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12,662 results for “Linking”
List of Links to Digital Resources for Latin and Ancient Greek
<p>List of Links to Digital Resources for Latin and Ancient Greek</p> <p>The list was produced as an appendix to the German publication "Wie die Digitalisierung unseren Umgang mit den Alten Sprachen verändert hat" (How Digitization Changed the Way We Deal with Latin and Ancient Greek) in the journal "Forum Classicum", scheduled for release at the end of the year 2020.</p> <p>It contains references to various resources, such as text editions, databases, teaching materials, newspaper articles, tools for natural language processing and more. Most of them are available in English, some only in German. The list is sorted by the appearance of links in the article.</p> <p>Changelog:</p> <p>Version 2.0: Added headings from the paper to indicate topics for each part of the link list. English translations for the German headings are given in brackets.</p> <p>The list:</p> <p>Wie die Digitalisierung unseren Umgang mit den Alten Sprachen verändert hat / Linkliste (How Digitization Changed the Way We Deal with Latin and Ancient Greek / Link List)<br>A. Umgang mit der Literatur und anderen Wissensbeständen (Dealing with Literature and Other Data Collections)<br>1. Digitale Textsammlungen sind schnell verfügbar und unterstützen Lehre und Forschung. (Digital text collections are quickly accessible and support teaching as well as research.)<br>https://www.degruyter.com/view/db/btltll <br>http://stephanus.tlg.uci.edu/ <br>https://cil.bbaw.de/ <br>https://latin.packhum.org/ <br>http://cite-architecture.org/cts/ <br>https://referenceworks.brillonline.com/entries/brill-s-new-pauly/ancient-authors-and-titles-of-works-Ancient_Authors_and_Titles_of_Works <br>http://www.perseus.tufts.edu/hopper/collection?collection=Perseus:collection:Greco-Roman <br>https://tesserae.caset.buffalo.edu/<br>2. Digitale Datenbanken ermöglichen schnelle systematische Suchanfragen in großen Text- oder Informationsbeständen, auch über disziplinäre Grenzen hinweg. (Digital databases enable quick systematic queries for large collections of texts and other information, even beyond disciplinary boundaries.)<br>https://about.brepolis.net/lannee-philologique-aph/ <br>https://www.gbd.digital/metaopac/start.do?View=gnomon <br>https://referenceworks.brillonline.com/browse/brill-s-new-pauly <br>https://www.navigium.de/ <br>https://www.navigium.de/latein-unterrichten.html <br>http://lehrerportal.ccbuchner.de/Textanalyse/Default.aspx <br>https://open-educational-resources.de/ <br>https://github.com/sommerschield/ancient-text-restoration <br>3. Digitale Datenbestände werden vernetzt und für neue Anwendungszwecke kombiniert. (Digital data collections can be interconnected and combined for new use cases.)<br>https://www.w3.org/standards/semanticweb/data <br>https://lila-erc.eu/ <br>https://peripleo.pelagios.org/ <br>https://medium.com/pelagios/linked-open-data-to-navigate-the-past-using-peripleo-in-class-4286b3089bf3 <br>https://topostext.org/ <br>4. Die maschinelle sprachliche Vorverarbeitung antiker Texte erleichtert den Zugang für Lernende und Forschende. (Natural language processing of ancient texts facilitates access for both teachers and researchers.)<br>http://www.lemlat3.eu/ <br>https://d.iogen.es/ <br>https://alpheios.net/</p> <p>B. Umgang mit dem Spracherwerb (Dealing with Language Acquisition)<br>5. Die Digitalisierung fördert einen multimodalen und inklusiven Spracherwerb. (Digitization supports multimodal and inclusive language acquisition.)<br>https://www.hearinglink.org/living/loops-equipment/hearing-loops/what-is-a-hearing-loop/<br>http://www.cross-plus-a.com/balabolka.htm<br>https://propylaeum.de/e-learning/historische-aussprache-des-lateinischen-und-altgriechischen<br>https://www.youtube.com/watch?v=R5vdg_2i_pU<br>https://www.lesediagnostik.de/eye-tracking/<br>https://www.youtube.com/watch?v=8QocWsWd7fc<br>https://www.speechtexter.com/<br>https://etherpad.org/<br>https://moodle.org<br>6. Der Spracherwerb kann flexibel und personalisiert gestaltet werden. (Language acquisition can be designed in a flexible and personalized manner.)