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SIDER Side Effect Resource
<p>Unwanted side effects of drugs are a burden on patients and a severe impediment in the development of new drugs. At the same time, adverse drug reactions (ADRs) recorded during clinical trials are an important source of human phenotypic data. It is therefore essential to combine data on drugs, targets and side effects into a more complete picture of the therapeutic mechanism of actions of drugs and the ways in which they cause adverse reactions. To this end, we have created the SIDER (‘Side Effect Resource’, <a href="http://sideeffects.embl.de/">http://sideeffects.embl.de</a>) database of drugs and ADRs. The current release, SIDER 4, contains data on 1430 drugs, 5880 ADRs and 140 064 drug–ADR pairs, which is an increase of 40% compared to the previous version. For more fine-grained analyses, we extracted the frequency with which side effects occur from the package inserts. This information is available for 39% of drug–ADR pairs, 19% of which can be compared to the frequency under placebo treatment. SIDER furthermore contains a data set of drug indications, extracted from the package inserts using Natural Language Processing. These drug indications are used to reduce the rate of false positives by identifying medical terms that do not correspond to ADRs.</p>
Patch-burn grazing impacts forage resources in subtropical humid grazinglands
Subtropical humid grazing lands represent a large global land use and are important for livestock production, as well as supplying multiple ecosystem services. Patch-burn grazing (PBG) management is applied in temperate grazing lands to enhance environmental and economic sustainability; however, this management system has not been widely tested in subtropical humid grazing lands. The objective of this study was to determine how PBG affected forage resources, in comparison with the business-as usual full-burn (FB) management in both intensively managed pastures (IMP) and seminative (SN) pastures in subtropical humid grazing lands. We hypothesized that PBG management would create patch contrasts in forage quantity and nutritive value in both IMP and SN pastures, with a greater effect in SN pastures. A randomized block design experiment was established in 2017 with 16 pastures (16 ha each), 8 each in IMP and SN at Archbold Biological Station’s Buck Island Ranch in Florida. PBG management employed on IMP and SN resulted in creation of patch contrast in forage nutritive value and biomass metrics, and recent fire increased forage nutritive value. Residual standing biomass was significantly lower in burned patches of each year, creating heterogeneity within both pasture types under PBG. PBG increased digestible forage production in SN but not IMP pastures. These results suggest that PBG may be a useful management tool for enhancing forage nutritive value and creating patch contrast in both SN and IMP, but PBG does not necessarily increase production relative to FB management. The annual increase in tissue quality and digestible forage production in a PBG system as opposed to once every 3 yr in an FB system is an important consideration for ranchers. Economic impacts of PBG and FB management in the two different pasture types are discussed, and we compare and contrast results from subtropical humid grazing lands with continental temperate grazing lands.
Qualitative Larval Fish Sampling at the California Department of Water Resource’s State Water Project
The California Department of Water Resource’s State Water Project utilizes the John E. Skinner Delta Fish Protective Facility (Skinner Fish Facility) to salvage fishes that would otherwise become entrained during operations to divert water from the Sacramento-San Joaquin River Delta (Delta). Water is diverted from the Delta to meet California’s agricultural, municipal, industrial, and environmental needs. The Skinner Fish Facility, located in Contra Costa County and situated ahead of the Harvey O. Banks Pumping Plant, began salvaging fish in 1968 but historically, only recorded fork length measurements for fish greater than 20 millimeters. Beginning in 2009, the Skinner Fish Facility implemented qualitative larval sampling in response to the 2008 U.S. Fish and Wildlife Service Biological Opinion on the coordinated operations of the Central Valley Project (CVP) and State Water Project (SWP). This entailed collecting, retaining, and identifying larval fishes to better understand SWP impacts on Delta Smelt. Qualitative larval sampling took place annually from 2009 through 2025, during the Old and Middle River management period and based upon Delta Smelt spawning (typically mid-February to June). The California Department of Water Resources collected and processed samples from 2020 through 2025. Data from 2009 through 2019 were processed and retained by others and are not included in this dataset.
