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12,237 results for “2019”
ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents [HisIR19] Dataset
<p>This dataset contains the training and test set used in the ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents.</p> <p>This competition investigates the performance of large-scale retrieval of historical document images based on<br> writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing<br> a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval.</p> <p>The training data set encompasses images from (i) Letters A, where each writer contributed one or three images; (ii) Manuscripts, where each writer was represented by five consecutive images from a single book.<br> In total, it contains 300 writers contributing one page, 100 writers contributing three pages, and 120 writers contributing five pages resulting in 1200 images of 520 writers.</p> <p>The test data set contains 20 000 images: About 7 500 pages stem from isolated documents (partially anonymous writers, contributing one page each), and about 12 500 pages are from writers that contributed three or five pages.</p> <p> </p> <p>If you use this dataset, please cite:</p> <p>V. Christlein, A. Nicolaou, M. Seuret, D. Stutzmann, A. Maier: "ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents", in 15th International Conference on Document Analysis and Recognition, 2019, Sydney, Australia</p> <p> </p>
Performance of Cities in IDL 2019 Variables
<p>These pictures show the performance of cities in IDL 2019 variables. More about IDL see: https://idl.institutomongeralaegon.org/</p>
Performance of Brazilian Cities in IDL 2019
<p>These pictures show the performance of Brazilian cities in terms of IDL 2019. More about IDL: https://idl.institutomongeralaegon.org/</p>
MALDI-TOF MS spectra data included in Dumolin et al. 2019
<p>MALDI-TOF MS and genome assembly data used for benchmarking of the SPeDE dereplication program.</p> <p> </p>
CWTS Leiden Ranking 2019
<p>This data set provides the statistics made available in the 2019 edition of the CWTS Leiden Ranking. The Leiden Ranking 2019 is a bibliometric ranking of 963 major universities worldwide. The ranking provides statistics on scientific impact, collaboration, open access publishing, and gender diversity. The ranking is based on bibliographic data from the Web of Science database produced by Clarivate Analytics.</p>
Bug Injector Benchmarks for IEEE SCAM 2019
<p>We present the benchmark data-set accompanying our paper "Automated Customized Bug-Benchmark Generation", published in the 19th IEEE International Working Conference on Source Code Analysis and Manipulation. </p>
Webis Query-Task-Mapping Corpus 2019 (Webis-QTM-19)
<p>The Webis Query-Task-Mapping Corpus 2019 (Webis-QTM-19) comprises three benchmark datasets on the query-task-mapping problem, which consists of finding the correct task for a new query in a given task-split background query log.</p> <p>It comprises three subdatasets in separate CSV files, each of which has three columns:</p> <ul> <li>Query. The query string.</li> <li>Source. The source of the query. In all datasets, a source field with value 'google' or 'bing' indicates that the query was derived from query suggestions from the respective search engine; otherwise, the query is from one of the underlying base corpora: <ul> <li>'lucc' : lucchese:2011</li> <li>'webis' : stein:2013b</li> <li>'trc' : stein:2016a</li> <li>'trec' : various collections of TREC queries</li> <li>'wikihow' : based on titles of wikiHow questions</li> </ul> </li> <li>Task. The ID of the ground-truth task for the corresponding query.</li> </ul> <p><br> Further details can be found in reference:<br> Michael Völske, Ehsan Fatehifar, Benno Stein, and Matthias Hagen. Query-Task Mapping. In 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR 2019), July 2019. ACM.<br> http://doi.acm.org/10.1145/3331184.3331286</p>
Dataset for the article 蓝珲. (2019). 浙师大学生的英文能力能帮助《人工智能》课程学习吗?
<p>Data set for the following paper.</p> <p>蓝珲. (2019). 浙师大学生的英文能力能帮助《人工智能》课程学习吗?. http://doi.org/10.5281/zenodo.3341684</p>
Reproducibility package for the KDD 2019 paper "Pairwise Comparisons with Flexible Time-Dynamics"
<p>This archive contains the data necessary to reproduce most experiments and all plots presented in the following paper:</p> <p>Lucas Maystre, Victor Kristof, Matthias Grossglauser, <a href="https://arxiv.org/abs/1903.07746">Pairwise Comparisons with Flexible Time-dynamics</a>, KDD 2019.</p>
LIBER 2019 Workshop. Open Access books in academic libraries – how can we adapt workflows and cost management to an open scholarly communications landscape?