</p> <p>C. Umgang mit der Öffentlichkeit (Dealing with the Public)<br>https://www.che.de/third-mission/<br>7. Social Media ermöglichen eine schnelle Interessens- und Wissensvernetzung innerhalb und vor allem außerhalb einer definierten Gemeinschaft. (Social Media enable us to quickly connect interests and knowledge inside and especially outside of a specific community.)<br>https://la.wikipedia.org/wiki/Vicipaedia_Latina<br>http://forum.latein24.de/<br>https://twitter.com/RomAthen<br>https://www.projekte.hu-berlin.de/de/callidus/blog-2017-2018<br>https://www.superprof.de/blog/lateinische-begriffe-im-deutschen/<br>https://www.facebook.com/klassphil/?__tn__=%2Cd%2CP-R&eid=ARDXqBAnvPxAePqFMxWrKxnFG2nfqqzKDWdoHdSg1CBNwBmcZbHwF5f8IWuQZXEODH6VKzqzWvUvUzfU<br>https://www.instagram.com/fs_klassphil_tuebingen/<br>https://hu-berlin.academia.edu/MarkusAsper<br>https://www.researchgate.net/profile/Monica_Berti<br>https://www.br.de/alphalernen/faecher/latein/latein-einfach-erklaert-100.html<br>https://www.pinterest.de/pin/5418462037462026/<br>https://www.youtube.com/channel/UChB8TYnAEtSIL1mY7FuBoqA<br>https://learnattack.de/latein/saetze-uebersetzen?utm_campaign=Learnattack_Kanal&utm_source=youtube.com&utm_medium=social&utm_content=saetze-uebersetzen-latein&kanal=youtube#video-wie-du-einen-lateinischen-satz-%C3%BCbersetzt<br>https://vimeo.com/276706092<br>8. Der digitale weltweite Zugang zu und Austausch von Wissen fördert das informelle Lernen und die Open-Science-Bewegung. (The worldwide digital access to and exchange of knowledge supports informal learning and the Open Science movement.)<br>https://www.udemy.com/course/an-introduction-to-classical-latin/<br>https://www.coursera.org/learn/roman-architecture<br>https://www.coursera.org/learn/plato<br>https://www.youtube.com/channel/UCNW1n7ctSkW3cgYFCzKPK3A/videos<br>https://scholar.google.de/<br>https://www.kim.uni-konstanz.de/openscience/onlinekurs-open-science-von-daten-zu-publikationen/<br>https://www.go-fair.org/fair-principles/<br>https://zenodo.org/record/3601182<br>https://zenodo.org/record/3816709<br>https://scm.cms.hu-berlin.de/callidus<br>https://www.ianus-fdz.de/<br>https://opr.degruyter.com/<br>http://ahropenreview.com/<br>https://arxiv.org/help/trackback<br>https://www.propylaeum.de/<br>https://journals.ub.uni-heidelberg.de/index.php/dco/index<br>http://www.pegasus-onlinezeitschrift.de/<br>https://www.schule-bw.de/faecher-und-schularten/sprachen-und-literatur/latein<br>https://www.schule-bw.de/faecher-und-schularten/sprachen-und-literatur/griechisch<br>https://www.bmbf.de/de/citizen-science-wissenschaft-erreicht-die-mitte-der-gesellschaft-225.html<br>https://pleiades.stoa.org/home</p> <p>Fazit (Conclusion)<br>http://pom.bbaw.de/cmg/</p>
Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site
<p>Metabolomics dataset used in the publication "Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site"</p> <p>Jaanika Kronberg, Jonathan J. Byrne, Jeroen Jansen, Philipp Antczak, Adam Hines, John Bignell, Ioanna Katsiadaki, Mark R. Viant and Francesco Falciani </p> <p>Metabolomics dataset for metabolic bins 1 to 1045 for 376 mussels as used in the publication.</p> <p>Mussel metadata are described in a separate file (spectrum number, sample label, sex, site, species, month, temperature of water, salinity of water, ADG rate, gonadal stage, parasite load)</p> <p>Species 1: Mytilus edulis, species 2: hybrid, species 3: Mytilus galloprovincialis</p>
Plate model for 'Extending Full-Plate Tectonic Models into Deep Time: Linking the Neoproterozoic and the Phanerozoic '
<p>Plate model for the last 1 Ga accompanying Merdith et al. (2021), Earth Science Reviews.</p> <p>-changes</p> <p>Correction to animation file.</p>
Supplement 1: Full list of ICD10 codes and number of gene-disease links (tab-separated-value file); Supplement 2: Mapping (tab-separated-value file)
<p>Supplements to BioMedBridges deliverable 10.2 A prototype linking ICD10/SNOMED CT concepts to Ensembl gene identifiers:</p> <p><strong>Supplement 1</strong>: Full list of ICD10 codes and number of gene-disease links: table_icd10_gene_count_descr.tsv</p> <p><strong>Supplement 2</strong>: Mapping of disease terms: <em>ICD10_to_doid.tsv</em></p>
Link-prediction on Biomedical Knowledge Graphs