Resource Gradient Experiment at the Kellogg Biological Station, Hickory Corners, MI (1999 to 2019)
Dataset Abstract This study provides a gradient of 9 different rates of nitrogen fertilization under rainfed and irrigated conditions. Irrigation began in 2003. Corn was grown from 2000-2005 and subsequently the crop rotation (wheat, corn, soybean) followed the crop of the LTER main site. Nitrogen applications differ by crop after 2007. The experiment was moved to its current location on the LTER main site in 2005. The experiment location/history are explained here. Plots are 5×30 m arranged in each of 4 replicate blocks . Crop yields, nitrous oxide and soil temperature, moisture and nitrogen data are available for this study. original data source http://lter.kbs.msu.edu/datasets/36
SBC LTER: Beach: Sandy beach prey resource use by surfperch across tidal phase
These data describe trophic links between sandy beach and an associated surf zone fish species. The datasets are the result of a short-term study investigating the effect of tidal phase on a local sandy beach macroinvertebrate community and the diet of barred surfperch (Amphistichus argenteus) during the summer and fall of 2020. The beach invertebrate population dataset details the abundance and biomass of taxon within each beach intertidal zone across three paired neap and spring tidal phases. The diet datasets report the counts and sizes of prey taxon observed in barred surfperch stomach samples taken at the time of each beach sampling event. Data are contained in three tables: 1) the beach macroinvertebrate population data, 2) prey counts from barred surfperch stomach content samples, and 3) the sizes of prey in stomach samples.
SBC LTER: Santa Cruz Island: Abundance and Biomass of Benthic Organisms (food resource collection)
These data describe the abundance of benthic organisms as determined by random quadrat scrapings. These data represent the availability of food resources for fish at various depths, and are part of a long term investigation of temporal patterns in reef community composition. The sampling locations in this dataset include three sites along the north shore of Santa Cruz Island. Data collection began in 1982 and this dataset is updated annually.
Techno-economic sustainability analysis methodology for conversion routes of renewable feedstock resources to bio-based products – case studies
<p>The dataset provides a set of sustainability principles, criteria and indicators for the evaluation of the conversion routes stage of a bio-based product. The selected case studies on the employment of alternative feedstocks and production of the bio-based products are implemented in order to evaluate the proposed methodology. Mass and energy balances for all case studies, estimated techno-economic metrics, cost of externalities and risk assessment results are provided</p>
Cooperative Proactive resource management for 5G in the unlicensed spectrum open data
<p>The data set consists of the following files:</p> <p><strong>1)COT information:</strong> The channel occupancy time of each channel for the first 5000 measurements. The COT values range from 0 to 1.</p> <p><strong>2)QL decisions uniform traffic:</strong> The decisions of QL for the channel utilization of the available SBS and their impact to the achieved throughput. In this file we consider uniform traffic generation patterns.</p> <p><strong>3)QL decisions NON uniform traffic:</strong> The decisions of QL for the channel utilization of the available SBS and their impact to the achieved throughput. In this file we consider non-uniform traffic generation patterns.</p> <p><strong>4)Performance measurements: </strong>The final results of the experiment in respect to the transmit power control and throughput measurements under different QL configurations.</p>
GREEN-VARAN scores resources (DANN GRCh37)
<p>Processed DANN scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh37 version for DANN.</p> <p>See: <a href="https://academic.oup.com/bioinformatics/article/31/5/761/2748191">https://academic.oup.com/bioinformatics/article/31/5/761/2748191</a></p> <p>If you use DANN score annotations with GREEN-VARAN don't forget to cite also the original DANN paper.</p>
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>
OptiSpot: Minimizing Application Deployment Cost using Spot Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1800 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_D_E_F_G.mat</p> <p>Where the fields A, B, C, D, E, F, and G are described as follows.</p> <p>A: number of users.<br /> Considered values are: 1000, 2000, 5000, 10000.</p> <p>B: maximum response time in milliseconds.<br /> Considered values are: 60, 80, 100, 200.</p> <p>C: overbid time cap in hours.<br /> Considered values are: 5, 20, 80, 0 (note: 0 is a code used to express infinite hours).</p> <p>D: Amazon region.<br /> Considered values are: us-east, eu-west.</p> <p>E: Operating system.<br /> Considerede values are: Windows, Linux.</p> <p>F: Optimization algorithm.<br /> Considered values are: heuristic (which is OptiSpot), fmincon.</p> <p>G: Experiment seed.<br /> Considered values are from 1 to 30</p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