<p>This dataset includes all the results from a workshop held at the LIBER Annual Conference 2019 on June 26, 2019, in Dublin, Ireland. The workshop aimed at collecting and discussing current library practices related to open access books.</p> <p>The dataset includes information from a survey made in preparation for the conference, where 67 European libraries responded to a questionnaire based on activities or workflows in libraries related to open access books. Both the survey questionnaire and the results from the survey are uploaded as separate files.</p> <p>The dataset also includes the presentation made by keynote speaker Eelco Ferwerda from the OAPEN Foundation. He presented results based on the 2017 landscape study report on open access monographs with some new results from more recent studies by Springer Nature and a follow-up report by Knowledge Exchange.</p> <p>Olaf Siegert from ZBW - Leibniz Information Centre for Economics presented a brief overview of what libraries can do to promote OA books in terms of collection management, publication services and development of staff and organisation. The conclusion is that it is not necessarily big changes that are needed.</p> <p>Sofie Wennström presented results from a survey aimed at European research libraries on behalf of the LIBER Open Access Working Group. The survey reveals that many libraries are already working with processes to promote OA books. This is done by libraries organising publishing services or inhouse publishing, by including OA books in discovery services and repositories and by supporting authors to learn more about open access and open licensing.</p> <p>Finally, the LIBER Open Access Working Group shares a report from the workshop providing some quick takeaways and some good examples brought up during the breakout session with the workshop participants.</p>
Data set supporting journal article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019
<p>This data set contains evapotranspiration data obtained by a conventional eddy covariance set-up and a low-cost eddy covariance set-up as described in the research article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019.</p> <p>The data set contains all necessary data needed to replicate figures and analysis presented in the research article. The data sets are sorted and named according to the figure the data were used for. </p>
CTD data from the Weddell Sea Expedition 2019
<p>This data was collected from January to February 2019 during the Weddell Sea Expedition on board the R/V SA Agulhas II. The instrument used was a Sea-Bird SBE 9/11 plus CTD rosette.</p> <p>This data was collected under the South African National Antarctic Programme grant numbers 105539 and 110735 and the Weddell Sea Expedition funded by the Flotilla Foundation.</p>
Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment — IROS 2019
<p>This video accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
Robot view — Supplementary dataset of experiment videos, IROS 2019 — Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment
<p>This is a supplementary dataset of experiment videos of self-organized multi-robot fibre deployment. Each video is true speed and shows the full respective experiment. These videos show the <strong>robot view</strong> of each experiment.</p> <p><em>For a 2-minute summary video of these experiments, refer to:</em></p> <pre>https://doi.org/10.5281/zenodo.3357187</pre> <p>This supplementary dataset accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
alexlwhite/WhiteBoyntonYeatman2019_Repository: Public repository for White, Boynton & Yeatman (2019)
<p>This is the public repository of data and code for the study described in our 2019 Cortex article, "The link between reading ability and visual spatial attention across development."</p>
Fibre view — Supplementary dataset of experiment videos, IROS 2019 — Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment
<p>This is a supplementary dataset of experiment videos of self-organized multi-robot fibre deployment. Each video is true speed and shows the full respective experiment. These videos show the <strong>fibre view</strong> of each experiment.</p> <p><em>For a 2-minute summary video of these experiments, refer to:</em></p> <pre>https://doi.org/10.5281/zenodo.3357187</pre> <p>This supplementary dataset accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
TESS-APRIL-2019
<p>Measurements taken by the European Photometer Network (Project STARS4ALL). April 2019</p>
YSI Data from Chalgrove Lake; 2019-07-01 to 2019-07-26
