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13,499 results for “researcher”
Smartphone datasets for Research
<p>The dataset of smartphone is downloaded from Kaggle. We do preprocessing and elimination of waste data that is not needed in the research. The dataset is in CSV file.</p>
MongoDB database dump for the analysis of the current sustainability state of research software
<p>This data set is the MongoDB dump (bson files) of the data created and analyzed with the rsps framework. In the first step, a research subject is assigned to the research software repositories. Afterwards, the current sustainability state is evaluated. The data set comprises the following six bson files:</p> <p><strong>repositories: </strong>metadata, received from the GitHub REST API, for repositories containing the search terms "doi+10" or "doi+10+in:readme", additional information are the request date, the contained search term, and the repository hosting service, in this case for all repositories "github". For repositories the Readme files are available.</p> <p><strong>publications:</strong> metadata of publications, published on arXiv and ACM, that contain the search term "github.com".</p> <p><strong>rs_repositories:</strong> research software candidates containing a DOI or that are referenced by the publications contained in the publications data set.</p> <p><strong>rs_artifacts: </strong>research software artifacts that are referenced in the harvested GitHub repositories by a DOI and the harvested publications.</p> <p><strong>publication_subjects:</strong> All Science Journal Classification (ASJC) of Scopus combined with the Scopus source list and Scopus book title list (https://www.scopus.com/home.uri)</p> <p><strong>arxiv_subjects:</strong> arXiv taxonomy complemented with the ASJC research subject.</p> <p> </p>
Datasets and Supporting Materials for the MALIN-ANR 2019 Competition (French national research agency)
<p>This "ZENODO deposit" provides a multiple sensor dataset collected by the CyborgLOC team during the intermediate competition of the Challenge MALIN (<em>MA</em><em>îtrise</em><em> de la </em><em>L</em><em>ocalisation </em><em>IN</em><em>door</em>), which is a competition for indoor/outdoor real-time positioning. The sensors, including a GNSS receiver Ublox NEO-M8N, a Realsense D435i stereo camera, three Xsens MTi-300 and one PERSY (<strong>PE</strong>destrian <strong>R</strong>eference <strong>SY</strong>stem), are mounted on different parts of the subject’s body. The PERSY is a foot-mounted positioning device with a tri-axial accelerometer, a tri-axial gyroscope, a tri-axial magnetometer as well as a GNSS receiver Ublox M8T. The two scenarios are designed in a training center of firefighters CFIS (Fire and Rescue Training Center) in Blois, France to simulate the situation of firefighters during interventions. With total distances around 2 km for each scenario, the travelled trajectories passed through challenging environments including indoor, outdoor, urban canyon. The indoor part contains different stair levels, from the underground up to the 6th floor. The travel modes are vehicles and pedestrians. Several classical activities of firefighters are realized such as walking, running, stair-climbing, side-walking, crawling, passing above/below obstacles, carrying a stretcher, ladder climbing, etc. High accurate ground truth of stationary points and enclosing volumes are provided by the organizers of the competition, i.e., the French Ministry of Defense (DGA: Direction Générale de l’Armement). Provided with raw data, they allow the evaluation of the positioning performances.</p> <p>To facilitate the use of our dataset under Rosbag format, a toolkit of python scripts named <em>MALIN Data Processing Tools</em> is provided on GitHub (<a href="https://github.com/4g-group/malin_data_processing_tools">https://github.com/4g-group/malin_data_processing_tools</a>). It allows merging Rosbags, converting Rosbag files to CSV files as well as republishing camera’s topics as decompressed data. Details about these processing tools could be found in the Readme file on the Github page. </p>
Near surface softening and healing in eastern Honshu associated with the 2011 Tohoku-Oki Earthquake: Research data and code
<p>The zip file named 'mainshock.zip' contains the Matlab codes and seismic data for reproducing the results of Figure 2.</p> <p>The Excel file named 'Source Data.xls' contains the raw data of Figures 3 and 4.</p>
FIG. 9 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 9. — Sample size and sorting process according to the sampling type and impact on the quantity and availability of specimens for taxonomic study. Process for broad-spectrum traps: A, automatic light trap with blue LED; B, yield of the trap after one week; C, conditioning of the sample in the field laboratory, and storage in WhirlPack bags with alcohol; D-F, sorting specimens by order and family at the SEAG laboratory (Montjoly, French Guiana); G, preparing packages with glassine envelopes and Eppendorf vials for dissemination among coordinators and/or taxonomic experts; H, typical output of this kind of broad-spectrum trap samples: about 50% fraction may finally be studied (arbitrary estimate). Photos: Julien Touroult.