<p>Release of code and experimental data from the paper <em>Towards Linking Graph Topology to Model Performance for Biomedical Knowledge Graph Completion </em>(<em>Machine Learning for Life and Material Sciences</em> workshop @ ICML2024) and <a href="https://arxiv.org/abs/2409.04103" rel="nofollow">The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models</a>.</p> <div> <div>Knowledge Graph Completion has been increasingly adopted as a useful method for several tasks in biomedical research, like drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models has been proposed over the years. However, little is known about the properties that render a dataset useful for a given task and, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. We conduct a comprehensive investigation into the topological properties of publicly available biomedical Knowledge Graphs and establish links to the accuracy observed in real-world applications. By releasing all model predictions we invite the community to build upon our work and continue improving the understanding of these crucial applications.</div> <div> </div> <div>Experiments were conducted on six datasets: five from the biomedical domain (<a href="../records/268568">Hetionet</a>, <a href="https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IXA7BM">PrimeKG</a>, <a href="../records/4077338">PharmKG</a>, <a href="../records/5361324">OpenBioLink2020 HQ</a>, <a href="../records/7011027">PharMeBINet</a>) and one trivia KG (<a href="https://aclanthology.org/W15-4007.pdf">FB15k-237</a>). All datasets were randomly split into training, validation and test set (80% / 10% / 10%; in the case of PharMeBINet, 99.3% / 0.35% / 0.35% to mitigate the increased inference cost on the larger dataset).</div> <div>On each dataset, five different KGE models were compared: <a href="https://dl.acm.org/doi/10.5555/2999792.2999923">TransE</a>, <a href="https://arxiv.org/abs/1412.6575">DistMult</a>, <a href="https://arxiv.org/abs/1902.10197">RotatE</a>, <a href="https://arxiv.org/abs/2209.08271">TripleRE</a>, <a href="https://dl.acm.org/doi/10.5555/3504035.3504256">ConvE</a>. Hyperparameters were tuned on the validation split (see final train configurations in <code>train/scripts</code>). We release results for tail predictions on the test split. In particular, each test query <code>(h,r,?)</code> is scored against all entities in the KG and we compute the rank of the score of the correct completion <code>(h,r,t)</code> , after masking out scores of other <code>(h,r,t')</code> triples contained in the graph.</div> <div>Note: the ranks provided are computed as the average between the optimistic and pessimistic ranks of triple scores.</div> <div> </div> <div>Inside <code>experimental_data.zip</code>, the following files are provided.</div> <div> <ul> <li><code>datasets/{dataset}</code>: a folder for each dataset, containing <ul> <li><code>{dataset}_preprocessing.ipynb</code>: a Jupyter notebook for downloading and preprocessing the datasets. In particular, this generates the custom label->ID mapping for entities and relations, and the numerical tensor of <code>(h_ID,r_ID,t_ID)</code> triples for all edges in the graph, which can be used to compute graph topological metrics (e.g., using <a href="https://github.com/graphcore-research/kg-topology-toolbox">kg-topology-toolbox</a>) and compare them with the edge prediction accuracy.</li> <li><code>test_ranks.csv</code>: csv table with columns <code>["h", "r", "t"]</code> specifying the head, relation, tail IDs of the test triples, and columns <code>["DistMult", "TransE", "RotatE", "TripleRE", "ConvE"]</code> with the rank of the ground-truth tail in the ordered list of predictions made by the five KGE models;</li> <li><code>entity_dict.csv</code>: list of entity labels, ordered by entity ID (as generated in the preprocessing notebook);</li> <li><code>relation_dict.csv</code>: list of relation labels, ordered by relation ID (as generated in the preprocessing notebook).</li> </ul> </li> <li><code>train</code>: code to reproduce training (and validation) of the five KGE models, using the <a href="https://github.com/graphcore-research/bess-kge">BESS-KGE</a> distribution framework. <ul> <li><code>train/scripts</code>: executable scripts, with specifications of the final hyperparameters for all models and datasets.