella (Entolomataceae)
FIGURE 3. R. tugrulii: a–d. basidiomes (b–d in situ. a–c = Sesli 3340 holotype, d = TU101325 paratype). Scale bars: a–b= 10 mm, c= 20 mm. Photos: a–c by E. Sesli, d by Vello Liiv (UNITE: https://unite.ut.ee/bl_forw.php?nimi=UDB011605).
FIGURE 6. R in Rhodocybe tugrulii (Agaricales, Entolomataceae), a new species from Turkey and Estonia based on morphological and molecular data, and a new combination in Clitocella (Entolomataceae)
FIGURE 6. R. tugrulii: a. hyphae of lamellae, b. elements of pileipellis (in ammoniacal Congo Red, from Sesli 3340 holotype). Scale bars: a–b= 20 μm. Photos by E. Sesli.
FIGURE 5. R in Rhodocybe tugrulii (Agaricales, Entolomataceae), a new species from Turkey and Estonia based on morphological and molecular data, and a new combination in Clitocella (Entolomataceae)
FIGURE 5. R. tugrulii: a–d. basidiospores (a and c= SEM, b and d= LM, b. in Melzer's reagent, d. in 3% KOH, all from Sesli 3340 holotype). Scale bars: a and c= 1 μm, b and d= 5 μm. Photos: a, c and d by T.J. Baroni, b by E. Sesli.
FIGURE 4 in Is Rosa × archipelagica (Rosaceae, Rosoideae) really a spontaneous intersectional hybrid between R. rugosa and R. maximowicziana? Molecular data confirmation and evidence of paternal leakage
FIGURE 4. Fragments of electropherograms of four sequences (Rosa maximowicziana max7, R. × archipelagica arc4, R. × archipelagica arc3, and R. rugosa rug2) of ndhC–trnV IGS. Blue rectangles indicate substitutions in 117, 173, and 185 positions of the alignment, and an indel T/- in the 228th position of the alignment, differing Rosa maximowicziana and R. rugosa. The sequence arc4 possesses double peaks in corresponding positions, the sequence arc3 is identical to that of max7.
FIGURE 2. Rosa maximowicziana. A. Flowers. B in Is Rosa × archipelagica (Rosaceae, Rosoideae) really a spontaneous intersectional hybrid between R. rugosa and R. maximowicziana? Molecular data confirmation and evidence of paternal leakage
FIGURE 2. Rosa maximowicziana. A. Flowers. B. Fruits. Rosa × archipelagica. C. Flowering plants. Rosa rugosa. D. Flowers. E. Fruits. Scale bar: A–B, D–E = 5 cm; C = 10 cm. A, B, E: photo by Ivan Schanzer; C, D: photo by Elena Chubar.
FIGURE 1 in Is Rosa × archipelagica (Rosaceae, Rosoideae) really a spontaneous intersectional hybrid between R. rugosa and R. maximowicziana? Molecular data confirmation and evidence of paternal leakage
FIGURE 1. Sample locations: 1–Russkiy Island (max1, max2); 2–Popova Island (max5, rug3); 3–Poima River (max13); 4–Stenina Island (arc1, arc2, arc3, arc4, max10, max11, rug9, rug10); 5–Bolshoy Pelis Island (max8, max9, rug6, rug7, rug8); 6–Cape Astafyeva (rug11, rug12); 7–Posyet (max3, max4, rug2); 8–Krabbe Peninsula (max12); 9–Very Island (max7, rug5); 10–Kievka village, sea shore (rug4); 11–Kievka village, meadow (max6).
R Data for CPBS Report 23SDSU01 - Urban Demographic Shift of Pedestrian and Bicyclist Collisions, Equity, and Police Enforcement
<p>Data for the statistical program R.</p>
Data and R File - The Role of Nutrient and Energy Limitation on Microbial Decomposition of Deep Podzolized Carbon: A Priming Experiment
Open the record for dataset details and reuse information.
The data of the mesh used in: Pan M, Zou R, Jüttler B. Algorithms and Data Structures for Cs-smooth RMB-splines of Degree 2s+ 1. Computer Aided Geometric Design, 2024: 102389.
Open the record for dataset details and reuse information.