A phylogenomics resource for the marine diatom Pseudo-nitzschia multistriata
<p>The resource contains phylogenetic trees built from approximately 9000 <em>P. multistriata </em>genes, comparing them to their orthologs (over 2 million sequences) across major taxa of archaea, bacteria and eukaryotes (Basu et al., 2017). For each orthologous group a tree is built twice using two different substitution models. Each sequence within a tree is given a specific ID which is a combination of a unique number along with a taxonomy code, for example "alla_stramenopile|34" signifies </p> <p> </p> <p>1) alla: organism Albugo laibachii.</p> <p>2) stramenopile: broad taxonomic class.</p> <p>3) 34: protein ID for Albugo laibachii DNA topoisomerase 2.</p> <p> </p> <p>The mapping between <em>Pseudo-nitzschia multistriata</em> proteins and the phylogenetic trees is present in the file "p.multistriata_gene_tree.txt". The detailed description of each protein ID given in any phylogenetic tree is present in the file "geneDB.txt". The phylogenetic trees generated using JTT and WAG substitution models of the FastTree program as present in the folders "treeJTT", "treeWAG".</p>
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection
<p><strong>NeSy4VRD</strong></p> <p>NeSy4VRD is a multifaceted, multipurpose resource designed to foster neurosymbolic AI (NeSy) research, particularly NeSy research using Semantic Web technologies such as OWL ontologies, OWL-based knowledge graphs and OWL-based reasoning as symbolic components. The NeSy4VRD research resource pertains to the <em>computer vision</em> field of AI and, within that field, to the application tasks of <em>visual relationship detection (VRD) and scene graph generation</em>.</p> <p>Whilst the core motivation of the NeSy4VRD research resource is to foster computer vision-based NeSy research using Semantic Web technologies such as OWL ontologies and OWL-based knowledge graphs, AI researchers can readily use NeSy4VRD to either: 1) pursue computer vision-based NeSy research without involving Semantic Web technologies as symbolic components, or 2) pursue computer vision research without NeSy (i.e. pursue research that focuses purely on deep learning alone, without involving symbolic components of any kind). This is the sense in which we describe NeSy4VRD as being <em>multipurpose</em>: it can readily be used by diverse groups of computer vision-based AI researchers with diverse interests and objectives.</p> <p>The NeSy4VRD research resource in its entirety is distributed across two locations: Zenodo and GitHub.</p> <p> </p> <p><strong>NeSy4VRD on Zenodo: the NeSy4VRD dataset package</strong></p> <p>This entry on Zenodo hosts the <em>NeSy4VRD dataset package</em>, which includes the <em>NeSy4VRD dataset</em> and its companion <em>NeSy4VRD ontology</em>, an OWL ontology called VRD-World.</p> <p>The <em>NeSy4VRD dataset</em> consists of an image dataset with associated visual relationship annotations. The images of the <em>NeSy4VRD dataset</em> are the same as those that were once publicly available as part of the <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">VRD</a> dataset. The NeSy4VRD visual relationship annotations are a highly customised and quality-improved version of the original VRD visual relationship annotations. The <em>NeSy4VRD dataset</em> is designed for computer vision-based research that involves detecting objects in images and predicting relationships between ordered pairs of those objects. A visual relationship for an image of the <em>NeSy4VRD dataset</em> has the form <'subject', 'predicate', 'object'>, where the 'subject' and 'object' are two objects in the image, and the 'predicate' describes some relation between them. Both the 'subject' and 'object' objects are specified in terms of bounding boxes and object classes. For example, representative annotated visual relationships are <'person', 'ride', 'horse'>, <'hat', 'on', 'teddy bear'> and <'cat', 'under', 'pillow'>.</p> <p>Visual relationship detection is pursued as a computer vision application task in its own right, and as a building block capability for the broader application task of scene graph generation. Scene graph generation, in turn, is commonly used as a precursor to a variety of enriched, downstream visual understanding and reasoning application tasks, such as image captioning, visual question answering, image retrieval, image generation and multimedia event processing.</p> <p>The <em>NeSy4VRD ontology</em>, VRD-World, is a rich, well-aligned, companion OWL ontology engineered specifically for use with the <em>NeSy4VRD dataset.