</li> </ul> </li> <li><code>notebooks</code>: Jupyter notebooks for data analysis and generation of all the figures in the paper.</li> </ul> <p>The separate <code>top_100_tail_predictions.zip</code> archive contains, for each of the test queries in the corresponding <code>test_ranks.csv</code> table, the IDs of the top-100 tail predictions made by each of the five KGE models, ordered by decreasing likelihood. The predictions are released in a <code>.npz</code> archive of numpy arrays (one array of shape <code>(n_test_triples, 100)</code> for each of the KGE models). </p> </div> </div>
Video, image, and supplemental files linked in Burge et al. (2023) "Depredation by Bottlenose Dolphins Tursiops truncatus from Antillean Z-traps at Discovery Bay, Jamaica"
<p>Video, image, and supplementary text files linked in Burge et al. (2023), Caribbean Naturalist, 95: 1–25.</p><p><strong>Depredation by Bottlenose Dolphins </strong><i><strong>Tursiops truncatus</strong></i><strong> from Antillean Z-traps at Discovery Bay, Jamaica</strong></p><p>All video and image files referred to in the main text, figures, and tables are available from this repository. See Table 1 and Table S1 for additional details.</p><p> </p>
Section 5.3 "Task Area 3: Multimodal data linking and integration" Figure 10
<p>Figure 10. Data flow to obtain a multimodal data structure (mmDS) with an overarching graph database (MUGDAT).</p> <p>from NFDI Grant Application, "<strong>National Research Data Infrastructure for Microscopy and Bioimage Analysis</strong>" (NFDI4BIOIMAGE)</p>
The LILY Database: Linking Lithology to IODP Physical, Chemical, and Magnetic Properties Data
<p>During each expedition of the International Ocean Discovery Program and its precursor, the Integrated Ocean Drilling Program (jointly referred to as IODP), vast arrays of data are collected from drill cores. These data, which are accessible from the IODP LIMS (Laboratory Information Management System) database, include physical, chemical, and magnetic properties collected semi-continuously along cores using automated track systems, as well as a variety of analyses conducted on discrete subsamples taken from the cores. In addition, the lithology of all cores is described based on visual characteristics of the surface of split cores, visual examination of smear slides and thin sections, and compositional or mineralogical information derived from geochemical analyses. We extract basic lithologic information from this complex array of descriptive information and then tie that information to all other measurements. This new database is referred to as <strong>LI</strong>MS with <strong>L</strong>itholog<strong>y</strong> (LILY). LILY currently contains over 34 million data from 89 km of core recovered on 42 expeditions conducted 2009-2019. Some uses of LILY include identifying the abundance of different lithologies, finding data from core intervals with a specific lithology, assessing the efficacy of coring systems in different lithologies, or characterizing and analyzing physical, chemical, and magnetic properties based on lithology. We illustrate the use of LILY by computing the grain density by lithology from over 24,000 moisture and density measurements and then use those grain densities, along with the large IODP bulk density dataset, to compute a new high-resolution porosity dataset with over 3.7 million new porosity estimates.</p> <h2>CONTENT DESCRIPTION:</h2> <p><strong>The main LILY database is stored in the files with the suffix DataLITH.csv.</strong> Each file contains IODP LIMS data with lithology and other metadata added. The file prefix gives the type of data. For example, AVS_DataLITH.csv contains the Automated Vane Shear (AVS) shear strength data paired with lithology and other metadata. A list of all data types is given in Supporting Information Table S1 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). There are a total of 23 DataLITH files.</p> <ul> <li>AVS_DataLITH.csv: automated vane shear; shear strength measurements.</li> <li>CARB_DataLITH.csv: total carbon, hydrogen, nitrogen, and sulfur, inorganic carbon (carbonate), and organic carbon measured on discrete samples.