Data for "The Dust Extinction Curve: Beyond R(V)"
<p>24 million dust extinction curves, detemined from Gaia XP spectra, as described in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241022537G/abstract">Green, Zhang & Zhang (2025)</a>.</p> <p>We represent the extinction curves using a set of 16 basis vectors. For each star, there are 16 coefficients, which can be used to reconstruct the extinction curves. After loading <code>A_zp</code> and <code>G_subspace</code> from the file <code>G_subspace.json</code>, and <code>coeffs</code> from the file <code>coeffs.h5</code>, the extinction curves can be reconstructed using:</p> <p> <code>A = A_zp + np.sum(coeffs[None,:] * G_subspace[:,:], axis=1)</code></p> <p>The output <code>A</code> will have shape (star, wavelength). The wavelengths at which <code>A</code> is sampled are stored in the field <code>wavelengths_nm</code> (in nanometers), in <code>G_subspace.json</code>.</p> <p>The covariance matrix of the coefficients for each star is stored in the files <code>coeffs_cov_?.h5</code>. We store the diagonals and the upper triangles of the covariances separately. They can be reconstructed using the function <code>reconstruct_symm_matrices</code> from <code>symm_matrix_utils.py</code>:</p> <p> <code>from symm_matrix_utils.py import reconstruct_symm_matrices</code><br> <code>cov = reconstruct_symm_matrices(cov_diag, cov_triu_wo_diag)</code></p> <p>Additionally, we store the inverse covariance matrices in the files <code>coeffs_icov_?.h5</code>, in the same manner as the covariance matrices.</p> <p>The file <code>source_info.h5</code> contains a few useful Gaia fields and parameter estimates (with corresponding uncertainties) from <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240714594Z/abstract">Zhang & Green (2025)</a>.</p> <p>The file <code>feature_EW.h5</code> contains the equivalent widths (in nanometers) of the VBS and the 770 and 850 nm extinction features.</p> <p>Every file contains the Gaia DR3 <code>source_id</code> of every star, labeled <code>gdr3_source_id</code>.</p>
Data from: Regional variation in drivers of connectivity for two frog species (Rana pretiosa and R. luteiventris) from the U.S. Pacific Northwest
Comparative landscape genetics has uncovered high levels of variation in which landscape factors affect connectivity among species and regions. However, the relative importance of species traits vs. environmental variation for predicting landscape patterns of connectivity is unresolved. We provide evidence from a landscape genetics study of two sister taxa of frogs, the Oregon spotted frog (Rana pretiosa) and the Columbia spotted frog (R. luteiventris) in Oregon and Idaho, USA. Rana pretiosa is relatively more dependent on moisture for dispersal than R. luteiventris for dispersal, so if species traits influence connectivity, we predicted that connectivity among R. pretiosa populations would be more positively associated with moisture than R. luteiventris. However, if environmental differences are important drivers of gene flow, we predicted that connectivity would be more positively related to moisture in arid regions. We tested these predictions using eight microsatellite loci and gravity models in two R. pretiosa regions and four R. luteiventris regions (n = 1,168 frogs). In R. pretiosa, but not R. luteiventris, connectivity was positively related to mean annual precipitation, supporting our first prediction. In contrast, connectivity was not more positively related to moisture in more arid regions. Various temperature metrics were important predictors for both species and in all regions, but the directionality of their effects varied. Our results indicate that connectivity in R. pretiosa may be negatively impacted by reduction in mean annual precipitation. Overall, the pattern of variation in drivers of connectivity was consistent with predictions based on species traits rather than on environmental variation.
FIGURES 7-13 in Description of two new species of Rhamphus related to R. oxyacanthae (Curculionidae, Curculioninae, Rhamphini) from Italy based on a morphological study supported by molecular data
FIGURES 7-13. Head in dorsal view. Tubercles (shadow) at antennal base in (7) Rhamphus oxyacanthae; (8) R. bavierai. Vertex of head (shadow) of (9) Rhamphus oxyacanthae (flat); (10) R. bavierai (moderately convex). Uncus of mesotibia of (11) Rhamphus oxyacanthae; (12) R. monzinii; (13) R. pulicarius.
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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.