Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,359
datasets available to search
ShareScore release 0.9.0
Dataset results
2,359 results for “Online”
F I G U R E 8 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 8 Reconstructed examples of classifieds listings with vague descriptions and poor-quality images.
F I G U R E 6 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 6 (a) Total number of invertebrate listings per State on online pet stores. (b) The classes of invertebrate each listing features. Y axis are log10 transformed to display less abundant classes.
F I G U R E 1 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 1 Species accumulation curve for invertebrate listings collected from the public classifieds website, representing the total cumulative number of species identified over time as listings were collected from the website.
F I G U R E 4 The top 10 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 4 The top 10 most popular invertebrate species in online pet stores, and those that also feature in the top 10 most popular species on the Australian classifieds website. Note that Urodacus elongatus (Flinders Ranges scorpion) has the same relative abundance in online pet stores as Exatosoma tiaratum (giant prickly stick insect), the overall most traded species online.
F I G U R E 7 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 7 (a) Histogram of mean species price-per-unit differences (AUD) between classifieds and online pet stores (classifieds price minus pet store price, i.e., negative price difference indicates that classifieds species are less expensive). Median value of 7.442 is represented by the red line. (b) Examples of species with large price differences on the two e-commerce platforms. Photo credits: (a) Rosie Steinberg, (b) Ajay Narendra, (c) Stephan Höhne.
F I G U R E 3 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 3 Bar graph of the top 10 most popular invertebrate species on the classifieds website by proportion of total listings, and those that also feature in the top 10 most popular species in online pet stores.
F I G U R E 2 in Untangling the web: dynamics of Australia s online terrestrial invertebrate trade
F I G U R E 2 (a) Total number of invertebrate orders traded on the classifieds website. (b) Total number of species within each invertebrate order traded on the classifieds website.
BioPropaPhenKG on Online Newspapers and Medical Articles
<p><br>The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured information to detect signs of possible public health events' progression. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this datasets presents a novel approach. We propose the use of BioPropaPhenKG to replace such systems. In this dataset, BioPropaPhen was enhanced with information comming from unstructured texts from online newspapers and medical articles. BioPropaPhenKG, its ontology and other useful information can be found in <a href="../records/10911980">https://zenodo.org/records/10911980</a>. The code used for this use case can be found in <a href="https://github.com/Gabriel382/DDPF-Health-Risks">https://github.com/Gabriel382/DDPF-Health-Risks</a> . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, <a href="https://aylien.com/blog/free-coronavirus-news-dataset">Aylien</a> (data only from November of 2019) and <a href="https://allenai.org/data/cord-19">CORD-19</a> (data only from December of 2019). </p> <p> </p> <p>To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command: </p> <p>docker run --interactive --tty --rm \<br> --publish=7474:7474 --publish=7687:7687 \<br> --volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\<br> neo4j:4.4.3 \<br>neo4j-admin load --from=/data/BioPropaPhenKG-Journal-Medical.dump --database "neo4j" --force</p> <p> </p>
Psicologia Online O Avanço da Terapia a Distância
<p>A psicologia online, também conhecida como terapia online ou e-terapia, é uma modalidade de atendimento psicológico que vem ganhando cada vez mais destaque e aceitação. Com o avanço da tecnologia e a crescente conectividade, as pessoas têm buscado alternativas mais acessíveis, convenientes e confortáveis para cuidar da saúde mental, e a psicologia online surge como uma resposta a essa demanda.</p>
Empty plots layer screenshot of the Kunbaja online resource
<p>Secondary information layer "empty plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
Magyar plots layer screenshot of the Kunbaja online resource with Ungar plots highlighted
<p>Secondary information layer "magyar plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format with Ungar plots highlighted</p>
Female plots layer screenshot of the Kunbaja online resource
<p>Secondary information layer "female plots" of the Kunbaja online resource model built in QGIS as screenhots in the .png file format</p>
BioPropaPhenKG on Multi-Relation Extraction Methods on Online Newspapers