</li> <li>GE_DataLITH.csv: gas elements from gas chromatography.</li> <li>GRA_DataLITH.csv: gamma ray attenuation bulk density from the Whole-Round Multisensor Logger (WRMSL).</li> <li>ICP_DataLITH.csv: Inductively-coupled plasma data.</li> <li>IW_DataLITH.csv: interstitial water chemistry.</li> <li>JR6A_DataLITH.csv: discrete magnetic measurements from the JR6A spinner magnetometer.</li> <li>KAPPA_DataLITH.csv: Kappabridge susceptibility meter measurements.</li> <li>MAD_DataLITH.csv: moisture and density from discrete samples.</li> <li>MS_DataLITH.csv: magnetic susceptibility from the WRMSL.</li> <li>MSP_DataLITH.csv: point magnetic susceptibility from the Section Half Multisensor Core Logger (SHMSL).</li> <li>NGR_DataLITH.csv: natural gamma radiation from the Natural Gamma Radiation Logger (NGRL).</li> <li>PEN_DataLITH.csv: pocket penetrometer compressional strength measurements.</li> <li>PWB_DataLITH.csv: P-wave velocity from the bayonet system.</li> <li>PWC_DataLITH.csv: P-wave velocity from the caliper system.</li> <li>PWL_DataLITH.csv: P-wave velocity from the WRMSL.</li> <li>RGB_DataLITH.csv: Red-Green-Blue color from the Section Half Imaging Logger (SHIL).</li> <li>RSC_DataLITH.csv: reflectance spectroscopy from the SHMSL.</li> <li>SRA_DataLITH.csv: source rock analyzer measurements.</li> <li>SRM_DataLITH.csv: Superconducting Rock Magnetometer (SRM) measurements of split-core sections.</li> <li>SRMD_DataLITH.csv: SRM measurements of discrete samples.</li> <li>TCON_DataLITH.csv: thermal conductivity measured with the Teka Berlin TK04 probe.</li> <li>TOR_DataLITH.csv: Torvane shear strength measurements.</li> </ul> <p>Other compressed data folders contain multiple files used in creating the LILY database:</p> <p>RawDESC.zip: Contains 7,940 .csv files derived from the raw text content of the DESClogik Excel worksheets that was extracted, converted to comma separated value (.csv) format, and put into files with a consistent naming convention, without applying any corrections or conversions to the original text. Each file is the direct extraction of a tab from the DESC workbooks, available at <a href="https://web.iodp.tamu.edu/DESCReport/">https://web.iodp.tamu.edu/DESCReport/</a></p> <p>CoreSUMM.zip: Contains one file with Core Summary information, which includes the expedition, site, hole, core, coring type, top and bottom depths drilled, advances and recoveries, time and date of recovery, and the number of sections. These data are further paired with additional metadata (expanded core type, latitude, longitude, and water depth). Coordinates and water depth for each hole are derived from LIMS (and the JANUS database at <a href="http://www-odp.tamu.edu/database/">http://www-odp.tamu.edu/database/</a> for older expeditions).</p> <p>RawDATA.zip: Contains the raw track/discrete dataset downloaded by expedition from IODP LIMS database and placed in folders for each type of data (AVS, CARB, SRM, etc.) </p> <p>RawLITH.zip: Contains 42 .csv files, with one file for each expedition. Each file contains all lithologic description (prefix, principal and suffix, etc.) information for an entire expedition, as it was originally described. These have been transformed to a consistent format and paired with consistent identification information and additional metadata. Headers are normalized across all expeditions and SampleID information is standardized.</p> <p>CleanLITH: Contains 42 .csv files. Each file contains all lithologic description (prefix, principal and suffix) information for an entire expedition. The lithologic descriptions have been standardized to a consistent nomenclature using the dictionary given in Support Information Table S4 of Childress et al. (2024, <a href="https://doi.org/10.1029/2023GC011287">https://doi.org/10.1029/2023GC011287</a>). These data are further paired with additional metadata (e.g., degree of consolidation, expanded core type, latitude, longitude, and water depth).</p> <h2>GitHub Repository:</h2> <ul> <li>Contains a few notebooks to demonstrate how to work with the LILY database</li> <li><a href="https://github.com/IODP/LILY">IODP LILY GitHub Repository</a></li> </ul>
How is tree growth rate linked to root functional traits in phylogenetically related poplar hybrids?
