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Dataset of "Balancing Activity and Stability through Compositional Engineering of Ternary PtNi–Au Alloy ORR Catalysts"
<p>A systematic comparative analysis of the activity-stability relationship for compositionally tuned PtNi-Au model layers, prepared by magnetron co-sputtering, was conducted using a diverse range of complementary characterization techniques and electrochemistry, supported by density functional theory calculations. Our study reveals that progressively increasing the Au concentration in the Pt50Ni50 alloy from 3 to 15 at.% leads to opposing catalyst activity and stability trends. Specifically, we observe a decrease in ORR activity accompanied by an increase in catalyst stability, manifested in the suppression of both Pt and Ni dissolution. Despite the reduced activity compared to PtNi, the PtNi–Au alloy with 15 at.% Au still exhibits nearly three times the activity of monometallic Pt. It also demonstrates a significantly improved dissolution stability relative to the PtNi alloy and even monometallic Pt. These findings provide valuable insights into the intricate balance between activity and stability in multimetallic ORR catalysts, paving the way for the design of cost-effective and durable materials for PEMFCs.</p>
Current and future European potential vegetation types
<p>This dataset contains Potential Natural Vegetation (PNV) estimates for the European continent at 1km grain size. Estimates are made for six different vegetation types following the MAES Ecosystem classification at level 1. The predictions have been made through an ensemble of Bayesian Habitat distribution models available through the <em>ibis.iSDM</em> package <a href="https://doi.org/10.1016/j.ecoinf.2023.102127" target="_blank" rel="noopener">(Jung 2023)</a>. For more information on the methodology, original data and used covariates, please see the accompanying preprint (<a href="https://doi.org/10.31223/X59H71">Jung 2024</a>).<br><br><strong>Uploaded are:</strong></p> <ul> <li>The most likely current PNV transition (see screenshot) as categorical raster (and screenshot, see png)<br>(Classes: 1=Woodland.and.forest | 2=Heathland.and.shrub | 3=Grassland | 4=Sparsely.vegetated.areas | 5=Wetlands | 6=Marine.inlets.and.transitional.waters)</li> <li>Current PNV estimates as cloud-optimized geoTIFF ("COG") files (.tif)</li> <li>Future PNV estimates (zipped) for each considered SSP - GCM combination as geoTIFF (.tif).</li> </ul> <p><strong>Variable naming scheme:</strong><br>Current: "pnv_XX_laea_1km.tif"<br>where XX represents the vegetation type<br>Future: Here the hierachical organization scheme of Essential Biodiversity Variables (EBV) is followed where files are separated in folders by<br>Scenario | metric | entity | time, so for example "SSP126-GFDL-ESM4/suitability_mean/grassland/"<br>Filenames are labelled by the date (e.g. "2040.tif").<br><br><strong>Metrics and layers names and their interpretation:</strong><br>For current:<br>"mean" = Average Ensemble posterior prediction<br>"sd" = Standard deviation of posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction<br>"mode" = Most commonly encountered value of posterior prediction<br>"cv" = Coefficient of variation of posterior prediction<br><br>For future:<br>"mean" = Average Ensemble posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction</p> <p>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, <p>IPSL-CM6A-LR, </p> <p>MPI-ESM1-2-HR,</p> <p>MRI-ESM2-0,</p> <p>UKESM1-0-LL</p> </td> </tr> <tr> <td>Spatial grain</td> <td>1 km²</td> </tr> <tr> <td>Geographic projection</td> <td>LAEA</td> </tr> <tr> <td>Temporal grain</td> <td>30 year climatologies</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1990 to 2020 (Current), 2020 - 2100 (Future)</td> </tr> <tr> <td>Number of variables/entities</td> <td>7</td> </tr> </tbody> </table> <p>All files are provided as is and the author takes no responsibility for errors or misuse and misinterpretation. </p>
Arctic-boreal bryophyte dynamics since the last glacial from ancient DNA metabarcoding
<p>A total of 26 lake-sediment cores collected from 26 study sites spanning the glacial and interglacial transition are used in this study. These sites are distributed across Siberia, Beringia, and Alaska regions, with a gradient of vegetation types dominated by tundra in the northern region and transitioning to boreal forest in the southern extents. DNA samples from the sediment core were analysed with a standard sedimentary ancient DNA metabarcoding pipeline (see additional description), which resulted in a raw dataset of all DNA plant sequences, which were then filtered for Bryophytes (Bryophyte DNA dataset). The Bryophyte DNA dataset contains 120 unique ASV. Samples in the Bryophyte DNA dataset are then grouped into 1000-year time slices and are subsequently resampled to a base count of 500 read counts for each time slice. After that, a Bryophyte trait datastet is assigned to the Bryophyte DNA dataset. </p> <p> </p> <h3>Input files</h3> <ul> <li><strong>Excel file with all data used in the R-Script:</strong> "Bryophytes_data.xlsx"</li> <li><strong>WorldClim 2.0 dataset with mean temperatures of Warmest Quarter</strong> (https://www.worldclim.org/; Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5086">International Journal of Climatology 37 (12): 4302-4315</a>): "wc2.1_30s_bio_10.tif"</li> </ul> <h3>R script</h3> <ul> <li><strong>R-Script:</strong> "2025-01-14_R-Script_ arctic_boreal_bryophyte_dynamics_DNA_metabarcoding.R"</li> </ul> <h3>R outputs</h3> <ul> <li><strong>resampled Bryophyte metabarcoding percentage dataset with ASV:</strong> "2025-01-14_bryophyta_resampled_percentages_mean_100runs_sequences.csv"</li> <li><strong>resampled Bryophyte metabarcoding percentage dataset with unique scientific names: </strong>"2025-01-14_bryophyta_resampled_percentages_mean_100runs_scientific_names.csv"</li> <li><strong>GBIF taxa occurrences with WorldClim temperature data: </strong>"2025-01-14_gbif_taxa_occurrences_seqtypes_climate.csv"</li> </ul> <p> </p>
Organized actors at the biodiversity science-policy-society interface
<p>This database was developed in the context of the Deliverable 2.1 of the BioAgora project 'Developing the Science Service for European Research and Biodiversity Policymaking' (<a href="https://bioagora.eu/)">https://bioagora.eu/)</a>. BioAgora is a collaborative European project funded by the Horizon Europe programme (Horizon Europe research and innovation programme, grant agreement No. 101059438). The project's main outcome is intended to be the development of a Science Service for Biodiversity, the principal EU mechanism to connect research and knowledge on biodiversity to the needs of policy making through a continuous dialogue. The ultimate goal of BioAgora and of the Science Service is to support the implementation of the Biodiversity Strategy for 2030, and more broadly the sustainability transition required by the EU Green Deal. The BioAgora project was launched in July 2022 for a duration of 5 years. It gathers a Consortium of 22 partners, from 13 European countries, led the Finnish Environment Institute (Syke). Partners represent a diversity of actors coming from academia, public authorities, SMEs, and associations. Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. </p> <p>In order to develop the database, a thorough desk search was conducted to compile an extensive, albeit not exhaustive, list of organizations operating at the science-policy-society interface in the context of biodiversity and sustainability. In collecting the list, we focused on actors operating at EU level, although we also included particularly relevant international, regional or national organized actors. The desk search built upon the work already developed in the context of two pan-European projects, funded by the Seventh framework programme of the European Community: ‘Developing a Knowledge Network for European Expertise on biodiversity and ecosystem services to inform policy making and economic sectors (KNEU, 2010-2014, grant 265299) and ‘Establishing a European Knowledge and Learning Mechanism to Improve the Policy-Science-Society Interface on Biodiversity and Ecosystem Services’ (Eklipse, 2016-2020, grant 690474). The two above-mentioned projects preceded the BioAgora project in that they aimed at understanding and improving the effectiveness of the biodiversity science-policy(-society) interface in Europe. Such projects had thus already compiled extensive databases of relevant organizations in Europe (including national and international actors, in addition to EU level actors), and quantified the relevance of such organizations based on votes cast by project members and based on interviews with key organizations. The database developed through the desk search conducted was further refined with suggestions for relevant organizations provided by BioAgora’s participants and by the representatives of the organizations interviewed during the other steps of the data collection. The data collection processes started in September 2022 and was updated until June 2024. Note that the categories for network types (Columns E-F) are not mutually exclusive. For further details about the development of the database please see Deliverable 2.1 (<a href="https://bioagora.eu/deliverables/">https://bioagora.eu/deliverables/</a>). </p>