</em> It directly describes the domain of the <em>NeSy4VRD dataset</em>, as reflected in the NeSy4VRD visual relationship annotations. More specifically, all of the object classes that feature in the NeSy4VRD visual relationship annotations have corresponding classes within the VRD-World OWL class hierarchy, and all of the predicates that feature in the NeSy4VRD visual relationship annotations have corresponding properties within the VRD-World OWL object property hierarchy. The rich structure of the VRD-World class hierarchy and the rich characteristics and relationships of the VRD-World object properties together give the VRD-World OWL ontology rich inference semantics. These provide ample opportunity for OWL reasoning to be meaningfully exercised and exploited in NeSy research that uses OWL ontologies and OWL-based knowledge graphs as symbolic components. There is also ample potential for NeSy researchers to explore supplementing the OWL reasoning capabilities afforded by the VRD-World ontology with Datalog rules and reasoning.</p> <p>Use of the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset </em>is, of course, purely optional, however. Computer vision AI researchers who have no interest in NeSy, or NeSy researchers who have no interest in OWL ontologies and OWL-based knowledge graphs, can ignore the <em>NeSy4VRD ontology</em> and use the <em>NeSy4VRD dataset </em>by itself.</p> <p>All computer vision-based AI research user groups can, if they wish, also avail themselves of the other components of the NeSy4VRD research resource available on GitHub.</p> <p> </p> <p><strong>NeSy4VRD on GitHub: open source infrastructure supporting extensibility, and sample code</strong></p> <p>The NeSy4VRD research resource incorporates additional components that are companions to the <em>NeSy4VRD dataset package</em> here on Zenodo. These companion components are available at <a href="https://github.com/djherron/NeSy4VRD/">NeSy4VRD on GitHub</a>. These companion components consist of:</p> <ul> <li>comprehensive open source Python-based infrastructure supporting the extensibility of the NeSy4VRD visual relationship annotations (and, thereby, the extensibility of the <em>NeSy4VRD ontology</em>, VRD-World, as well)</li> <li>open source Python sample code showing how one can work with the NeSy4VRD visual relationship annotations in conjunction with the <em>NeSy4VRD ontology</em>, VRD-World, and RDF knowledge graphs.</li> </ul> <p>The NeSy4VRD infrastructure supporting extensibility consists of:</p> <ul> <li>open source Python code for conducting deep and comprehensive analyses of the <em>NeSy4VRD dataset</em> (the VRD images and their associated NeSy4VRD visual relationship annotations)</li> <li>an open source, custom-designed <em>NeSy4VRD protocol</em> for specifying visual relationship annotation customisation instructions declaratively, in text files</li> <li>an open source, custom-designed <em>NeSy4VRD workflow, </em>implemented using Python scripts and modules, for applying small or large volumes of customisations or extensions to the NeSy4VRD visual relationship annotations in a configurable, managed, automated and repeatable process.</li> </ul> <p>The purpose behind providing comprehensive infrastructure to support extensibility of the NeSy4VRD visual relationship annotations is to make it easy for researchers to take the <em>NeSy4VRD dataset</em> in new directions, by further enriching the annotations, or by tailoring them to introduce new or more data conditions that better suit their particular research needs and interests. The option to use the NeSy4VRD extensibility infrastructure in this way applies equally well to each of the diverse potential NeSy4VRD user groups already mentioned.</p> <p>The NeSy4VRD extensibility infrastructure, however, may be of particular interest to NeSy researchers interested in using the <em>NeSy4VRD ontology</em>, VRD-World, in conjunction with the <em>NeSy4VRD dataset. </em>These researchers can of course tailor the VRD-World ontology if they wish without needing to modify or extend the NeSy4VRD visual relationship annotations in any way. But their degrees of freedom for doing so will be limited by the need to maintain alignment with the NeSy4VRD visual relationship annotations and the particular set of object classes and predicates to which they refer. If NeSy researchers want full freedom to tailor the VRD-World ontology, they may well need to tailor the NeSy4VRD visual relationship annotations first, in order that alignment be maintained.