<p>Fine roots play a crucial role in soil nutrient and water acquisition, significantly contributing to tree growth. Fine roots with a high specific root length (SRL) and small diameter are often considered to help trees grow fast. However, inconsistencies in the literature do not provide a clear basis on the effect of root functional traits, such as SRL or root mass density (RMD), on tree growth rate in phylogenetically related trees. Our aim was to examine relationships between tree growth rate and root functional traits, using clones displaying different growth rates in a hybrid poplar plantation located in New Liskeard, ON, Canada. Fine roots (diameter < 2 mm) samples were collected using soil cores at depths of 0–20, 20–40 and 40–60 cm, and analyzed for morphological, chemical and architectural traits. High SRL and thin fine roots were associated with the least productive clones, which is not consistent with the root economics spectrum (RES) theory. However, the most productive clone had larger fine root diameter and higher root lignin concentrations, probably reducing root construction and maintenance costs and C losses. Therefore, at the 0–20 and 20–40 cm depths, tree growth rates showed positive correlations with root diameter and root lignin concentrations, but negative correlations with SRL and root soluble compounds concentration. Increasing RMD at the 0–20 cm depth promoted tree growth rates, showing the importance of soil exploration in the topsoil for tree growth. We conclude that fine root variation does not always follow the RES hypothesis and argue that the rapid growth rate of trees may also be driven by fine root growth in diameter and mass in phylogenetically related trees.</p>
Linked Open African Red Slip Ware
<ul> <li>first release after project ending</li> </ul> <p>Characteristic of the North African bowls, plates, and jugs are their pictorial decorations applied mainly by appliqués and stamps. As mass-produced image carriers and everyday objects, the ARS spread throughout the empire.</p> <p>The range of motifs includes mythological scenes as well as scenes from the Old and New Testament, circus, arena and hunting scenes as well as fish and plant motifs. The appliqués-decorated pottery thus provides insights into Late Antique imagination and its changes, as well as into the economic history of the period between the 3rd and 5th centuries AD in North Africa.</p> <p>Previous documentation methods were not able to capture the objects and their decoration in an adequate way. The digital recording of the RGZM's collections by 3D scans allows to compare potentially identical appliqués and to assign them to their negative forms and the corresponding stamps.</p> <p>Whereas vessel curvature previously falsified the assignment of appliqués and models, 3D analysis and visualisation tools now allow a comparison . Metadata created for each object increases the effectiveness and accuracy of determining image context and content. Issues related to the production of the ARS and the process flows within the workshops can be investigated through the analysis of the 3D data.</p> <p>The developed ontology uses CIDOC CRM and various extensions. The data mainly consists of objects (E24), on which e.g. applications (features, E25) can be found. These objects can be described semantically, e.g. by the shape or time epoch. Features are described with observations, e.g. a human type man standing and wearing a beard. These observations lead to interpretations whose arguments can be observations.</p>
COSI-Article matrix: linking ISCB Communities of Special Interest to Wikipedia
<p>Wikipedia is regarded as one of the most important channels for the public communication of science; English Wikipedia has around 1,500 articles relating to computational biology, which are frequently accessed as an educational resource. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further quality improvement, primarily while comparing to related scientific fields such as genetics and medicine. Facilitating the involvement of members from ISCB COSIs (Communities of Special Interest) would improve a vital open educational resource in computational biology, additionally allowing COSIs to provide a quality educational resource particular to their subfield.</p> <p>This first version of the COSI-Article matrix is a binary matrix identifying relevant ISCB COSIs for all Wikipedia articles relating to computational biology, defining a domain-specific open educational resource for each COSI. In addition, quality and importance ratings for each article allow identification of areas where domain experts could improve computational biology representation.</p>
TWikiL - Twitter Wikipedia Link Dataset