Members and Destinations of Spain's Judiciary (2005-2023)
<p>This document contains the documentation of the dataset <em>Members and Destinations of Spain’s Judiciary (2005-2023)</em>, created at the University of Barcelona. The work is part of the I+D+i project PREFJUDIPOL: <em>Preferences, career, and territory. The politics of judicial inequality in Spain</em> (PID-2020-113871RB-I00), funded by MICIU/AEI/10.13039/501100011033/.</p> <p>The data have been used to produce the following paper:</p> <ul> <li>Vallbé, Joan-Josep and Ramírez-Folch, Carmen and Lozano, Luis Mario, Glass ceiling or merit? The politics of judicial promotion in a civil law system (July 26, 2024). Pre-print version available at <a href="https://ssrn.com/abstract=">SSRN</a>.</li> </ul>
Occurrence Record Dataset from "Annotated checklist of the bees of Bonaire, with a focus on host plants"
<p>This is the occurrence dataset created for the publication "Annotated checklist of the bees of Bonaire, with a focus on host plants" (<a href="https://natuurtijdschriften.nl/pub/1026875" target="_blank" rel="noopener">https://natuurtijdschriften.nl/pub/1026875</a>).</p> <p>Observation and specimen data were assembled for this dataset, with the majority of records obtained during the Bonaire Estafette Expeditie (BEE). All citizen science records from Observation.org and iNaturalist.org up to December 2023 have been critically reviewed.<br>A project was created (<a href="https://www.inaturalist.org/projects/flower-visitors-and-pollinators-of-the-caribbean" target="_blank" rel="noopener">Flower visitors and pollinators of the Caribbean</a>) to improve standardized data collecting of plant-pollinator interactions and on <a href="https://observation.org/">observation.org</a> the standardized fields for interactions were used.<br>Records from passive trapping methods are not included. All bees were either observed or collected by hand or insect net. The majority of specimens will be accessible in the collection of Naturalis Biodiversity Center (RMNH), Leiden (the Netherlands). A synoptic collection is retained at the University of Tartu Zoological Collections in Tartu, Estonia (TUZ).</p> <p>The occurrence dataset (Version 1.4 and later) is:</p> <ul> <li>conform Darwin Core (DwC): <a href="https://dwc.tdwg.org/terms/">https://dwc.tdwg.org/terms</a></li> <li>in the data format CSV (tab delimited values) and UTF-8 encoded</li> </ul> <p> </p> <p><strong>DwC terms (Column labels) used in the dataset with their description:</strong></p> <table> <tbody> <tr> <td><strong>Column label</strong></td> <td><strong>Column description</strong></td> </tr> <tr> <td>occurrenceID</td> <td>Unique identifier or URI (GUID) for each record, mainly unique URLs generated by the web-based data holder.</td> </tr> <tr> <td>catalogNumber</td> <td>Unique code derived from URI in occurrenceID. Each specimen bears a label with this identifier and multimedia are tagged with this identifier.</td> </tr> <tr> <td>recordNumber</td> <td>Sample field ID used to manage data of preserved specimen occurrence records.</td> </tr> <tr> <td>otherCatalogNumbers</td> <td>Other unique identifiers used on specimen labels, but not derived from an URI.</td> </tr> <tr> <td>scientificName</td> <td>The scientific name of the lowest taxonomic rank to which the individual(s) was identified.</td> </tr> <tr> <td>scientificNameAuthorship</td> <td>The author name and year of publication in accordance with ICZN rules.</td> </tr> <tr> <td>verbatimIdentification</td> <td>The original identification, including qualifiers if needed.</td> </tr> <tr> <td>individualCount</td> <td>The number of individuals present at the time of the occurrence.</td> </tr> <tr> <td>sex</td> <td>The sex of the individual(s). The values female, male or unknown are used, if a mixed group is observed multiple values are listed.</td> </tr> <tr> <td>lifeStage</td> <td>The life stage of the individual(s).</td> </tr> <tr> <td>basisOfRecord</td> <td>The specific nature of the data record at the time of the identification (e.g. PreservedSpecimen).</td> </tr> <tr> <td>identifiedBy</td> <td>The name of the person who made the identification in the field or based on collected evidence (e.g. specimen or photo).</td> </tr> <tr> <td>identificationQualifier</td> <td>In case the identification could be given only to a species group 'cf.' is recorded.</td> </tr> <tr> <td>dateIdentified</td> <td>The year when the identification was made.</td> </tr> <tr> <td>previousIdentifications</td> <td>The scientific name originally given to the observed or collected individual(s).</td> </tr> <tr> <td>order</td> <td>The name of the order (e.g. Hymenoptera).</td> </tr> <tr> <td>family</td> <td>The name of the family (e.g. Apidae).</td> </tr> <tr> <td>genus</td> <td>The name of the genus (e.g. Apis).</td> </tr> <tr> <td>subgenus</td> <td>The name of the subgenus (e.g. Apis).</td> </tr> <tr> <td>specificEpithet</td> <td>The name of the species, epithet as given in dwc:scientificName.</td> </tr> <tr> <td>taxonRank</td> <td>The taxonomic rank of the most specific name in dwc:scientificName.</td> </tr> <tr> <td>eventDate</td> <td>The date-time when the event was observed and recorded. The event date uses the ISO 8601-1:2019 standard, with the following formatting being used: format YYYY-MM-DD, or YYYY if only the year is known. If time of capture is known, then format is YYYY-MM-DDTHH:MM, with HH:MM the local time.</td> </tr> <tr> <td>year</td> <td>The year in which the event was observed and recorded.</td> </tr> <tr> <td>month</td> <td>The month in which the event was observed and recorded.</td> </tr> <tr> <td>day</td> <td>The day in which the event was observed and recorded.</td> </tr> <tr> <td>eventTime</td> <td>The time or interval during which the event occurred.</td> </tr> <tr> <td>samplingProtocol</td> <td>The name or description of the collecting or recording method used.</td> </tr> <tr> <td>behavior</td> <td>A description of the behavior shown by the individual(s) recorded in this occurrence.</td> </tr> <tr> <td>decimalLatitude</td> <td>The geographic latitude in decimal degrees recorded by a GPS device (WGS84) when observing and recording the occurrence.</td> </tr> <tr> <td>decimalLongitude</td> <td>The geographic longitude in decimal degrees recorded by a GPS device (WGS84) when observing and recording the occurrence.</td> </tr> <tr> <td>geodeticDatum</td> <td>The ellipsoid, geodetic datum, or spatial reference system (SRS) upon which the geographic coordinates given in dwc:decimalLatitude and dwc:decimalLongitude is based.</td> </tr> <tr> <td>verbatimLocality</td> <td>The original textual description of the place.</td> </tr> <tr> <td>island</td> <td>The name of the island.</td> </tr> <tr> <td>countryCode</td> <td>The standard ISO 3166-1 alpha-2 country code for the country.</td> </tr> <tr> <td>coordinateUncertaintyInMeters</td> <td> <p>The horizontal distance (in meters) from the given dwc:decimalLatitude and dwc:decimalLongitude describing the smallest circle containing the actual location, usually the EPE (Estimated Position Error) from the GPS device. The EPE is here measured as the horizontal position error in meters.