<p>The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured data to detect signs of possible public health events. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this article presents a novel approach. We propose the biomedical domain specialization of the Core Propagation Phenomenon Ontology (PropaPhen) to capture spatiotemporal characteristics of the propagation of health-related phenomena. We also propose the Description-Detection-Framework (DDF), which leverages PropaPhen, UMLS, and OpenStreetMaps to detect new medical events automatically. Finally, we demonstrate a use case with experiments on extracts from online newspapers about COVID-19. The results show that DDF can be useful for detecting clusters of suspicious cases of possible emerging health-related phenomena.</p> <p>BioPropaPhenKG, its ontology and other useful information can be found in <a href="../records/10911980">https://zenodo.org/records/10911980</a>. The code used for this use case can be found in <a href="https://github.com/Gabriel382/DDPF-Health-Risks">https://github.com/Gabriel382/DDPF-Health-Risks</a> . Finally, the datasets used where UMLS MetamorphoSys, OpenStreetMaps, Wikidata, <a href="https://aylien.com/blog/free-coronavirus-news-dataset">Aylien</a> (data only from November of 2019).</p> <p> </p> <p>To read, you just need to load it with Neo4j:4.4.3. Alternatively, you can open it with docker using the following command: </p> <p>docker run --interactive --tty --rm \<br> --publish=7474:7474 --publish=7687:7687 \<br> --volume=/path-to-data-folder:/data --user="$(id -u):$(id -g)"\<br> neo4j:4.4.3 \<br>neo4j-admin load --from=/data/BioPropaPhenKG-Journal-MultiRE.dump --database "neo4j" --force</p>
TAXODIS - A Taxonomy of Online Disinformation
<p>The <strong>Taxonomy of Online Disinformation (TAXODIS)</strong> structures a set of well-defined and analysed linguistic features of online disinformation discourse and is meant to help annotate training data to nourish machine learning and computational models.</p> <p>TAXODIS has been implemented as a <strong>SKOS</strong> vocabulary/thesaurus and is available as an RDFS resource (in TTL/Turtle format) at: </p> <p><a href="https://hop.fzi.de/wordpress/wp-content/uploads/taxodis/v1.0/TAXODIS.ttl">https://hop.fzi.de/wordpress/wp-content/uploads/taxodis/v1.0/TAXODIS.ttl</a></p> <p>The (resolvable) namespace of the taxonomy is <a title="TAXODIS taxonomy" href="https://hop.fzi.de/taxodis/">https://hop.fzi.de/taxodis/</a>.</p> <h3><strong>PURPOSE</strong></h3> <p>The taxonomy can be used for the fine-grained annotation of disinformation datasets and for the publication of the annotation results as Linked (Open) Data. It can be used by automated detection systems to train classification models.</p> <h3><strong>STRUCTURE</strong></h3> <p>The taxonomy contains 66 concepts organized in a hierarchical structure. Its top concept is <em>"disinformation characteristic"</em>, which describes characteristics that are indicative of disinformation in a piece of content. This top term has three narrower terms: </p> <ul> <li><em>"detection feature"</em>, which classifies the piece of content based on linguistic or stylistic features that are indicative of the detection of disinformation (e.g. length of the headline, lexical and contentual poorness, level of semantic incoherence, lack of new information, level of topicality, etc.),</li> <li><em>"categorization"</em>, which classifies the piece of content based on its theme or content type. e.g. social (theme), conspiracy theory (content type), and </li> <li><em>"veracity"</em>, which classifies the piece of content based on its veracity, e.g. mostly false, mixture, etc. </li> </ul> <p>The full hierarchy is shown in <a href="http://users.ics.forth.gr/~fafalios/taxodis/taxodis.jpg" target="_blank" rel="noopener">this figure</a>.</p> <h3><strong>USAGE</strong></h3> <p>The taxonomy can be used together with existing, established vocabularies for the annotation of (disinformation) resources. We suggest the exploitation of the <a href="https://www.w3.org/TR/annotation-model/" target="_blank" rel="noopener">Web Annotation Data Model</a>; a W3C recommendation for the structured representation of annotations that can be shared and reused across different platforms. </p> <p><a href="http://users.ics.forth.gr/~fafalios/taxodis/taxodis_annotation_example.jpg" target="_blank" rel="noopener">This figure</a> shows an example in which an article (instance of class <em>oa:Target</em>) is linked to two annotations: one which categorises the article as of social theme (<em>taxodis:themsoc</em>) and one which categorises the article as having "high" topicality level (<em>taxodis:mobtopical</em>). The intension (motivation) of both annotations is classification (<em>oa:classifying</em>). Notice that the first annotation is directly linked to the taxonomy term <em>taxodis:themsoc</em> through multiple instantiation (the term is an instance of both <em>oa:Body</em> and <em>skos:Concept</em>). This annotation method can be applied for all taxonomy terms that are under <em>"categorisation"</em> and <em>"veracity"</em>, since these terms do not accept a degree value or level like the terms that are under <em>"detection feature"</em>.