</p> <p>To illustrate our point, and to illustrate our vision of how the NeSy4VRD extensibility infrastructure can be used, let us consider a simple example. It is common in computer vision to distinguish between <em>thing</em> objects (that have well-defined shapes) and <em>stuff</em> objects (that are amorphous). Suppose a researcher wishes to have a greater number of <em>stuff</em> object classes with which to work. Water is such a <em>stuff</em> object. Many VRD images contain water but it is not currently one of the annotated object classes and hence is never referenced in any visual relationship annotations. So adding a <em>Water</em> class to the class hierarchy of the VRD-World ontology would be pointless because it would never acquire any instances (because an object detector would never detect any). However, our hypothetical researcher could choose to do the following:</p> <ul> <li>use the analysis functionality of the NeSy4VRD extensibility infrastructure to find images containing water (by, say, searching for images whose visual relationships refer to object classes such as 'boat', 'surfboard', 'sand', 'umbrella', etc.);</li> <li>use free image analysis software (such as GIMP, at gimp.org) to get bounding boxes for instances of water in these images;</li> <li>use the <em>NeSy4VRD protocol</em> to specify new visual relationships for these images that refer to the new 'water' objects (e.g. <'boat', 'on', 'water'>);</li> <li>use the <em>NeSy4VRD workflow</em> to introduce the new object class 'water' and to apply the specified new visual relationships to the sets of annotations for the affected images;</li> <li>introduce class Water to the class hierarchy of the VRD-World ontology (using, say, the free Protege ontology editor);</li> <li>continue experimenting, now with the added benefit of the additional <em>stuff</em> object class 'water';</li> <li>contribute the enriched set of NeSy4VRD visual relationship annotations, and the enriched companion VRD-World ontology, to research communities.</li> </ul> <p> </p> <p><strong>Information pertaining to the VRD dataset</strong></p> <p>Information about the original VRD dataset is available <a href="https://cs.stanford.edu/people/ranjaykrishna/vrd/">here</a>. </p> <p>Public availability of the VRD images (via information accessible from that location) ceased sometime in the latter part of 2021. We thank Dr. Ranjay Krishna, one of the principals associated with the VRD dataset, for granting us permission to re-establish the public availability of the VRD images as part of NeSy4VRD.</p> <p>The original VRD visual relationship annotations are still publicly available from that location. But our deep analysis of those annotations, driven by our desire to design a robust companion ontology, revealed them to be highly problematic in many ways that made credible ontology modelling infeasible. They were also found to be replete with all manner of errors. The NeSy4VRD visual relationship annotations are far superior and we recommend them over the original VRD annotations to anyone contemplating conducting research using the VRD images. The NeSy4VRD annotations also have the added benefit of the rich, well-aligned companion <em>NeSy4VRD ontology</em>, VRD-World, for those whose research requires such a companion ontology.</p> <p>Researchers wishing to use the original VRD dataset may still do so. They can access the VRD images here, from within the <em>NeSy4VRD dataset</em> on Zenodo, and access the VRD visual relationship annotations from the location in the link.</p> <p><em>A note of caution</em>: the <em>NeSy4VRD ontology</em>, VRD-World, is <em>not</em><strong> </strong>compatible with the original VRD visual relationship annotations and cannot be used in conjunction with them. The VRD-World ontology has been engineered in relation to the highly customised and quality-improved NeSy4VRD visual relationship annotations. The customisations that were applied include ones that introduced many new object classes, merged some of the existing object classes, introduced one new predicate, and changed several predicate names.</p> <p>However, researchers can, if they wish, use the NeSy4VRD extensibility infrastructure (described above) to undertake their own customisation and quality-improvement exercise with respect to the original VRD visual relationship annotations. This is precisely how the NeSy4VRD visual relationship annotations were created in the first place. The primary intended use case of NeSy4VRD's extensibility infrastructure, however, is for researchers to use the NeSy4VRD visual relationship annotations as their starting point, and to take these annotations forward with onward customisations and extensions, as illustrated in the example use case given above.</p> <p> </p> <p> </p>
Biodata Resource Inventory Dataset
<p>final_inventory_2022.csv is the result of the Biodata Resource Inventory conducted in 2022. data_dictionary.csv provides an explanation of the columns in the inventory file.</p>
Quality controlled observations of hourly incoming shortwave radiation data at the surface for solar resource mapping in Norway (2016-2020).