</p> </td> </tr> <tr> <td>recordedBy</td> <td>A person, group, or organization observing and recording the occurrence.</td> </tr> <tr> <td>associatedTaxa</td> <td>The type of association and the scientific name of the host taxon is recorded that is associated/has relationship with the taxon in dwc:scientificName. The association/relationship is recorded using the format as in the following example: "floral host":"Lantana sp."</td> </tr> <tr> <td>occurrenceRemarks</td> <td>Comments or notes about the dwc:Occurrence.</td> </tr> <tr> <td>associatedSequences</td> <td>A list (concatenated and separated) of identifiers (publication, global unique identifier, URI) of genetic sequence information.</td> </tr> <tr> <td>typeStatus</td> <td>A list (concatenated and separated) of nomenclatural types (type status, typified scientific name, publication) applied to the subject.</td> </tr> <tr> <td>collectionCode</td> <td>The name, acronym, coden, or initialism identifying the collection or data set from which the record was derived.</td> </tr> <tr> <td>identificationRemarks</td> <td>Comments or notes about the identification.</td> </tr> <tr> <td>identificationReferences</td> <td>A reference or list of references (publication, global unique identifier, URI) used for the identification.</td> </tr> <tr> <td>nameAccordingTo</td> <td>A reference to the checklist or publication that was followed to record the name in dwc:scientificName.</td> </tr> <tr> <td>samplingEffort</td> <td>The amount of effort, expressed in minutes or hours, to obtain and record the occurrences.</td> </tr> <tr> <td>occurrenceStatus</td> <td>A statement about the presence or absence of a taxon during the time of an event.</td> </tr> <tr> <td>disposition</td> <td>The current state of a specimen with respect to a collection.</td> </tr> <tr> <td>language</td> <td>The language of the record using ISO 639-1 codes, e.g. en</td> </tr> </tbody> </table>
LocalDEM+ of the hinterland of Ravenna
<p><strong>Digital elevation model at 10 m resolution free of modern interferences of the hinterland of Ravenna, Italy.</strong></p> <p>This dataset includes:</p> <ul> <li><em>Ravenna_hinterland_LocalDEM+.tif =</em> digital elevation model at 10 m resolution free of modern interferences of the hinterland of Ravenna</li> <li><em>Ravenna_hinterland_LocalDEM+.gpkg =</em> vector point file containing the elevation data used to model the LocalDEM+ of the hinterland of Ravenna</li> </ul> <p>The LocalDEM+ for the hinterland of Ravenna was created by interpolating ground control points (GCP) manually recorded by the Emilia-Romagna region [1]. In December 2018, the downloaded dataset still included modern artefacts among the almost 400.000 points, such as those over artificial fluvial banks, streets, railroads, and the A14 highway. Therefore, all elevation points not classified as “isolato al suolo” (= recorded on the bare soil) were removed to filter out these modern disturbances. Further manual cleaning was carried out to remove additional points along artificial infrastructures such as fluvial banks and the highway.</p> <p>The original LocalDEM was created interpolating the resulting 160.481 points using the Inverse Distance Weighting method [2] creating a DTM at 10 m resolution devoid of modern interferences (Version <strong>v1</strong>).</p> <p>The same 160.481 points dataset has also been interpolated via co-kriging [3] using FABDEM V1-2 [4] as the second correlated variable. Tiles “N044E011” and “N044E012” [5] were merged together before interpolation and 250.000 locations were randomly sampled within the merged grid extension using the “Random points” tool. Elevation for all 250.000 locations was extracted using the "Sample raster values” tool.</p> <p>The improved LocalDEM+ shares the same spatial dimension and pixel resolution of 10 m, being as well devoid of modern interferences (Version <strong>v2</strong>).</p> <p> </p> <p>-------------------------------------------------------------------------------</p> <p>[1] Data was retrieved from https://geoportale.regione.emilia-romagna.it/download/download-data on 21st December 2018 (last accessed on 10 February 2021).</p> <p>[2] Mitas, L., & Mitasova, H. (2005). Spatial Interpolation. In P. Longley, M. F. Goodchild, D. J. Maguire, & D. W. Rhind (Eds.), Geographical Information Systems: Principles, Techniques, Management and Applications (Second Edition., pp. 481–832). Wiley.</p> <p>[3] Gooverts, P. (1998). “Ordinary cokriging revisited”, <em>Mathematical Geology</em> 30, pp. 21–42, https://doi.org/10.1023/A:1021757104135</p> <p>[4] FABDEM (Forest And Buildings removed Copernicus DEM) is a global elevation map that removes building and tree height biases from the Copernicus GLO 30 Digital Elevation Model (DEM). The data is available at 1 arc second grid spacing (approximately 30m at the equator) for the globe. FABDEM is introduced in Hawker <em>et al</em>. (2022), “A 30 m global map of elevation with forests and buildings removed”, <em>Environmental Research Letters</em> 17(2), https://dx.doi.org/10.1088/1748-9326/ac4d4f</p> <p>[5] Data was retrieved from <a href="https://data.bris.ac.uk/data/dataset/s5hqmjcdj8yo2ibzi9b4ew3sn">https://data.bris.ac.uk/data/dataset/s5hqmjcdj8yo2ibzi9b4ew3sn</a> on 12th January 2024 (last accessed on 12th January 2024).</p>
German Index of Socioeconomic Deprivation (GISD)
<p>Der German Index of Socioeconomic Deprivation (GISD) ist ein am Robert Koch-Institut entwickelter Index zur Erfassung regionaler sozioökonomischer Benachteiligung. Er wird verwendet, um regionale sozioökonomische Ungleichheiten in der Gesundheit sichtbar zu machen und Ansatzpunkte zur Erklärung regionaler Unterschiede in der Gesundheit aufzeigen zu können. Mit dem GISD wird es möglich, sozioökonomische Unterschiede in den Gesundheitschancen, Krankheits- und Sterberisiken in Deutschland auch dann zu untersuchen, wenn die betreffenden Gesundheitsdaten auf individueller Ebene keine Information zum sozioökonomischen Status enthalten. Für die Generierung des GISD werden Information der Bildungs-, Beschäftigungs- und Einkommenssituation in Kreisen und Gemeinden aus der Datenbank INKAR verwendet. Er wird auf der Ebene der Gemeinden generiert und wird für die Raumbezüge Gemeinden, Gemeindeverbände, Stadt- und Landkreise, Raumordnungsregionen, NUTS-2 und Postleitzahlbereiche bevölkerungsgewichtet aggregiert bereitgestellt. Die Gewichtung der Indikatoren wird über Hauptkomponentenanalysen innerhalb der Teildimensionen vorgenommen. Die aktuell verfügbaren Daten beziehen sich auf den Gebietsstand 31.12.2021 und enthalten Werte von 1998 bis 2021.</p>
Dataset of "Activity-stability relationship in magnetron co-sputtered bimetallic catalysts for proton exchange membrane fuel cells"
<p>In the present study, magnetron sputtered PtxM100-x (M = Co, Cu, Y; x = 25, 50, 75 and 100) bimetallic alloys were investigated as PEMFC cathodes. Accurate composition control enabled a systematic study of the correlation between alloy composition, activity, and stability. The catalysts underwent thorough characterization, employing a diverse portfolio of characterization techniques such as scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy and cyclic voltammetry. The activity of all investigated alloys was tested directly in a fuel cell device, while stability was assessed through potentiodynamic cycling in a half-cell. <br>The activity-stability index, considering experimental results for both activity and stability, was calculated and compared for all investigated catalysts. All alloys exhibited a volcano-type trend in activity-stability index as a function of the concentration of alloying element with peaks observed at Pt50Co50, Pt50Cu50 and Pt75Y25 for respective alloys, surpassing that of monometallic platinum. Overall, Pt50Co50 emerged as a catalyst with the highest activity-stability ratio.</p>
Digitised, searchable Holle List in Stokhof (1980)