</p> <p><a href="http://users.ics.forth.gr/~fafalios/taxodis/taxodis_enrichment_example.jpg" target="_blank" rel="noopener">This figure</a> shows how we can link the annotated resource with rich (meta)data using another established vocabulary, namely <a href="https://schema.org/" target="_blank" rel="noopener">schema.org</a>. The annotated article is both an instance of <em>oa:Target</em> and an instance of <em>schema:CreativeWork</em>. This allows using properties of schema.org for providing more information about the article, such as its URL (instance of <em>schema:URL</em>), its publication date (instance of <em>schema:DateTime</em>), its headline (instance of <em>schema:Text</em>), its author (instance of <em>schema:Person</em>), and its content (instance of <em>schema:Text</em>). We can also link the article with entities of different types mentioned in it, such as persons, places, etc., using the property <em>schema:mentions</em>. <br>In addition, we can link claims (instances of <em>schema:Claim</em>) to the articles using the property <em>schema:appearance</em>. A claim can be then linked to its text, video/audio (if any) and author (using the properties <em>schema:text</em>, <em>schema:video</em>/<em>schema:audio</em>, and <em>schema:author</em>, respectively), as well as with claim reviews (instances of <em>schema:ClaimReview</em>). In a similar way, a claim review can be linked with related data such as its author, URL, publication date, headline, review body, etc.</p> <p>Queries that can be answered using TAXODIS annotations include:</p> <ul> <li>Retrieve all resources classified as of <em>social theme</em> and which have a <em>high</em> level of <em>emotional polarization</em></li> <li>Retrieve all resources with <em>imposter content</em> together with the values of all features that are under <em>"psychology feature"</em></li> <li>Retrieve the number of resources per content type having high usage of <em>emphatic wording</em></li> <li>Retrieve all resources published on a specific time period containing claims that have been reviewed and have received a veracity score <em>"mostly false"</em></li> <li>Retrieve all resources mentioning a specific person which are mostly <em>false</em>, together with the values of all features that are under <em>"detection feature"</em></li> </ul> <p>The first query of the above list is translated to SPARQL as follows:</p> <pre><code>PREFIX taxodis: <https://hop.fzi.de/taxodis/> PREFIX oa: <http://www.w3.org/ns/oa#> PREFIX schema: <http://schema.org/> SELECT ?resourceUri ?resourceHeadline ?resourceAuthor WHERE { ?annot1 oa:hasTarget ?resourceUri ; oa:hasBody taxodis:themsoc . ?annot2 oa:hasTarget ?resourceUri ; oa:hasBody ?annot2Body . ?annot2Body oa:type taxodis:mobpolar ; rdf:value "high" . OPTIONAL { ?resourceUri schema:headline ?resourceHeadline } OPTIONAL { ?resourceUri schema:author ?resourceAuthor } }</code></pre> <h3><strong>ANNOTATION PROTOCOL / GUIDELINES</strong></h3> <p>We do not provide specific annotation guidelines as of how to use the taxonomy, because such protocol is usually highly task and domain dependent. Thus, we leave to the specific community and teams the design of annotation guidelines according to their needs. It is worth noting that some taxonomy categories, such as <em>"emotional polarization"</em> versus <em>"sensationalism"</em>, exhibit conceptual proximity that may challenge consistent annotation. In such cases, multiple labels to a single content item can be assigned, even within the same dimension where categories are not strictly mutually exclusive. For example, content may be simultaneously labeled as both <em>"clickbait"</em> and <em>"propaganda"</em> when relevant. This multilabel approach reflects the complex and often overlapping nature of disinformation phenomena.</p> <p>In the context of the <a href="https://defakts.de/" target="_blank" rel="noopener">DeFaktS project</a>, a group of interested researchers (from sociology, computer science, and political science) and practitioners (from NGOs and industry) utilized an earlier version of the taxonomy to create labels for identifying different types of disinformation. The participants were provided with a <a href="http://users.ics.forth.gr/~fafalios/taxodis/Taxonomy_Workshop_Handout.pdf" target="_blank" rel="noopener">handout of the taxonomy</a> and applied it to social media posts derived from various platforms (mainly Telegram and X). This handout may also prove useful in similar manual annotation efforts.</p> <h3><strong>RELATED PUBLICATION</strong></h3> <p>(currently under review)</p>
Deceptive and Abusive Online Dialogs
<p>This corpus consists of annotated deceptive and abusive online dialogues that have been produced by the Chattack system. The Chattack system is a gamified crowd-sourcing platform for tagging deceptive and abusive online behaviour.</p>
A personalized value-based justification in food swaps to stimulate healthy online food choices
<p>This study examined the effect of a personalized value-based justification in explaining the rationale behind healthy food swaps. Additionally, consumers' willingness to share their personal information with retailers to personalize swap recommendations is explored.</p>
Which journal characteristics are crucial for scientists when selecting journals for their publications? Results tables of an online survey