<p>Observed hourly incoming shortwave radiation data at the surface of Norway for the years 2016-2020 along with quality control flags, visualization plots and a descriptive report. The data has been collected, visually inspected and quality controlled within the SunPoint project (SUn in Norway - POtential and INTegration of the solar energy resource, Norwegian Research Council project 320750). The main data source is frost.met.no but some gaps were filled with data directly obtained by the station holders.</p> <p>There are three NetDCF files for 47 stations selected after quality control:</p> <ul> <li>rsds_1hr_selection_v5_2016-2020.nc: Raw data</li> <li>rsds_flagged_1hr_selection_v5_2016-2020.nc: Raw data with flags</li> <li>rsds_cleaned_1hr_selection_v5_2016-2020.nc: Filtered data (i.e. all flagged data has been removed)</li> </ul> <p>and one NetCDF file for all available stations (106 stations)</p> <ul> <li>rsds_1hr_frost_and_more_2016-2020.nc</li> </ul> <p>Version 3.5 of the McClear clear-sky model is used for flagging which reduces the bias to ground measurements compared to earlier versions. </p> <p>The visualization and automated quality control routines are available in the Scripts.zip file (python).</p>
Data from: Solar energy resource availability under extreme and historical wildfire smoke conditions
<p>The data in this repository are used to generate the figures in the article "Solar energy resource availability under extreme and historical wildfire smoke conditions" by Corwin et al. (accepted 2024) in <em>Nature Communications</em>. Data are the final processessed and merged datasets sourced from the following publicly available data products:</p> <ul> <li>National Renewable Energy Laboratory’s (NREL) National Solar Radiation Database (NSRDB) (<a href="https://nsrdb.nrel.gov/)">https://nsrdb.nrel.gov/)</a>. <ul> <li>Bulk download in July 2023 via AWS: <a href="https://registry.opendata.aws/nrel-pds-nsrdb/">https://registry.opendata.aws/nrel-pds-nsrdb/</a></li> <li>Variables: modeled irradiance (clear-sky and all-sky direct normal (DNI) and global horizontal (GHI) irradiance, aerosol optical depth, and cloud optical depth</li> </ul> </li> <li>National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product. <ul> <li>Access: <a href="https://www.ospo.noaa.gov/Products/land/hms.html#maps">https://www.ospo.noaa.gov/Products/land/hms.html#maps</a></li> <li>Variables: smoke plume locations</li> </ul> </li> <li>National Aeronautics and Space Administration's (NASA) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product (MCD19A2 MODIS/Terra + Aqua land aerosol optical depth daily L2G Global 1km SIN Grid V006). <ul> <li>Access: <a href="https://lpdaac.usgs.gov/products/mcd19a2v006/">https://lpdaac.usgs.gov/products/mcd19a2v006/</a></li> <li>Variables: aerosol optical depth and cloud mask</li> </ul> </li> <li>NASA's Clouds and the Earth’s Radiant Energy System (CERES) cloud data product (SYN1deg-1Hour Edition 4.1) <ul> <li>Access: <a href="https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp">https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp</a></li> <li>Variables: cloud optical depth</li> </ul> </li> </ul> <p>A detailed description of the data processing methods used to produce the final merged data are available in the article by Corwin et al. </p> <p>Associated code scripts are located in the linked code repository.</p>
Resources for Mitigating Chemotherapy Side Effects through Targeted Gamma-Ray Delivery and CNNs
<p>This repository includes datasets and code used in the study "Mitigating Chemotherapy Side Effects through Targeted Gamma-Ray Delivery and Convolutional Neural Networks." The resources comprise:<br>- Binding Affinity Data: Used for simulations.<br>- Brain Tumor MRI and Chest CT Scan Datasets: Used for model training.<br>- Lightweight Deep CNN: Code for building and testing models.</p>