<p>This repository contains the digitised Holle List in Stokhof (<a href="https://core.ac.uk/reader/159464813">1980</a>). Details and the interactive web version of the list can be accessed via <a title="Digitised, searchable Holle List" href="https://engganolang.github.io/digitised-holle-list/" target="_blank" rel="noopener">https://engganolang.github.io/digitised-holle-list/</a> (Rajeg 2023).</p> <p>The work in this repository is part of the <a href="https://gtr.ukri.org/projects?ref=AH%2FW007290%2F1">AHRC-funded research</a> on <a title="Lexical resources for Enggano" href="https://portal.sds.ox.ac.uk/Lexical_resources_for_Enggano" target="_blank" rel="noopener"><em>Lexical resources for Enggano, a threatened language of Indonesia</em></a> (central webpage of the Enggano project: <a title="Enggano research" href="https://enggano.ling-phil.ox.ac.uk/" target="_blank" rel="noopener">https://enggano.ling-phil.ox.ac.uk/</a>)</p> <h2>Updates in version 1.4.1</h2> <ul> <li>Adding the Transcription table (Stokhof 1980: 75-77, §6.2) on the interactive webpage version (see <a title="Transcription Symbols" href="https://engganolang.github.io/digitised-holle-list/#:~:text=Transcription%20Symbols" target="_blank" rel="noopener">Table 4 "Transcription Symbols"</a>)</li> <li>Adding an update on the potential for the list to be included in the <em>Concepticon</em> (cf. the note <a href="https://github.com/concepticon/concepticon-data/issues/1324">here</a>)</li> <li>Adding reference to <em>EnoLEX</em>, a diachronic lexical database for the Enggano language (cf. <a href="https://doi.org/10.25446/oxford.28282169">here</a>)</li> </ul> <h2>References</h2> <p>Forkel, Robert, Johann-Mattis List, Simon J. Greenhill, Christoph Rzymski, Sebastian Bank, Michael Cysouw, Harald Hammarström, Martin Haspelmath, Gereon A. Kaiping & Russell D. Gray. 2018. Cross-Linguistic Data Formats, advancing data sharing and re-use in comparative linguistics. Scientific Data. Nature Publishing Group 5(1). 180205. <a href="https://doi.org/10.1038/sdata.2018.205" target="_blank" rel="noopener">https://doi.org/10.1038/sdata.2018.205</a>.</p> <p>Krauße, Daniel; Rajeg, Gede Primahadi Wijaya; Pramartha, Cokorda Rai Adi; Zobel, Erik; Nothofer, Bernd; Hemmings, Charlotte; et al. (2024). EnoLEX: A diachronic lexical database for the Enggano language. University of Oxford. Online database. <a title="EnoLEX metadata record" href="https://doi.org/10.25446/oxford.28282169" target="_blank" rel="noopener">https://doi.org/10.25446/oxford.28282169</a>.</p> <p>Rajeg, Gede Primahadi Wijaya. 2023. Digitised, searchable Holle List in Stokhof (1980). Dataset. University of Oxford. <a href="https://doi.org/10.25446/oxford.23205173" target="_blank" rel="noopener">https://doi.org/10.25446/oxford.23205173</a></p> <p>Stokhof, W. A. L. (ed.). 1980. Holle lists, vocabularies in languages of Indonesia, vol. 1: Introductory volume. Vol. Materials in Languages of Indonesia. Canberra, A.C.T., Australia: Dept. of Linguistics, Research School of Pacific Studies, The Australian National University. <a title="Source Holle List in PDF" href="https://core.ac.uk/reader/159464813" target="_blank" rel="noopener">https://core.ac.uk/reader/159464813</a>.</p>
Geoarchaeological dataset for the hinterland of Ravenna
<p><strong>Geoarchaeological dataset with 8798 archaeological sites and geological layers and known extent of 591 archaeological sites collected for the hinterland of Ravenna, Italy.</strong></p> <p>The dataset includes:</p> <ul> <li><em>Ravenna_hinterland_geoarchaeological_data.csv</em> = text file with the full geoarchaeological dataset</li> <li><em>Ravenna_hinterland_geoarchaeological_data.gpkg</em> = vector file with the full geoarchaeological dataset</li> <li><em>Ravenna_hinterland_geoarchaeological_site.gpkg</em> = vector file with archaeological sites extent</li> <li><em>Ravenna_hinterland_geoarchaeological_data_metadata.txt = </em>text file with metadata of geoarchaeological dataset</li> <li><em>Ravenna_hinterland_geoarchaeological_dataset_bibliography.odt </em>= text file with bibliographical references of geoarchaeological dataset</li> <li><em>Ravenna_hinterland_geoarchaeological_dataset_archaeological_reports.odt</em> = reference file with the list of unpublished archaeological reports collected from SABAP-RA and SABAP-BO</li> <li><em>Ravenna_hinterland_geoarchaeological_dataset_archaeological_reports.bib</em> = reference file with the list of unpublished archaeological reports collected from SABAP-RA and SABAP-BO</li> <li><em>Ravenna_hinterland_geoarchaeological_dataset_geological_reports.bib</em> = reference file with the list of raw geological reports from SGSS available online</li> </ul> <p> </p> <p>-------------------------------------------------------------------------------</p> <p>SABAP-RA = Soprintendenza Archeologia, Belle Arti e Paesaggio per le province di Ravenna, Forlì-Cesena e Rimini</p> <p>SABAP-BO = Soprintendenza Archeologia belle arti e paesaggio per la città metropolitana di Bologna e le province di Modena, Reggio Emilia e Ferrara</p> <p>SGSS = Servizio Geologico Sismico e dei Suoli, Regione Emilia-Romagna</p>
FULFILL dataset - diet policy acceptability - efficacy and acceptability framing Denmark
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Denmark in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from two countries: Denmark and Germany, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from Denmark and Germany, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes a framing experiment including three groups with participants being randomly assigned to. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if an information on either the efficacy of the measures or a combination of information with acceptance information or none of these information could influence people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset round 2 Delhi and Mumbai (India)
<p>This dataset and codebook correspond to the second round of survey data gathered in Delhi and Mumbai (India) in 2024, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. </p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 1000 respondents from the initial survey round, ensuring representativity is maintained.</p> <p>In order to consider sufficiency-oriented lifestyles not only in Europe but also in the Global South, we conducted a similar survey in India. More specifically, we adjusted the survey to fit the context (e.g., including cooling) and, due to the large size and diversity within India, we focused data collection on two Mega Cities (>10Mio inhabitants), namely Mumbai and Delhi. Due to the different cultural context and in exchange with Indian researchers and the supporting market research institute, we decided to change the methodology for data collection from an online survey to face-to-face interviews. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</p>
Dataset of "Molecular dynamics of evaporative cooling of water clusters"
<p>The cooling of water clusters through evaporation into a vacuum is studied using classical molecular dynamics with the SPC water model, and the results are compared with semimacroscopic theory. A model based on the Hertz–Knudsen equation underestimates the cooling rates. A modified approach, which accounts for the Kelvin equation, provides better results. While the rotational temperature of the clusters is in equilibrium with their internal temperature, the translational temperature of the clusters “as individual particles” remains unchanged.</p>
Ergebnisdatensatz BURDEN 2020 – Krankheitslast in Deutschland und seinen Regionen