<p>As part of the BMBF-funded project "B!SON - Bibliometric and Semantic Open Access Recommender Network", an online survey was conducted among scientists using SoSci Survey (Leiner, 2019). The aim of the survey was to determine the importance of various characteristics of scientific journals in the decision for a publication venue by scientists. The characteristics were determined by analysis of other recommender systems, literature research and discussion with scientists. The data published here are based on 884 completed questionnaires (only questionnaires in which at least 90% of the questions had been answered were included in the analysis).</p> <p>In the questionnaire, a distinction was made between those characteristics that scientists would like to use to limit the selection of eligible journals from the outset (table "B!SON_Survey_Filter_Criteria_EN") and those that scientists need for their final decision from a list of recommended journals ("B!SON_Survey_Journal_Selection_EN"). For each journal property, the respondents could choose between the categories of a 5-point Likert scale: "not at all important - not very important - somewhat important - very important - extremely important". If the scientists were not able to evaluate a characteristic, they could also select "I can't say". </p> <p>In the two tables of results, the approval percentage and the rank based on it are listed for all journal properties queried in the respective part of the survey. The approval percentage is calculated from the percentage of people who rated the respective property as "very important" or "extremely important". Approval rate and rank are presented across all respondents (overall column), as well as within the 4 science disciplines by DFG (Natural Sciences, Engineering Sciences, Life Sciences, Humanities and Social Sciences) and the category "Other Sciences".</p> <p>Both tables can be sorted by approval rate and rank per scientific discipline or across disciplines.</p> <p>Note on the use of the HTML files: These can be downloaded via the download button and then opened and viewed with any web browser.</p> <p>A German version of the results tables is available under <a href="https://doi.org/10.5281/zenodo.5412197">https://doi.org/10.5281/zenodo.5412197</a>.</p>
Online Appendix and Cetacean Datasets for: The Occurrence Birth-Death Process for combined-evidence analysis in macroevolution and epidemiology
<p>Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, i.e. lacking morphological and molecular data, which we call occurrences, provide invaluable information to reconstruct the past number of lineages.</p> <p>Here, we present new methodological developments around the Fossilized Birth-Death Process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death and sampling rates; and (iii) reconstruct the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions.</p> <p>We finally illustrate our new implementation using two empirical datasets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the COVID-19 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics.</p>
FlyWire: Online community for whole-brain connectomics
<p>A ground truth dataset for 3D neuron reconstruction from electron microscopy (EM) images of the fly whole-brain, created for our project FlyWire: A human-AI collaboration to map the fly connectome. For more information, please visit <a href="https://flywire.ai/">https://flywire.ai/</a>.</p> <p> </p> <p><strong>Citation</strong></p> <p><em>FlyWire: Online community for whole-brain connectomics</em><br> Dorkenwald et al.<br> bioRxiv 2020.08.30.274225; doi: https://doi.org/10.1101/2020.08.30.274225</p> <p> </p> <p><strong>Dataset description</strong></p> <ul> <li><strong>cremi_realigned.tar.gz</strong> <ul> <li>cremi_{a,b,c}_realigned.h5: re-aligned CREMI volumes (<a href="https://cremi.org/">https://cremi.org/</a>)</li> <li>cremi_b_realigned_new.h5: re-aligned CREMI B volume with de-novo annotation</li> </ul> </li> <li><strong>focused.tar.gz</strong> <ul> <li>A set of densely annotated volumes covering diverse structures in the fly brain that are underrepresented in the CREMI volumes.</li> </ul> </li> <li><strong>sparse.tar.gz</strong> <ul> <li>A set of sparsely annotated volumes covering tricky failure modes in the initial segmentation attempt. Here intracellular structures are often oversegmented conservatively to prevent merge errors at the expense of introducing some split errors. </li> </ul> </li> <li><strong>glia.tar.gz</strong> <ul> <li>Semi-automatically generated ground truth for glia detction. A set of subvolumes from the FlyWire segmentation was sampled from which human experts classified automatically generated segments into either neurons or glia, while existing merge errors were excluded from annotation.</li> </ul> </li> </ul>
Final project report: The Side Effects of Forced Online Distance Education (FODE)
<p>The outbreak of COVID-19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID-19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE). The aim of the project, was to investigate the response of university teachers and students to the new situation.</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.