PUDL Raw GridPath Resource Adequacy Toolkit Data
<p>Hourly renewable generation profiles compiled for the Western United States as part of the <a href="https://gridlab.org/gridpathratoolkit/">GridPath Resource Adequacy Toolkit</a>.</p> <p>Profiles are stated as a capacity factor (a fraction of nameplate capacity). There are 3 different levels of processing or aggregation provided, all at hourly resolution: Individual plant (wind) or generator (solar) output, capacity-weighted averages of wind and solar output aggregated to the level of balancing authority territories (or transmission zones for larger balancing authorities), and that same aggregated output but with some problematic individual generator profiles modified such that they match the overall production curve of the balancing authority they are within. This data also contains some daily weather data from several sites across the western US and tables describing the way in which individual wind and solar projects were aggregated up to the level of balancing authority or transmission zone.</p>
New Version: Resources for BMF CP 78 and 79 (RV2)
<p>These are the resources for completing BMF CP 78 and 79. This RV2 version contains revised R script. </p>
Gene tagging and gene deletion resources for Leishmania mexicana MNYC/BZ/62/M379 Cas9/T7 strain
<p><em>primers_barcodes.csv</em>: List of primer sequences necessary for N and C terminus gene tagging, as well as gene deletion in the Leishmania mexicana MNYC/BZ/62/M379 Cas9/T7 strain. Each row contains the gene name and the DF (downstream forward), DR (downstream reverse), DSG (downstream guide sRNA), UF (upstream forward), UFB (upstream forward including a gene-unique 17nt barcode sequence), UR (upstream reverse), USG (upstream guide sRNA), VF (verification forward) and VR (verification reverse) primer sequences. Empty cells indicate that it was not possible to design this primer for this gene. Guide sRNA perfect match and off-target counts are included as well. The primer sequences were designed using LeishGEdit (http://www.leishgedit.net). Barcode sequences and assigned IDs for the unique identification of knock-out or tagged strains are included as separate columns. For recommended methods for endogenous tagging or gene deletion see Beneke <em>et al., </em>R. Soc. Open Sci.4170095 (2017), for generating barcoded deletion mutants see Beneke and Gluenz, Mol. Biochem. Parasitol. 239 (2020).</p> <p><em>genome.gff</em>: Annotated genome of the <em>L. mexicana</em> MNYC/BZ/62/M379 strain, genetically modified to express T7 RNA polymerase and Cas9. The annotation is provided in a combined GFF3 / FASTA format that also includes the sequences of the chromosomes and small contigs. The annotation also specifies polyadenylation sites (PAS features) and splice leader acceptor sites (SLAS features) which were used to refine the boundaries of protein-coding sequences as well as 3' and 5' untranslated regions over the reference genome of <em>L. mexicana</em> MNYC/BZ/62/M379<em>.</em></p> <p><em>c9t7_sequences.fasta</em>: Raw chromosome and contig sequences in FASTA format.</p> <p><em>c9t7_transcripts.fasta</em>: mRNA transcript sequences in FASTA format (includes 5' and 3' UTRs).</p> <p><em>c9t7_transcript_CDSs.fasta</em>: Coding sequences in FASTA format.</p> <p><em>c9t7_predicted_protein_sequences.fasta</em>: Predicted protein amino acid sequences in FASTA format.</p> <p>Note: This version provides an update for <em>c9t7_transcript_CDSs.fasta, c9t7_predicted_protein_sequences.fasta</em> and <em>genome.gff</em>, correcting an off-by-one sequence coordinate in 48 of the the protein-coding genes.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.