<p>Kennzahlen der Krankheitslast (engl. Burden of Disease) rücken für eine Vielzahl von Anwendungsmöglichkeiten, unter anderem im Öffentlichen Gesundheitsdienst des Bundes und der Länder, in Forschung, Entwicklung und anderen Bereichen des Gesundheitswesens, zunehmend in den Vordergrund. Sie erfassen den Gesundheitszustand einer Bevölkerung mit Hilfe einer konsistenten Metrik. Auch als Summenmaße der Bevölkerungsgesundheit bezeichnet, integrieren sie verschiedene Erkrankungen und Verletzungen als Ursachen für gesundheitliche Beeinträchtigungen (Morbidität) und Versterben (Mortalität). Standardisierte Berechnungsverfahren ermöglichen es, die Bedeutung einzelner Erkrankungen zu quantifizieren und zueinander ins Verhältnis zu setzen. Die Möglichkeit zur Stratifizierung nach Region, Alter oder Geschlecht lässt darüber hinaus eine differenzierte Betrachtung zu. Zu den Kennzahlen zählen der Indikator für die Krankheitslast insgesamt (DALY), welcher sich aus der Summe der beiden Einzelkomponenten zur Mortalität (YLL) und Morbidität (YLD) ergibt. Diese Indikatoren wurden im Rahmen des Projektes "BURDEN 2020 – Die Krankheitslast in Deutschland und seinen Regionen" auf nationaler sowie regionaler Ebene bestimmt. Mit der vorliegenden Datenveröffentlichung wird der Ergebnisdatensatz des Projekts zur freien Verfügung gestellt (Open Data). Sie enthält den Aufbau und Inhalt des Ergebnisdatensatzes, einen kurzen Überblick zum Projekt, den verwendeten Datenquellen und eingesetzten Verfahren zur Bestimmung der Einzelindikatoren. Die Ergebnisdaten wurden zur Veranschaulichung des Konzepts auf der Webseite <a href="http://www.daly.rki.de">www.daly.rki.de</a> visualisiert.</p>
Dominant contribution of Asgard archaea to eukaryogenesis (2024) Tobiasson, V., Koonin, E. PROCESSED DATA AND METADATA
<h1>Main data deposit for "Dominant contribution of Asgard archaea to eukaryogenesis". </h1> <p>Victor Tobiasson, Jacob Luo, Yuri I Wolf, Eugene V Koonin</p> <p>Computational Biology Branch, Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA</p> <p><strong>The Origin of eukaryotes is one of the key problems in evolutionary biology. The demonstration that the Last Eukaryotic Common Ancestor (LECA) already contained the mitochondrion, an endosymbiotic organelle derived from an alphaproteobacterium, and the discovery of Asgard archaea, the closest archaeal relatives of eukaryotes inform and constrain evolutionary scenarios of eukaryogenesis. We undertook a comprehensive analysis of the origins of the core eukaryotic genes tracing to the LECA within a rigorous statistical framework centered around evolutionary hypotheses testing using constrained phylogenetic trees. The results reveal dominant contributions of Asgard archaea to the origin of most of the conserved eukaryotic functional systems and pathways. A limited contribution from Alphaproteobacteria was identified, primarily relating to the energy transformation systems and Fe-S cluster biogenesis, whereas ancestry from other bacterial phyla was scattered across the eukaryotic functional landscape, without consistent trends. These findings suggest a model of eukaryogenesis in which key features of eukaryotic cell organization evolved in the Asgard ancestor, followed by the capture of the Alphaproteobacterial endosymbiont, and augmented by numerous but sporadic horizontal acquisition of genes from other bacteria both before and after endosymbiosis. </strong></p> <div> <div> <div>Version 0.3, updated 180325</div> <div> </div> <div> </div> <div>Main data repository for:</div> <div>Dominant contribution of Asgard archaea to eukaryogenesis (2024) </div> <div>Tobiasson, V., Koonin, E.</div> <div> </div> <div>Contains all final parsed data from the main Eukaryogenesis project </div> <div>investigating the evolutionary ancetries of eukaryotic protein families. </div> <div> </div> <div>Currently (non-static) available at: </div> <div>https://www.biorxiv.org/content/10.1101/2024.10.14.618318v2</div> <div>https://assets-eu.researchsquare.com/files/rs-5352492/v1/2f9c68ae-cf3e-420a-8d29-867b6fb1a878.pdf</div> <div> </div> <div>All code used to generate the data present within this repository available at: </div> <div>https://github.com/VictorTobiasson/eukgen </div> <div> </div> <div> </div> <div>### General information</div> <div> </div> <div>To identify associations between prokaryotic and eukaryotic protein families, separate</div> <div>hidden Markov model (HMM) databases for prokaryotes and eukaryotes were constructed </div> <div>using a custom, cascaded, sequence-to-profile clustering pipeline, implemented using </div> <div>mmseqs2, followed by a multistep data-reduction and multiple sequence alignment (MSA) </div> <div>procedure to generate HMM profiles using hhsuite. </div> <div> </div> <div>A prokaryotic database of 37 million protein sequences was curated from prokaryotic </div> <div>genomes obtained from the NCBI GenBank in November 2023 and supplemented with proteins </div> <div>extracted from 146 Asgard genome assemblies. To avoid inclusion of genes present only </div> <div>within a narrow subset of species, possibly resulting from horizontal transfer from </div> <div>eukaryotes post LECA, we reconstructed the “soft-core” pangenome for each of the 26 </div> <div>curated prokaryotic taxonomic classes. These pangenomes include only those genes that </div> <div>are present in at least 67% of the families within each class of Bacteria and Archaea. </div> <div>The initial eukaryotic database consisted of 30 million protein sequences from 993 </div> <div>species taken from EukprotV3 and cleaned using mmseqs2 to remove likely prokaryotic </div> <div>contaminants. </div> <div> </div> <div>Both databases were clustered and MSAs constructed for all non, singleton clusters </div> <div>and HMM profiles created. The resulting eukaryotic HMM dataset was queried against </div> <div>the prokaryotic dataset using hhblits to identify sets of homologous protein sequences. </div> <div>Each eukaryotic cluster and all its significant prokaryotic hits constituted an individual</div> <div> sequence set, hereinafter referred to as an Eukaryotic/Prokaryotic Orthologous Cluster </div> <div>(EPOC). The EPOCs constitute groups of homologous proteins from eukaryotes and prokaryotes </div> <div>(each EPOC contains a unique set of eukaryotic proteins, but some clusters of prokaryotic </div> <div>proteins can be present in multiple EPOCs) that were used for phylogenetic tree </div> <div>construction, annotation, and evolutionary hypothesis testing. </div> <div> </div> <div>To infer the most likely prokaryotic ancestry of the eukaryotic proteins in each EPOC, </div> <div>rather than relying on the tree topology directly, we employed a probabilistic approach </div> <div>for evolutionary hypothesis testing using constraint trees. We exhaustively sampled all </div> <div>arrangements of likely sister clades and obtained Expected Likelihood Weights (ELW) for </div> <div>the set of possible sister clade models. As the ELW metric is analogous to model selection </div> <div>confidence, here we take it to be proportional to the probability of a sampled prokaryotic </div> <div>clade to be the true sister group of the given eukaryotic clade among a set of competing </div> <div>sister clades. For each EPOC, our analysis dynamically accounts for long branch outliers </div> <div>and is robust to phylogenetically non-homogenous clades. This analysis is further capable </div> <div>of resolving eukaryotic paraphyly, treating each eukaryotic clade within a EPOC as a </div> <div>single datapoint for downstream analysis. Our resulting data contains EPOCs annotated </div> <div>using profiles generated from KEGG Orthology Groups (KOGs), each with an MSA generated </div> <div>using muscle5, a maximum likelihood tree inferred using IQtree2 and associated ELW values </div> <div>for all candidate prokaryotic sister phyla. The analysis of prokaryotic ancestry was </div> <div>performed only for those eukaryotic clades that included more than 5 distinct taxonomic </div> <div>labels, with at least one coming from Amorphea and one from Diaphoretickes, the two </div> <div>expansive eukaryotic clades considered to represent either the first or the second </div> <div>bifurcation in the evolution of eukaryotes. Thus, these clades likely represent genes </div> <div>mapping back to the LECA.</div> <div> </div> <div>For further details please see main publication or contact</div> <div>victor.tobiasson@nih.gov</div> <div>eugene.koonin@nih.gov</div> <div> </div> <div> </div> <div>### Included files</div> <div> </div> <div>Unless otherwise stated all files contained are tab separated and utf-8 encoded </div> <div>with the first row containing header information. </div> <div>All data entries encoding lists are “|” (pipe) separated. </div> <div>Fields without data values are filled with string entries of “none”.