<p>The Twitter Wikipedia Link (TWikiL) dataset contains all Tweets posted on Twitter that contain a Wikipedia URL. The data was collected via Twitters academic research access and spans 15 years of Tweets from March 2006 to January 2021. TWikiL comes in two versions: <strong>TWikiL_raw</strong> is a list of Tweet IDs in CSV format. <strong>TWikiL_curated</strong> is an SQLite database, which is a curated version of TWikiL containing only links to Wikipedia articles. The curated version has been augmented with the language edition that the URL in the Tweet links to, the Wikidata identifier and a Wikipedia topic category. <br> <br> TWikiL raw contains 44,945,098 Tweet IDs<br> TWikiL curated contains 35,252,782 URLs/Wikidata concepts with 34,543,612 unique Tweets and 474,577 Tweets linking to multiple Wikipedia articles.</p>
Avena barbata Link agg. (Poaceae) in Flora iberica XIX(II)
<p>Testimonios de herbario en los que se basan los resultados correspondientes a los taxones del complejo de <em>Avena barbata</em> Link, dentro del capítulo del género <em>Avena</em> L. (Poaceae), publicado en el libro <em>Flora iberica</em>, vol. XIX(II) Gramineae (partim), ISBN: 978-84-00-10817-5, páginas: 772-792 (2021).</p>
Data Sets for SNR Estimation in Flexible Optical Networks: Lightpath, Link, and Span Levels
<p>These data sets have been generated based on the analytic models [1,2] to estimate signal to the noise ratio (SNR) for spans, links, and lightpaths of a Flexible Optical Network (FON) over standard single-mode fiber (SSMF). For PM-BPSK and PM-QPSK modulation format levels, equation 41-43 [1], and for PM-8-64QAM modulation format levels, equation 7.32 [2], are applied.</p> <p>[1] P. Poggiolini, G. Bosco, A. Carena, V. Curri, Y. Jiang and F. Forghieri, "The GN-Model of Fiber Non-Linear Propagation and its Applications," in <em>Journal of Lightwave Technology</em>, vol. 32, no. 4, pp. 694-721, Feb.15, 2014, DOI: 10.1109/JLT.2013.2295208.</p> <p>[2] P. Poggiolini, Y. Jiang, A. Carena and F. Forghieri, "Analytical modeling of the impact of fiber non-linear propagation on coherent systems and networks" in Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks, New York, NY, USA:Wiley, pp. 247-310, 2016.</p>
Dataset linking to the paper "Exploring characteristics of national forest inventories for integration with global space-based forest biomass data"
<p>The dataset links to the study titled “Exploring characteristics of national forest inventories for integration with global space-based forest biomass data”. This study is published in the journal “Science of the Total Environment” and the publication can be found at <a href="https://doi.org/10.1016/j.scitotenv.2022.157788">https://doi.org/10.1016/j.scitotenv.2022.157788</a>. The dataset contains four csv files that were used to produce the results and other figures in the paper. The description of the individual data files contained in the dataset is given below.</p> <p><strong>NFI availability and characteristics data: </strong>The data file “NFI_availability_characteristics.csv” contains data on the total number of NFIs, the NFI extent, and the year of the most recent NFI in countries with NFI as reported in FRA 2020 country reports. The respective data variables in the data file are termed as Number_of_NFI, Latest_NFI_extent_FRA2020, and Latest_NFI_year_FRA2020 (NFI years generally refer to the years of data collection). In addition, the data file contains data on the region and tropical domain per country. The tropical and subtropical countries were considered tropical in the analysis and interpretation of the results. These data were used to produce Figure 2 of the study. ArcMap 10.7.1 was used for this purpose. </p> <p><strong>National biomass intercomparison data: </strong>The data file “national_biomass_intercomparison.csv” contains national forest AGB data for the year 2018 from FRA 2020 and CCI Biomass product that were used in the national biomass intercomparison analysis. The total (tons) and average space-based AGB (tons/ha) are extracted directly from the CCI Biomass Map 2018 for each country included in the study. The processing is done in Python and R environments. The spatial resolution of the map is 100 m. The average FRA AGB data in tons per ha was compiled from FRA 2020 country reports. The total FRA AGB data (tons) was estimated by multiplying each country's average FRA AGB data with FRA forest area data (in ha).</p> <p>The data unit for total AGB was converted from tons to gigaton (Gt) in intercomparison analysis. The total CCI Map AGB estimates used in the analysis are termed as CCI_MAP_AGB_Gt in the data file and the average as CCI_Map_AGB_tons.ha. Similarly, the total FRA AGB data are termed as FRA_AGB_Gt and the average as FRA_AGB_ton.ha. The NFI availability and temporality were also used in intercomparison analysis and this data is termed as Latest_NFI_year_FRA2020 in the data file. The data were used to produce Figure 3 of the study in the R environment.