<p># General Metadata for Chalgrove Lake YSI Data </p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> chalgrove_lake_DDMonthYYYY_metadata.txt<br> <br> Where DDMonthYYY is the date that the deployment ended.</p> <p>## File Created</p> <p> * 2019-04-12 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a YSI EXO2 multiparameter sonde deployed at approximatly 0.7 m in Chalgrove Lake (37.242983 N, -78.464116 W) in approximately 4 m of water.</p> <p>## Sonde Specifics</p> <p> The specific sensors and SNs deployed are on the metadata specific to each deployment but generally the sonde has:<br> <br> * Temperature<br> * Conductivity<br> * pH<br> * ODO<br> * Total Algae<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * date.time = the combined date and time of the measurement (YYYY-MM-DD HH:MM:SS)<br> * date = the sampling date reported by the YSI (M/D/YYYY)<br> * time = the sampling time reported by the YSI (HH:MM:SS)<br> * fract_sec = the smapling fractions of a second reported by the YSI<br> * site_name = the site name on the YSI deployment (left blank)<br> * cond = the conductivity (uS/cm)<br> * nLF_cond = the nLF conductivity (uS/cm)<br> * perc_ODO = the percent oxygen saturation<br> * perc_local_ODO = the percent local oxygen saturation<br> * ODO_conc = the dissolved oxygen concentration (mg/L)<br> * sal = the salinity (psu)<br> * sp_cond = the specific conductivity (uS/cm)<br> * TDS = the total dissolved solids (mg/L)<br> * pH = the pH<br> * pH mV = the millivolts of the pH sensor<br> * temp = the temperature (dC)<br> * battery = the battery voltage<br> * cable_pwr = the cable power<br> <br> </p>
YSI Data from Chalgrove Lake; 2019-06-12 to 2019-07-01
<p># General Metadata for Chalgrove Lake YSI Data </p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> chalgrove_lake_DDMonthYYYY_metadata.txt<br> <br> Where DDMonthYYY is the date that the deployment ended.</p> <p>## File Created</p> <p> * 2019-04-12 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a YSI EXO2 multiparameter sonde deployed at approximatly 0.7 m in Chalgrove Lake (37.242983 N, -78.464116 W) in approximately 4 m of water.</p> <p>## Sonde Specifics</p> <p> The specific sensors and SNs deployed are on the metadata specific to each deployment but generally the sonde has:<br> <br> * Temperature<br> * Conductivity<br> * pH<br> * ODO<br> * Total Algae<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * date.time = the combined date and time of the measurement (YYYY-MM-DD HH:MM:SS)<br> * date = the sampling date reported by the YSI (M/D/YYYY)<br> * time = the sampling time reported by the YSI (HH:MM:SS)<br> * fract_sec = the smapling fractions of a second reported by the YSI<br> * site_name = the site name on the YSI deployment (left blank)<br> * cond = the conductivity (uS/cm)<br> * nLF_cond = the nLF conductivity (uS/cm)<br> * perc_ODO = the percent oxygen saturation<br> * perc_local_ODO = the percent local oxygen saturation<br> * ODO_conc = the dissolved oxygen concentration (mg/L)<br> * sal = the salinity (psu)<br> * sp_cond = the specific conductivity (uS/cm)<br> * TDS = the total dissolved solids (mg/L)<br> * pH = the pH<br> * pH mV = the millivolts of the pH sensor<br> * temp = the temperature (dC)<br> * battery = the battery voltage<br> * cable_pwr = the cable power<br> <br> </p>
YSI Data from Chalgrove Lake; 2019-07-26 to 2019-08-08
<p># General Metadata for Chalgrove Lake YSI Data </p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> chalgrove_lake_DDMonthYYYY_metadata.txt<br> <br> Where DDMonthYYY is the date that the deployment ended.</p> <p>## File Created</p> <p> * 2019-04-12 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a YSI EXO2 multiparameter sonde deployed at approximatly 0.7 m in Chalgrove Lake (37.242983 N, -78.464116 W) in approximately 4 m of water.</p> <p>## Sonde Specifics</p> <p> The specific sensors and SNs deployed are on the metadata specific to each deployment but generally the sonde has:<br> <br> * Temperature<br> * Conductivity<br> * pH<br> * ODO<br> * Total Algae<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * date.time = the combined date and time of the measurement (YYYY-MM-DD HH:MM:SS)<br> * date = the sampling date reported by the YSI (M/D/YYYY)<br> * time = the sampling time reported by the YSI (HH:MM:SS)<br> * fract_sec = the smapling fractions of a second reported by the YSI<br> * site_name = the site name on the YSI deployment (left blank)<br> * cond = the conductivity (uS/cm)<br> * nLF_cond = the nLF conductivity (uS/cm)<br> * perc_ODO = the percent oxygen saturation<br> * perc_local_ODO = the percent local oxygen saturation<br> * ODO_conc = the dissolved oxygen concentration (mg/L)<br> * sal = the salinity (psu)<br> * sp_cond = the specific conductivity (uS/cm)<br> * TDS = the total dissolved solids (mg/L)<br> * pH = the pH<br> * pH mV = the millivolts of the pH sensor<br> * temp = the temperature (dC)<br> * battery = the battery voltage<br> * cable_pwr = the cable power<br> <br> </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.