</div> <div> </div> <div>--- Databases ---</div> <div>euk72_ep.tar.gz</div> <div>prok2311_as.tar.gz</div> <div>Prok2311As_final_clusters.tsv</div> <div>Euk72Ep_final_clusters.tsv</div> <div>prok2311_as.hmmDB.tar.gz</div> <div>euk72_ep.hmmDB.tar.gz</div> <div> </div> <div>--- Annotation and Curation ---</div> <div>NCBI_taxonomy_species_addendum.tsv</div> <div>NCBI_taxonomy_class_addendum.tsv</div> <div>Euk72Ep_Prok2311As_final_classes.tsv</div> <div>Euk72Ep_Prok2311As_final_classes.GTDB.tsv</div> <div>KEGG_category_mapping.tsv</div> <div>KEGG_metadata.tsv</div> <div> </div> <div>--- EPOC data ---</div> <div>EPOC_data.tar.gz</div> <div>EPOC_annotation_KEGG.tsv</div> <div>EPOC_data.tsv</div> <div>EPOC_data.pangenomes_s10.tsv</div> <div>EPOC_data.pangenomes_s25.tsv</div> <div>EPOC_data.pangenomes_s67.tsv</div> <div>EPOC_data.GTDB.tsv</div> <div> </div> <div># euk72_ep.tar.gz</div> <div>Gunzip-ed .tar archive containing a single directory with 10 files </div> <div>constituting the initial eukaryotic mmseqs2 database with taxonomy annotation. </div> <div>Constructed from a pre-selected list of 72 eukaryotic proteomes downloaded from </div> <div>NCBI as well as a “clean” version of Eukprot, lacking highly prokaryotic-like </div> <div>contaminant sequences. </div> <div> </div> <div># prok2311_as.tar.gz</div> <div>Gunzip-ed .tar archive containing a single directory with 10 files constituting the </div> <div>initial prokaryotic mmseqs2 database with taxonomy annotation. Constructed from </div> <div>47545 complete genomes retrieved from NCBI in November 2023. </div> <div> </div> <div># prok2311_as.hmmDB.tar.gz</div> <div>Gunzip-ed .tar archive containing 6 files. Comprises an HHSuite Databse formatted </div> <div>from prok2311_as non--singleton clusters, contains 26286 profiles.</div> <div> </div> <div># euk72_ep.hmmDB.tar.gz</div> <div>Gunzip-ed .tar archive containing 6 files. Comprises an HHSuite Databse formatted </div> <div>from euk72_ep non-singleton clusters, contains 1631704 profiles.</div> <div> </div> <div># NCBI_taxonomy_species_addendum.tsv</div> <div>Taxonomy mapping file with manually curated ‘class’ level annotation for poorly </div> <div>annotated species. </div> <div> </div> <div>taxid: NCBI taxid</div> <div>proposed_class_id: Manually assigned NCBI taxid</div> <div>proposed_class_label: NCBI class name</div> <div>org_name: NCBI organism name</div> <div> </div> <div># NCBI_taxonomy_class_addendum.tsv</div> <div>Class revision file mapping poorly populated class level entries to higher order </div> <div>manually curated labels. Also includes information for small classes with shallow </div> <div>taxonomy which are deleted from the EPOC analysis at the level of tree construction.</div> <div> </div> <div>taxid: NCBI taxid</div> <div>ncbi_class: NCBI taxid of rank corresponding to ‘class’ following manual </div> <div>amendment as per NCBI_taxonomy_species_addendum.tsv</div> <div>revised_class_id: Manually assigned NCBI taxid of rank corresponding to ‘class’</div> <div>revised_class_label: Proposed cleartext name of manually revised revised_class_id </div> <div> </div> <div># Euk72Ep_Prok2311As_final_classes.tsv</div> <div>Final taxonomy at NCBI rank ‘class’ following revisions for all sequences in Euk72Ep or </div> <div>Prok2311As. These taxonomic labels are used for EPOC tree annotation. </div> <div> </div> <div>acc: mmseqs database header in either prok2311_as or euk72_ep databases</div> <div>taxid: NCBI taxid for organism</div> <div>superkingdom: Top level NCBI taxonomy classification Bacteria, Archaea or Eukarya, </div> <div>used to define Eukaryotic outgroups in EPOC analysis</div> <div>class: Cleartext name of manually revised NCBI rank ‘class’ identifier for annotation</div> <div> </div> <div># Euk72Ep_Prok2311As_final_classes.GTDB.tsv</div> <div>Final taxonomy at GTDB rank ‘phylum’ transferred using marker genes from GTDB release 220</div> <div> </div> <div>acc: mmseqs database header in either prok2311_as or euk72_ep databases</div> <div>taxid: NCBI taxid for organism</div> <div>superkingdom: Top level NCBI taxonomy classification Bacteria, Archaea or Eukarya, </div> <div>used to define Eukaryotic outgroups in EPOC analysis</div> <div>class: Cleartext name of assigne GTDB phylum</div> <div> </div> <div># Prok2311As_final_clusters.tsv</div> <div>Cluster mapping file for accessions within the initial Prok2311A database to the </div> <div>final clusters used for HMM creation </div> <div> </div> <div>cluster_acc: cluster representative</div> <div>acc: cluster member</div> <div> </div> <div># Euk72Ep_final_clusters.tsv</div> <div>Cluster mapping file for accessions within the initial Prok2311A database to the </div> <div>final clusters used for HMM creation</div> <div> </div> <div>cluster_acc: cluster representative</div> <div>acc: cluster member</div> <div> </div> <div># EPOC_data.tar.gz</div> <div>Gunzip-ed directory containing 16035 EPOC folders. Each folder is named corresponding </div> <div>to the eukaryotic cluster representative which generated its profile as an ID </div> <div>Matches the tree_name field in EPOC_data_prok2311As.tsv</div> <div>contains the following files:</div> <div> </div> <div><EPOC_ID>.merged.fasta: sequences for all members of the EPOC</div> <div><EPOC_ID>.merged.fasta.leaf_mapping: tsv separated file containing taxonomy and tree reduction data</div> <div><EPOC_ID>.merged.fasta.muscle: main cropped MSA for tree generation </div> <div><EPOC_ID>.merged.fasta.muscle.iqtree: IQtree2 output from tree generation</div> <div><EPOC_ID>.merged.fasta.muscle.treefile.annot: annotated newick tree file with final tree</div> <div><EPOC_ID>.merged.tree_data.tsv: final parsed tree data with columns matching EPOC_data_prok2311As.tsv</div> <div> </div> <div>EPOCs with more than one possible eukaryotic sister phyla also contains </div> <div>a folder "constraint_analysis" with constraint tree information used for </div> <div>ELW value calculation. </div> <div> </div> <div># EPOC_data.tsv</div> <div>Main resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) </div> <div>based on pangenomes defined as including 10% of species per class. This is the main</div> <div>data to be used for genereting the core dataset and for data visualistation</div> <div>Contains information regarding tree breakdown, LCA membership and phylogenetic </div> <div>distances between all detected LCAs. Equivalent to the stacked dataframes from all </div> <div>EPOC directories in EPOC_data </div> <div> </div> <div>tree_name: unique index for each EPOC </div> <div>euk_clade_rep: unique index for each annotated eukaryotic clade within each tree_name</div> <div>euk_clade_size: number of original sequences represented by euk_clade_rep</div> <div>euk_clade_weight: metric for taxonomic purity for each euk_clade_rep</div> <div>euk_leaf_clade: boolean indicating whether euk_clade_rep contains a single leaf</div> <div>euk_LCA: lowest taxa spanning all members in euk_clade_rep</div> <div>euk_scope: list of all taxonomic classes in euk_clade_rep</div> <div>euk_scope_len: length of euk_scope list</div> <div>prok_clade_rep: unique index for each annotated prokaryotic clade for each euk_clade_rep</div> <div>prok_clade_size: number of original sequences represented by prok_clade_rep</div> <div>prok_clade_weight: metric for taxonomic purity for each prok_clade_rep</div> <div>prok_leaf_clade: boolean indicating whether prok_clade_rep contains a single leaf</div> <div>prok_taxa: lowest taxa spanning all members in prok_clade_rep</div> <div>dist: tree-distance from lowest tree node containing all members of prok_clade_rep to lowest tree node containing all members of euk_clade_rep</div> <div>top_dist: graph-distance (node-distance) from lowest tree node containing all members of prok_clade_rep to lowest tree node containing all members of euk_clade_rep</div> <div>raw_stem_length: tree-distance from lowest tree node containing the union of all members of prok_clade_rep and euk_clade_rep to the tree node containing all members of euk_clade_rep</div> <div>median_euk_leaf_dist: median value for all tree distances from the tree node containing all members of euk_clade_rep to the individual leaves</div> <div>stem_length: raw_stem_length/median_euk_leaf_dist</div> <div>logL: log likelihood of best constraint tree constructed</div> <div>deltaL: log likelihood difference between constraint tree for prok_clade_rep and best constraint tree constructed</div> <div>bp-RELL: validation metric from IQtree -trees, see iqtree.org</div> <div>bp-RELL_accept: as above</div> <div>p-KH: as above</div> <div>p-KH_accept: as above</div> <div>p-SH: as above</div> <div>p-SH_accept: as above</div> <div>c-ELW: as above</div> <div>c-ELW_accept: as above</div> <div>p-AU: as above</div> <div>p-AU_accept: as above</div> <div> </div> <div># EPOC_data.pangenomes_s10.tsv</div> <div>Resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>based on pangenomes defined as including 10% of species per class.