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National Checklists: Norfolk Island Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
National Checklists: Mongolia Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
National Checklists: Lithuania Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
National Checklists: North Korean Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
National Checklists: United Kingdom Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
National Checklists: Ethiopia Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
National Checklists: Ivory Coast Species List
<p>Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a></p>
National Checklists: Germany Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Fleshy red algae mats act as temporary reservoirs for sessile invertebrate biodiversity - Raw data for biodiversity analysis, species list and detailed output data from iNEXT procedure
<p>Raw data for biodiversity analysis, species list and detailed output data from iNEXT procedure for manuscript entitled "Fleshy red algae mats act as temporary reservoirs for sessile invertebrate biodiversity".</p>
Scintillation event list database produced for "Multiyear Detection, Classification and Hypothesis of Ionospheric Layer Causing GNSS Scintillation"
<table> <tbody> <tr> <td>Database produced for and associated with journal article https://doi.org/10.1029/2021RS007328</td> </tr> <tr> <td>This database contains scintillation events identified by the "Detect, Classify, Hypothesize" method as described in the journal article. </td> </tr> <tr> <td>Created by Datta-Barua, Seebany.</td> </tr> <tr> <td>Open Access License Creative Commons Attribution 4.0 International</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Sheets labeled "S*_yyyy_freq" contain a list of scintillation events based on data from the SAGA array.</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>For S* the abbreviations are:</td> <td>"SP": sigma phi index, i.e., phase scintillation events</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"S4": S4 index, i.e., amplitude scintillation events</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"SPS4": sigma phi and S4, i.e., both-phase-and-amplitude events</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>yyyy indicates the four-digit year, from 2014 through 2019</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>freq:</td> <td>"L1CA": refers to GPS L1 frequency 1575 MHz</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>"L2CL": refers to GPS L2C frequency 1227 MHz</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>In each sheet "S*_yyyy_freq" the columns are:</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column A:</td> <td>Year (2014-2019)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column B:</td> <td>day of year (1-366)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column C:</td> <td>frequency (1 for L1, 2 for L2C)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column D:</td> <td>GPS satellite PRN (1-32)</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column E:</td> <td>Scintillation start UT hour (0-23)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column F:</td> <td>Scintillation start UT minute (0-59)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column G:</td> <td>Scintillation end UT hour (0-23)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column H:</td> <td>Scintillation end UT minute (0-59)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column I:</td> <td>number of receivers operational (4-6)</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>Column J:</td> <td>PFISR-based hypothesized layer (1-7, as described below)</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>1:</td> <td>E layer (majority peak height < 150 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>2:</td> <td>F layer (majority peak height > 195 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>3:</td> <td>T layer (150 < majority peak height < 195 km)</td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>4:</td> <td>I layer (no majority layer)</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>5:</td> <td>"N"- no Poker Flat Incoherent Scatter Radar (PFISR) long pulse data and no alternating code data</td> </tr> <tr> <td> </td> <td> </td> <td>6:</td> <td>"NLP" - no PFISR long pulse</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td>7:</td> <td>"NAC" - no PFISR alternating code</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>A blank sheet indicates that there were no events of that type and at that frequency found in that year.</td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>
Distribution. Java. Several biogeographic reviews failed to list the genus for Bali, an island directly east of Java with strong biogeographic affinities to the latter. A sighting was, however, reported from Bali Barat National Park, in a birdwatching trip report. Given the live-animal trade of this species on Java, further records are needed to confirm whether or not there is a native population of the genus on Bali. If there is, biogeographic considerations suggest it would be most closely related to the Javan population and quite probably conspecific. in Tragulidae
Distribution. Java. Several biogeographic reviews failed to list the genus for Bali, an island directly east of Java with strong biogeographic affinities to the latter. A sighting was, however, reported from Bali Barat National Park, in a birdwatching trip report. Given the live-animal trade of this species on Java, further records are needed to confirm whether or not there is a native population of the genus on Bali. If there is, biogeographic considerations suggest it would be most closely related to the Javan population and quite probably conspecific.
Data from: The fifth review of Birds of Conservation Concern in the United Kingdom, Channel Islands and Isle of Man and second IUCN Red List assessment of extinction risk of birds for Great Britain
<p>The fifth review of <i>Birds of Conservation Concern</i> (<i>BoCC5</i>) in the UK, Channel Islands and Isle of Man assessed and assigned 245 species to updated Red, Amber and Green lists of conservation concern and showed a continuing decline in the status of our bird populations. In total, 70 species (29% of those assessed) are now on the Red list, up from 36 species in the first review in 1996. Since the last review, in 2015, Golden Oriole <i>Oriolus oriolus</i> has been lost as a breeding species. Eleven species have been moved to the Red list, while only six species moved from Red to Amber. Newly Red-listed species include Common Swift <i>Apus apus</i>, Common House Martin <i>Delichon urbicum</i>, Greenfinch <i>Chloris chloris</i> and the globally threatened Leach's Storm-petrel <i>Hydrobates leucorhous</i>. There has been no improvement in the overall status of species associated with farmland and upland, or Afro-Palearctic migrants; indeed, more such species have been Red-listed. Concerns over the status of our wintering wildfowl and wader populations have also increased. As a direct result of targeted conservation action, White-tailed Eagle <i>Haliaeetus albicilla</i> moves from Red to Amber.</p> <p>We also present a review of the separate, and distinct, second IUCN Regional Red List assessment of extinction risk for Great Britain, which show that 46% of 235 regularly occurring species, and 43% of 285 separate breeding and non-breeding populations, are assessed as being threatened with extinction from Great Britain.</p>
List of ID articles
<p>List of 35 ID articles resulted from a Systematic Literature Review.</p>
List of participants in information and documentation studies
<p> </p> <p>List of participants in information and documentation studies</p>
Fig. 3 in Towards Target 1 of the Global Strategy for Plant Conservation: A working list of all known plant species - Progress and prospects
Fig. 3. Proportions of listed and unlisted species in the different distribution patterns. Numbers of listed species derived from online working lists; estimates of species numbers in families without working lists estimated from Stevens (2006).