FIG. 10 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 10. — Process for active collecting: A, active collection of cricket (Orthoptera); B, photography of live specimens, important part of the process in some groups; C, preparation and management of the specimens for short term storage in the field laboratory; D, output of the active or selective methods: lower yields than broad-spectrum traps but a larger proportion is effectively studied. Photos: A, C, Xavier Desmier; B, Julien Touroult.
FIG. 12 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 12. —Database and portals for entry, consultation and access to data, illustrated by means of a newly described Mitaraka species, Eupromera pascali Dalens, 2016 (Coleoptera, Cerambycidae): A, CardObs database entry interface (https://cardobs.mnhn.fr). The morphospecies name was initially entered as "Eupromera n. sp." in April 2015 and after publication (Feb. 2016), the morphospecies name was replaced by the species name, and the record was completed with publication reference and the collection deposit number; B, INPN French Natural Heritage consultation portal, displaying this species from the Mitaraka dataset (https://inpn.mnhn.fr/espece/cd_nom/814643/tab/rep/GUF); C, public interface to database of the Coleoptera collection (EC) of the MNHN illustrating the holotype and its labels, with full traceability (http://coldb.mnhn.fr/catalognumber/mnhn/ ec/ec7591); D, International GBIF Data Portal displaying the Coleoptera collection (EC) dataset of the MNHN (https://www.gbif.org/occurrence/1413051340).
FIG. 8 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 8. — Active and passive substrate sampling: A, B, collection of dead branches infested with saproxylic larvae for "rearing" in emergence chambers (EXL); C, sampling soil litter for invertebrates with Winkler sieve (WS); D, spraying trunks with insecticide to collect small bark-dwelling arthropods that fall on the white sheet at the bottom of the trunk; E, searching for Annelida in soil samples collected with a spade; F, fish sampling in a small stream using rotenone. Photos: A, B, Stéphane Brûlé; C, Benoît Fontaine; D, Jürgen Schmidl; E, F, Xavier Desmier.
FIG. 7 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 7. — Active collecting techniques: A, collecting butterflies with a net; B, sweeping vegetation (NS or SW) with a rugged sweep net; C, beating tray (BS), the vegetation is hit with a stick, which causes the arthropods to fall on the white nape mounted on a frame; D, searching for aquatic larvae with a rugged aquatic net; E, looking for butterfly caterpillars (Riodinidae and Lycaenidae) on liana flowers; F, visual search for reptiles, here with a Lachesis muta (Linnaeus, 1766) snake. Photos: A, B, C, E, Stéphane Brûlé; D, Nicolas Moulin; F, Xavier Desmier.
FIG. 3 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 3. — Illustration of the landscape and main habitat types found in the Mitaraka study area: A, general landscape of the study area, with the drop zone visible in the foreground; B, inselberg "Sommet-en-Cloche" with bare rocks and transition forest; C, mosaic of forests and cambrouses; D, forest interior; E, swamp forest (bas-fond) with Euterpe oleracea Mart palm. Photos: Xavier Desmier, except B, Stéphane Brûlé.
FIG. 5 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 5. — Passive interception traps: A, windowpane flight intercept trap (FIT) suspended over a fallen tree crown; B, 6 meter Malaise trap (MT) set up over a fallen tree near the Alama river; C, SLAM traps on an inselberg forest edge; D, a buprestid beetle (Buprestidae) trapped in artificial spider web (ASW). Photos: A, B, D, Julien Touroult; C, Stéphane Brûlé.
FIG. 4 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 4. — Some of the collecting sites and techniques: A, drop zone forest clearing, with a high amount a freshly cut trees, and scattered SLAM traps; B, clearing, equiped with SLAM traps, automatic light trap and artificial spider web (ASW); C, active net collecting of butterflies on the "Sommet-en-Cloche" inselberg. Photos: A, B, Julien Touroult, C, Stéphane Brûlé.
FIG. 2 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 2. — Mitaraka study area map with the four trails indicated (map by Maël Dewynter, map base by IGN and Parc amazonien de Guyane).
FIG. 6 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 6. — Attractive traps: A, pink LED based automatic light trap (PVP) suspended at 15 m height close to a small canopy gap; B, light trap (LT) with light bulb of 125W and with white sheet, covered with moths at the end of a rainy night; C, colored pan traps (blue [BPT], white [WPT], and yellow [YPT]) at soil surface level to collect Diptera; D, fruit baited Coleoptera traps with banana nectar (BT), suspended in forest canopy; E, Nymphalidae butterfly trap (CHX), suspended in the forest canopy; F, tree equiped with ropes and baits composed of honey and tuna at different heights to attract ants; G, pitfall trap baited with dung (PFC) to collect coprophagous Scarabaeidae; H, Big Shot, a type of slingshot used to shoot ropes and suspend traps high up in the trees. Photos: A, B, G, H, Julien Touroult; C, Marc Pollet; F, Maurice Leponce; D, E, Stéphane Brûlé.