</div> <div>Identical file structure to EPOC_data.tsv</div> <div> </div> <div># EPOC_data.pangenomes_s25.tsv</div> <div>Resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>based on pangenomes defined as including 25% of species per class.</div> <div>Identical file structure to EPOC_data.tsv</div> <div> </div> <div># EPOC_data.pangenomes_s67.tsv</div> <div>Resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>based on pangenomes defined as including 67% of species per class.</div> <div>Identical file structure to EPOC_data.tsv</div> <div> </div> <div># EPOC_data.GTDB.tsv</div> <div>Resulting data from all Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>under revised taxonomy from GTDB based on data from Euk72Ep_Prok2311As_final_classes.GTDB.tsv</div> <div>Identical file structure to EPOC_data.tsv</div> <div> </div> <div># EPOC_data.alpha_replicates.tsv</div> <div>Resulting data from 20 repetitions of Eukaryotic/Prokaryotic Orthologous Clusters (EPOCs) calculated </div> <div>from a subset of Alphaproteobacterial-derived EPOCs. </div> <div>Identical file structure to EPOC_data.tsv with the addition of:</div> <div> </div> <div>rep: indicating technical replicate number, 0-19</div> <div> </div> <div># EPOC_annotation_KEGG.tsv</div> <div>Parsed HHblits output of HMM profiles generated from KEGG KOGs (KEGG Orthologous Groups) </div> <div>against eukaryotic profiles constituting each EPOC</div> <div> </div> <div>Query: query name equal to tree_name from EPOC_data</div> <div>Target: target name equal to kogid in KEGG_category_mapping and KEGG_metadata</div> <div>Prob: data from HHblits, see https://github.com/soedinglab/hh-suite/wiki</div> <div>E-value : as above</div> <div>P-value : as above</div> <div>Score: as above</div> <div>SS: as above</div> <div>Cols: as above</div> <div>Identities: as above</div> <div>Similarity: as above</div> <div>Sum_probs: as above</div> <div>Query-HMM-start: as above</div> <div>Query-HMM-end: as above</div> <div>Template-HMM-start: as above</div> <div>Template-HMM-end: as above</div> <div>Template_columns: as above</div> <div>Template_Neff : as above</div> <div>Pairwise_cov: calculated pairwise coverage from Query and Target start and end</div> <div>Description: category_name from KEGG_category_mapping</div> <div> </div> <div># KEGG_category_mapping.tsv</div> <div>Mapping of relevant KOG identifiers to their higher order categories as </div> <div>"Maps" "Modules" or "Reactions" as per KEGG see https://www.kegg.jp/kegg/pathway.html</div> <div> </div> <div>kogid: unique KOG identifier</div> <div>category_id: KEGG map, module, or reaction number</div> <div>category_name: cleartext name for KOG identifier</div> <div> </div> <div># KEGG_metadata.tsv</div> <div>File mapping KOGs to BRITE classification and to additional databases of chemical properties.</div> <div> </div> <div>kogid: unique KOG identifier</div> <div>name: cleartext name for KOG identifier</div> <div>brite_A: list of BRITE-A sets including KOG</div> <div>brite_B: list of BRITE-A sets including KOG</div> <div>brite_C: list of BRITE-A sets including KOG</div> <div>EC: list of Enzyme commission numbers associated with KOG, see https://enzyme.expasy.org/</div> <div>TC: list of transporter classification numbers associated with KOG, see https://www.tcdb.org/</div> <div>RN: list of KEGG reaction numbers associated with KOG</div> <div>CA: list of CAZY numbers associated with KOG, see http://www.cazy.org/</div> <div>GO: list of GO terms associated with KOG, see https://geneontology.org/</div> </div> <div> </div> </div>
Cirrus formation regimes - Data driven identification and quantification of mineral dust effect
<p>This repository contains the data for the paper: </p> <p>Authors: Kai Jeggle , David Neubauer , Hanin Binder and Ulrike Lohmann<br>Titel: Cirrus formation regimes - Data driven identification and quantification of mineral dust effect<br>Date: 2024</p> <p>Note that the scripts can be found in the accompanying code repository (https://github.com/tabularaza27/cloud_clustering)<br><br>Contents:<br><br>├── cirrus_cloud_trajectories.ftr<br>├── cluster_input_data.ftr<br>├── cluster_models<br>│ └── temperature_clustering_k4_12<br>│ ├── cloud_ids.npy<br>│ ├── model_params.json<br>│ └── trained_model.hdf5</p> <p>│ └── temperature_clustering_k4_24<br>│ ├── cloud_ids.npy<br>│ ├── model_params.json<br>│ └── trained_model.hdf5</p> <p>├── cluster_predictions.ftr<br>└── readme.txt<br><br>For more info, please have a look at the <em>readme.txt</em><br><br>This is an updated version of the data, containing updated models and predictions based on the Journal revisions</p>
Full-length and split homologs of human proteins in the gut microbiome
<p>These files were generated as part of the manuscript "Human xenobiotic metabolism proteins have full-length and split homologs in the gut microbiome" (submitted).</p> <p>The .tar file contains .ipc files that are tables of full-length (full_humcover3.ipc) and split homologs (part_humcover3.ipc) of human proteins in the gut microbiome, organized by alignment coverage threshold. For example, the directory `HumanUPR_0.67_src_20000_70` contains results obtained at a 67% alignment coverage threshold for the bacterial protein, and 70% for the human protein. Note that our pipeline collapses full-length alignments to the same UHGP-90 protein family into a single entry per species, with the number of genomes reported in the column nGenomes. Split homologs are not collapsed because genomic context is used to define them, and this context may differ across individual genomes.</p> <p>These files are in Arrow <a href="https://arrow.apache.org/docs/python/ipc.html#ipc">IPC</a> format, which provides compression and fast I/O for large tables. We recommend reading them using <a href="https://pola.rs/">pola.rs</a> or the <a href="https://arrow.apache.org/docs/r/">R Arrow</a> package. In particular, because the full-length homolog table is large, you may wish to work with it without loading it into memory, which can be accomplished using <a href="https://docs.pola.rs/api/python/dev/reference/api/polars.scan_ipc.html">scan_ipc</a> in pola.rs or <a href="https://arrow.apache.org/docs/r/reference/open_dataset.html">open_dataset</a> in R Arrow.</p> <p>We also provide gzipped .csv format datasets of full-length (pgkb_FH_drugs.csv.gz) and split (pgkb_SH_drugs.csv.gz) homologs, at the default 67% alignment coverage threshold for bacterial and 70% for human proteins, organized by their <a href="https://www.pharmgkb.org/">PharmGKB</a> annotations. For each drug annotated in PharmGKB as being metabolized by a human protein with full-length or split homologs, we provide the human protein(s) responsible, its xenobiotic enzyme class, the bacterial protein homolog(s), length and percent identity of the alignment, and either the specific genome (g, split homologs only) or the number of genomes (nGenomes, full homologs only). Xenobiotic enzyme classes are defined as in Figure 4 of the manuscript, with the additional classes "nucl" (nucleobase-containing metabolic proteins not annotated to any other class), "redox" (oxidoreductases not annotated to any other class), and "other" (all remaining proteins).</p>
CLDF dataset of the Enggano word list from 1895 in Stokhof and Almanar's (1987) Holle List