</p> <p><strong>NFI plot design characteristics: </strong>The data file named “NFI_plot_design_characteristics.csv” contains data on variables that were used in the analysis of NFI plot designs in 46 tropical countries. This data file mainly contains the data that was used to produce Figure 4 and Figure 6 in the R environment. The value “uniform” in the sampling_stratification variable means no stratification was used in the sampling design. The variable name “psu” stands for primary sampling unit (both cluster and single plots), “psu_distance_km” for the distance between primary sampling units in km, “cluster_plotdis_m” for the distance between plots in meter in the cluster, “plotsize_ha” for plot (single and cluster plots ) size in ha, “plotshape” for plot shapes (single and cluster plots), “ILUA” for Integrated Land Use Assessment. The data were compiled from the latest NFI design manuals and NFI reports.</p> <p><strong>NFI years: </strong>The data file “NFI_years_tropical_countries_data.csv” contains data on NFI years of the latest NFI in 46 tropical countries that were used to produce Figure 1 using ArcMap 10.7.1. The years generally refer to the last years of data collection. Data were compiled from the latest country NFI design manual or NFI report. This included both ongoing and completed NFI.</p>
Cascading effects augment the direct impact of CO2 on phytoplankton growth in a biogeochemical model, links to model results
<p>This dataset provides the output of eight model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2022). In addition to information on the mesh, the dataset contains 1) 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, calcification, grazing rates, calcite concentrations, zooplankton biomass, export fluxes as well as CO<sub>2(aq)</sub>, HCO<sub>3</sub><sup>-</sup> and nutrient concentrations, and 2) a time series of global and North Atlantic coccolithophore biomass, temperature, and CO<sub>2(aq)</sub> concentrations from 1958 to 2018.</p> <p>File names refer to the Figures and Tables in the paper where the respective data are used. See “readme” for detailed information on the dataset and separate files.</p>
GERDAT010 Dataset for literature search linked to publication "Information needs of older patients newly diagnosed with cancer"
<p>Dataset of the literature search belonging to the publication "Information needs of older patients newly diagnosed with cancer"</p>
Atomic clock dataset for 'Coherent Optical-Fiber Link Across Italy and France'
<p>Dataset of the comparison of the atomic clocks at LNE-SYRTE and INRIM via optical fibre link between October 2021 and February 2022. Results discussed in Clivati et al., Coherent Optical-Fiber Link Across Italy and France, <em>Phys. Rev. Applied, American Physical Society, </em><em> 18</em>, 054009, <strong>202<em>2</em></strong>.</p> <p>The involved atomic clocks are the Cs fountains SYRTE-F02Cs, IT-CsF2, the Rb fountain SYRTE-F02Rb and the Yb optical lattice clock IT-Yb1.</p> <p>Data is organized in folders, one for each comparison. In the folders data is separated is one file per day. Data is reported as fractional frequency ratios in bins of 864 s. Timetags are reported in modified Julian date (MJD). A validity flag is given where 0 = invalid, valid otherwise. Each folder includes a yaml file with metadata required for generalized data processing as in [Lodewyck et al., 2020]. The Python package used for data processing can be found on <a href="https://github.com/INRIM/tintervals">github.</a></p> <p> </p>
Dataset of the paper "Vermicomposting as a sustainable option for the management of the biomass of the invasive tree Acacia dealbata Link."
<p>Data generated during an experiment of vermicomposting of <em>Acacia dealbata</em> fresh biomass. Four files are included: "<strong>vermicompost_and_earthworm_data.csv</strong>" and "<strong>readme.csv</strong>" are the raw data of different parameters measured in vermicompost samples during the vermicomposting of <em>Acacia dealbata</em> by the earthworm <em>Eisenia andrei</em> and an explanation of each parameter and the unit in which the parameter is expressed. </p> <p>"<strong>germination test.csv</strong>" and "<strong>radicle_length.csv</strong>" are the results of an ecotoxicological test on the effect of <em>A. dealbata</em> biomass and vermicompost on the germination and radicle elongation in <em>Lepidium sativum</em>.</p>
MS data linked to manuscript https://doi.org/10.3390/ijms22169055
<p>Dataset of MS raw data linked to the publication https://doi.org/10.3390/ijms22169055.</p> <p>Data contains:</p> <p>1. 90% MeOH fraction_Rhodococcus_neg. raw file corresponding to the UPLC-ESI-HRMS/MS spectra of the enriched fraction containing threlolipids.</p> <p>2. Mgf file generated through Mzmine. The file contains 22 aligned MS/MS spectra corresponding to the 22 growth condition of the bacteria.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.