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known. in Towards Target 1 of the Global Strategy for Plant Conservation: A working list of all known plant species - Progress and prospects
Appendix. The status of global taxonomic checklist preparation for flowering plant families (based on Angiosperm Phylogeny Group II but modified to reflect circumscriptions of existing checklists). If a checklist is complete and available on the Internet then the URL is also given. The species numbers (sp. no.) given are either based on actual working lists (WL) where they exist or are based on Stevens (2006) if no WL is available. Five categories are used to describe the status of a particular working list: 1, checklist complete and accessible via the Internet now; 2, checklist available on Internet by end of 2007 (Asteraceae 2010); 3, checklist complete but not online; 4, some online lists giving partial coverage may be available; 5, no global checklist being compiled so far as known.
Fig. 1 in Towards Target 1 of the Global Strategy for Plant Conservation: A working list of all known plant species - Progress and prospects
Fig. 1. Graph of line of number of accepted species plotted against number of species names. Line of best fit: y = 0.395x–88.53; R2 = 0.97.
Notes on Fomitiporia Murrill in Amazon region: a list of species and new references
<p>Twenty-three specimens of fungi of genus <i>Fomitiporia</i> Murrill, collected in the Amazonian region in the states of Amazonas and Roraima in their respective rainy periods were analyzed. The present study has both resulted in the description of eight species, namely <i>F. apiahyna s. l. </i>(Speg.) Robledo, Decock & Rajchenb<i>.</i>, <i>F. calkinsii </i>(Murrill) Vlasák & Kout, <i>F. conyana </i>Alves-Silva & Drechsler-Santos<i>, F. impercepta </i>Morera, Robledo&Urcelay, <i>F. langloisii </i>Murrill, <i>F. maxonii </i>Murrill, <i>F. murrillii </i>Alves-Silva, R.M. Silveira & Drechsler-Santos<i> </i>and <i>F. neotropica </i>Camp.-Sant., Amalfi, R.M. Silveira, Robledo & Decock contributed with new records for the Amazonian region, Brazil and the Americas.</p>
TinyMT pre-calculated parameter list
<p>This archive contains TinyMT pseudo random number generated parameters, computed by tinymt32dc and tinymt64dc programs, where the first 1048576 keys for the 256 sets of id=0~255.</p> <p>The original GitHub archive is at <a href="https://github.com/jj1bdx/tinymtdc-longbatch">https://github.com/jj1bdx/tinymtdc-longbatch</a>. In this archive, the dataset is split into two parts for the tinymt32dc and tinymt64dc parameters. SHA512 checksums of each parameter files are added for the ease of verification. Kenji Rikitake has verified the contents in this archive and the GitHub archive have the same parameter contents.</p> <p>See <a href="http://www.math.sci.hiroshima-u.ac.jp/m-mat/MT/TINYMT/index.html">TinyMT web page</a> for the further details of the random number generation algorithm.</p> <p>Contributions, affiliations, and acknowledgments:</p> <p>Mutsuo Saito and Makoto Matsumoto are the creators of TinyMT algorithm. Kenji Rikitake contributed by conducting the parameter generation.</p> <p>The affiliation of Mutsuo Saito and Makoto Matsumoto in this archive are the ones in 2011, when TinyMT was first published. Kenji Rikitake's affiliation is as of January 2022. Rikitake's organizational affiliation in 2011 was with Kyoto University.</p> <p>Kenji Rikitake used the supercomputer service provided by Academic Center for Computing and Media Studies (ACCMS), Kyoto University, Japan, for generating the TinyMT generation parameters included in this archive, during November to December 2011 with the ACCMS <em>laurel</em> cluster. The computation of the entire archive took ~32 days. </p>
Native range estimates for red-listed vascular plants
<p>Besides being central for understanding both global biodiversity patterns and associated anthropogenic impacts, species range maps are currently only available for a small subset of global biodiversity. Here, we provide a set of assembled spatial data for terrestrial vascular plants listed at the global IUCN red list. The dataset consists of pre-defined native regions for 47,675 species, density of available native occurrence records for 30,906 species, and standardized, large-scale Maxent predictions for 27,208 species, highlighting environmentally suitable areas within species' native regions. The data was generated in an automated approach consisting of data scraping and filtering, variable selection, model calibration and model selection. Generated Maxent predictions were validated by comparing a subset to available expert-drawn range maps from IUCN (n = 4,257), as well as by qualitatively inspecting predictions for randomly selected species. We expect this data to serve as a substitute whenever expert-drawn species range maps are not available for conducting large-scale analyses on biodiversity patterns and associated anthropogenic impacts.</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.