<p>The repository for the digitised Enggano word list from 1895 (see Stokhof and Almanar 1987 for the original source) that has been matched with the <a href="https://engganolang.github.io/digitised-holle-list/">digitised Holle List</a> (Rajeg 2023a; cf. Stokhof 1980), providing the English and Indonesian glosses for the Enggano forms. The data set <a href="https://github.com/engganolang/holle-list-enggano-1895/actions/workflows/cldf-validation.yml">conforms</a> to the Wordlist module of the Cross-Linguistic Data Format (<a href="https://cldf.clld.org/">CLDF</a>) (Forkel et al. 2018).</p> <p><em>The work in this repository is part of the <a href="https://gtr.ukri.org/project/8AB0C3DC-F1C9-4CFA-BB4D-5BE748213372">AHRC-funded research</a> on <strong>Lexical resources for Enggano, a threatened language of Indonesia</strong> (visit the <a href="https://enggano.ling-phil.ox.ac.uk/">central webpage of the Enggano research</a> and the specific repository of the <a href="https://portal.sds.ox.ac.uk/Lexical_resources_for_Enggano">Lexical Resources for Enggano</a> project as well the <a href="https://portal.sds.ox.ac.uk/Enggano/groups">main Enggano repository</a> on the University of Oxford's Sustainable Digital Scholarship (SDS))</em></p> <h1>Updates in version 2.0.0</h1> <p>The following items summarise the major updates in version 2.0.0:</p> <ul> <li> <p><strong>Adding <a href="https://github.com/engganolang/holle-list-enggano-1895/blob/main/cldf/media.csv">MediaTable</a></strong> to accommodate <a href="https://github.com/engganolang/holle-list-enggano-1895/tree/main/img">images</a> in/for note ID <26> (commits <a href="https://github.com/engganolang/holle-list-enggano-1895/commit/dab95401f128bd4203a81294f2e9f4620d45b145">dab9540</a> & <a href="https://github.com/engganolang/holle-list-enggano-1895/commit/a0040038e2577cb37ff8ad3ac68e8bbdedc26291">a004003</a> <a href="https://github.com/engganolang/holle-list-enggano-1895/blob/a0040038e2577cb37ff8ad3ac68e8bbdedc26291/code/Enggano-Holle-List-with-NBL.R#L229-L232">at this line</a> and <a href="https://github.com/engganolang/holle-list-enggano-1895/blob/2aab3ab385fc82c0613de2aacb5947ca4981b417/code/Enggano-Holle-List-with-NBL.R#L370-L397">these lines</a>)</p> </li> <li> <p><strong>Splitting multiple forms in a cell</strong> into their own rows, both for the original list and the forms in the Notes (commit <a href="https://github.com/engganolang/holle-list-enggano-1895/commit/39cdc663843b265aa8f3c5bbdcb11628fcc17b5e">39cdc66</a> at <a href="https://github.com/engganolang/holle-list-enggano-1895/commit/39cdc663843b265aa8f3c5bbdcb11628fcc17b5e#diff-ac46f8a3edb85868970d77f55bb86c5f0449feb25c37295895c4a9e560564301R83">this line</a> and <a href="https://github.com/engganolang/holle-list-enggano-1895/commit/39cdc663843b265aa8f3c5bbdcb11628fcc17b5e#diff-ac46f8a3edb85868970d77f55bb86c5f0449feb25c37295895c4a9e560564301R83">this line</a>, and commit <a href="https://github.com/engganolang/holle-list-enggano-1895/commit/a0040038e2577cb37ff8ad3ac68e8bbdedc26291">a004003</a> at <a href="https://github.com/engganolang/holle-list-enggano-1895/commit/a0040038e2577cb37ff8ad3ac68e8bbdedc26291#diff-5b59e4a74b953f80c8867a60c422bd8405c9d6236bd61239a0d3f20b0d582b78R69">this line</a>)</p> </li> <li> <p><strong>Orthography transliteration</strong> into Enggano's common orthography and IPA (across several commits and [closed] issues [#1 #3 #4 #5 #7], but see <a href="https://github.com/engganolang/holle-list-enggano-1895/blob/2aab3ab385fc82c0613de2aacb5947ca4981b417/code/Enggano-Holle-List-with-NBL.R#L32-L93">these lines</a> for retrieving the existing orthography profile and doing the editing, and <a href="https://github.com/engganolang/holle-list-enggano-1895/blob/2aab3ab385fc82c0613de2aacb5947ca4981b417/code/Enggano-Holle-List-with-NBL.R#L220-L306">these lines</a> for running the transliteration using the <a href="https://cran.r-project.org/web/packages/qlcData/index.html">qlcData</a> R package [Moran & Cysouw 2018; Cysouw 2024])</p> <ul> <li>In the <a href="https://github.com/engganolang/holle-list-enggano-1895/blob/main/cldf/forms.csv">FormTable</a>, the <code>Form</code> column contains the Enggano forms in their common orthography; the <code>Value</code> column contains their original transcription/orthography, with their tokenised/segmented formats available under the <code>Graphemes</code> column; the <code>Segments</code> column, finally, contains the segmented IPA transliteration of the Enggano forms (cf. #6 ). The <code>Comment</code> column is derived from the contents of the Notes. It includes, if any, Enggano forms in their original transcription followed by their segmented/tokenised forms in IPA in square brackets, their glosses in English (<strong>EN</strong>) and/or Indonesian (<strong>ID</strong>) inside the bracket, and finally the ID of the Notes in the original document inside angular brackets. The <code>English</code> and <code>Indonesian</code> columns respectively are glosses of the given language from the master/main Holle List (Stokhof 1980) that has been digitised (Rajeg 2023a).</li> <li>The output files of the orthography profiling and transliteration (commit <a href="https://github.com/engganolang/holle-list-enggano-1895/commit/2aab3ab385fc82c0613de2aacb5947ca4981b417">2aab3ab</a>) are available in <a href="https://github.com/engganolang/holle-list-enggano-1895/tree/main/data-raw">data-raw</a> with the file names prefixed with <code>ortho-...</code>.</li> </ul> </li> </ul> <h2>References</h2> <p>Cysouw, Michael. 2024. qlcData: Processing Data for Quantitative Language Comparison. https://cran.r-project.org/web/packages/qlcData/index.html. (25 December, 2024). Version 0.3</p> <p>Forkel, Robert, Johann-Mattis List, Simon J. Greenhill, Christoph Rzymski, Sebastian Bank, Michael Cysouw, Harald Hammarström, Martin Haspelmath, Gereon A. Kaiping & Russell D. Gray. 2018. Cross-Linguistic Data Formats, advancing data sharing and re-use in comparative linguistics. Scientific Data. Nature Publishing Group 5(1). 180205. https://doi.org/10.1038/sdata.2018.205.</p> <p>Moran, Steven & Michael Cysouw. 2018. The Unicode cookbook for linguists: Managing writing systems using orthography profiles (Translation and Multilingual Natural Language Processing 10). Berlin: Language Science Press. https://doi.org/10.5281/zenodo.1296780.</p> <p>Rajeg, Gede Primahadi Wijaya. 2023a. Digitised, Searchable Holle List in Stokhof (1980) [Data set]. (1.3.0). Zenodo. https://doi.org/10.5281/ZENODO.7972273. https://engganolang.github.io/digitised-holle-list/. https://ora.ox.ac.uk/objects/uuid:a511951b-86fb-4019-94d4-280efa83de02</p> <p>Rajeg, Gede Primahadi Wijaya. 2023b. CLDF dataset of the Enggano word list from 1895 in Stokhof and Almanar's (1987) Holle List [Data set]. https://github.com/engganolang/holle-list-enggano-1895 https://doi.org/10.25446/oxford.23515788</p> <p>Stokhof, W. A. L., ed. 1980. Holle Lists, Vocabularies in Languages of Indonesia, Vol. 1: Introductory Volume. Vol. Materials in Languages of Indonesia. Canberra, A.C.T., Australia: Dept. of Linguistics, Research School of Pacific Studies, The Australian National University. https://core.ac.uk/reader/159464813.</p> <p>Stokhof, W. A. L., and Alma E. Almanar. 1987. Holle Lists, Vocabularies in Languages of Indonesia, Vol. 10/3: Islands Off the West Coast of Sumatra. Vol. Materials in Languages of Indonesia. Pacific Linguistics (Series d) 76. Canberra, A.C.T., Australia: Dept. of Linguistics, Research School of Pacific Studies, The Australian National University. http://hdl.handle.net/1885/144589.</p>
Dataset of "MoO3-xNiMoO4 nanorods synthetized using NiO nanoparticles for hydrogen evolution in anion exchange membrane water electrolysis"
<p>Novel method of Mo-Ni catalyst for hydrogen evolution reaction in anion exchange membrane water electrolysis was used. Complete physico-chemical and electrochemical characterization was done. Prepared material showed enhanced performance when compared to the similar Ni based materials. Physico-chemical characterization showed, that final material is formed by NiMoO4 nanorods coverd on the surface by the layer of the MoO3-x.</p>
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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
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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.