FIG. 11 in Overview of Mitaraka survey: research frame, study site and field protocols
FIG. 11. — Process flow for Diptera: A, each Diptera coordinator and taxonomic expert signs an agreement prior to receiving samples; B, sampling specimens with an array of methods (Malaise trap, pan traps, sweep net, SLAM trap); C, transporting of partly processed and unprocessed samples to the Belgian lab; D, sorting Diptera from complete samples and splitting the Diptera fraction into workable fractions (mostly on family level) for Diptera coordinators – taxonomic experts; E, processed Diptera fractions (Dolichopodidae, Empidoidea, Mycetophilidae, Phoridae); F, dissemination of workable fractions to Diptera coordinators – taxonomic specialists (10 in Europe, 5 in Canada, 8 in the USA, 10 in Brazil); G, examination and identification of specimens of workable fractions by the taxonomic expert (or further splitting of fractions by Diptera coordinator); H, commitments as part of the signed agreement (see Fig. 11A), with submission of identification file as first.
Data set 2 anonymized collection of data on BRAD research participants
<p>Database on research participants in the BRAD project. The Personal Data have been removed in order to make the identification of the research participants impossible. For Polish migrants in the UK, the database contains the information about the application to European Union Settlement Scheme.</p>
Data set 1 discourse analysis BRAD research project
<p>Discourse analysis data set with excerpts of press articles generated in the coding (coded with keywords ‘Brexit’ and ‘deportations’). This data set connects to the WP3 of the BRAD research project.</p>
Data set 3 photographic documentation BRAD research project
<p>Photographic documentation collected during the BRAD research project. For the personal data protection reasons, the published pictures do not represent recognizable people. The pictures present the places where part of the fieldwork was done (London, Croydon in the UK, Poznań in Poland). A separate folder contains images related to EUSS application.</p>
Softcite Dataset: A dataset of software mentions in research publications
<p>The Softcite dataset is a gold-standard dataset of software mentions in research publications, a free resource primarily for software entity recognition in scholarly text. This is the first release of this dataset.</p> <p><strong>What's in the dataset</strong></p> <p>With the aim of facilitating software entity recognition efforts at scale and eventually increased visibility of research software for the due credit of software contributions to scholarly research, a team of trained annotators from Howison Lab at the University of Texas at Austin annotated 4,093 software mentions in 4,971 open access research publications in biomedicine (from PubMed Central Open Access collection) and economics (from Unpaywall open access services). The annotated software mentions, along with their <em>publisher</em>, <em>version</em>, and access <em>URL</em>, if mentioned in the text, as well as those publications annotated as containing no software mentions, are all included in the released dataset as a TEI/XML corpus file.</p> <p>For understanding the schema of the Softcite corpus, its design considerations, and provenance, please refer to our paper included in this release (preprint version).</p> <p><strong>Use scenarios</strong></p> <p>The release of the Softcite dataset is intended to encourage researchers and stakeholders to make research software more visible in science, especially to academic databases and systems of information retrieval; and facilitate interoperability and collaboration among similar and relevant efforts in software entity recognition and building utilities for software information retrieval. This dataset can also be useful for researchers investigating software use in academic research.</p> <p><strong>Current release content</strong></p> <p><em>softcite-dataset v1.0</em><strong> </strong>release includes<strong>:</strong></p> <ul> <li>The Softcite dataset corpus file: softcite_corpus-full.tei.xml</li> <li><em>Softcite Dataset: A Dataset of Software Mentions in Biomedical and Economic Research Publications</em>, our paper that describes the design consideration and creation process of the dataset: Softcite_Dataset_Description_RC.pdf. (This is a preprint version of our forthcoming publication in the Journal of the Association for Information Science and Technology.)</li> </ul> <p>The Softcite dataset is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p>If you have questions, please start a discussion or issue in the <a href="https://github.com/howisonlab/softcite-dataset">howisonlab/softcite-dataset Github repository</a>.</p>
Underlying data - Results from the Open Call: How Citizens can participate in solar energy research?
<p>Underlying data to the "Results from the Open Call: How Citizens can participate in solar energy research?" @</p> <pre>https://zenodo.org/record/3554901#.YAgimxaCE2w</pre> <p>Answers to the online survey in "Call for ideas_answers online_survey.xlsx"</p> <p>Notes from the World Cafe and other meetings from the secretaries: "notes_MMLs_GRECO_2019.pdf</p> <p> </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.