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FIGURE 3. Agapetes wardii var. heterotricha. A. Habit. B. Flowering branch. C. Opened corolla, abaxial view. D. Stamen, abaxial view. E. Calyx and style. From Ayeyarwady Expedition 1245 in New taxa of Agapetes (Ericaceae) from Myanmar
FIGURE 3. Agapetes wardii var. heterotricha. A. Habit. B. Flowering branch. C. Opened corolla, abaxial view. D. Stamen, abaxial view. E. Calyx and style. From Ayeyarwady Expedition 1245 (IBSC).
Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring
<h2><strong>Open urban mmWave radar and camera vehicle classification dataset for traffic monitoring</strong></h2><h3><strong>Description</strong></h3><p>The archive contains a dataset that can be used for multi-sensor-based vehicle detection/classification. Each part of the dataset is divided into four separate subfolders. All the footage was collected from different parts of Tallinn during the late winter and early spring. The dataset contains 8393 frames. Each frame comes with a corresponding annotation in XML and YOLO formats and a JSON file containing mmWave radar point cloud data. </p>
Open Research Datasets – Testing Mantle Convection Simulations with Paleobiology and Other Stratigraphic Observations: Examples from Western North America
<p>Data supporting the findings of "Testing Mantle Convection Simulations with Paleobiology and Other Stratigraphic Observations: Examples from Western North America" by Victoria M. Fernandes, Gareth G. Roberts and Fred D. Richards. Further details about these data can be found in the main manuscript and accompanying Supporting Information. This research was funded by NERC Large Grant MC2: Mantle Convection Constrained (NE/T012595/1). <br><br>The datasets contained in this repository:<br>1) Uplift constraints from youngest outcropping marine to terrestrial stratigraphic transitions, as described in Fernandes et al. (2019) JGR Earth Surface (Figure 1; MarineTerrestrialTransition_Fernandes_etal2019.txt)<br>2) Digitised sediment isopachs from Roberts & Kirschbaum (1995), USGS Professional Paper 1561 (Supplementary Figure S3; Roberts_Kirschbaum_1995_isopachs.zip)<br>3) Air-loaded subsidence grids (Supplementary Figure S4; Subsidence.zip)<br>4) Temperature grids at 25 km depth intervals, from 50–400 km depth, generated from the SLNAAFSA hybrid Vs model of Hoggard et al. (2020), Nature Geoscience (Supplementary Figure S6; Temperature_grids_txt.zip)</p>
Mirror of "ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials"
<h2>Mirrored from Joint Research Centre Data Catalogue</h2><p><a href="https://data.jrc.ec.europa.eu/collection/id-00138#datasets">https://data.jrc.ec.europa.eu/collection/id-00138#datasets</a></p><blockquote><p>This collection contains datasets from ENSPRESO, an EU-28 wide, open dataset for energy models on renewable energy potentials, at national (NUTS0) and regional levels (NUTS2) for the 2010-2050 period. Within ENSPRESO, ENergy Systems Potential Renewable Energy SOurces, technical potentials are provided for wind, solar and biomass, based on coherent GIS-based land-restriction scenarios. For wind, resource evaluation also considers setback distances as well as high resolution geo-spatial wind speed data. For solar, potentials are derived from irradiation data and available area for solar applications. For biomass, agriculture, forestry and waste sectors are considered. The temporal resolution for wind and solar is both annual and year fractions (timeslices as used by JRC-EU-TIMES). ENSPRESO complements the EMHIRES collection, that provides meteorologically derived power time series at high temporal and spatial resolution. ENSPRESO can impact the results of any energy model by improving its analyses of the competition and complementarity of energy technologies.</p></blockquote><p><a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:RUIZ%20CASTELLO%20Pablo">RUIZ CASTELLO Pablo</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:NIJS%20Wouter">NIJS Wouter</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:TARVYDAS%20Dalius">TARVYDAS Dalius</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:SGOBBI%20Alessandra">SGOBBI Alessandra</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ZUCKER%20Andreas">ZUCKER Andreas</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:PILLI%20Roberto">PILLI Roberto</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:CAMIA%20Andrea">CAMIA Andrea</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THIEL%20Christian">THIEL Christian</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:HOYER-KLICK%20Carsten">HOYER-KLICK Carsten</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:DALLA%20LONGA%20Francesco">DALLA LONGA Francesco</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:KOBER%20Tom">KOBER Tom</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BADGER%20Jake">BADGER Jake</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:VOLKER%20Patrick">VOLKER Patrick</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:ELBERSEN%20Berien">ELBERSEN Berien</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:BROSOWSKI%20Andre">BROSOWSKI Andre</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:THR%C3%84N%20Daniela">THRÄN Daniela</a>; <a href="https://publications.jrc.ec.europa.eu/repository/search/?filter=CONTRIBUTOR:JONSSON%20Klas">JONSSON Klas</a></p><h3>How to cite</h3><p>Ruiz Castello, P., Nijs, W., Tarvydas, D., Sgobbi, A., Zucker, A., Pilli, R., Camia, A., Thiel, C., Hoyer-Klick, C., Dalla Longa, F., Kober, T., Badger, J., Volker, P., Elbersen, B., Brosowski, A., Thrän, D. and Jonsson, K., ENSPRESO - an open data, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials, European Commission, 2019, JRC116900.</p><p>European Commission</p><p>JRC116900</p><h3>Remarks</h3><p>The originator of this mirror requires stable and reliable URLs due to an integration of the dataset into an automated workflow. The data catalogue has frequent outages.</p>
Figure 4 in "How Sensitive are Exports of a Small Open Economy to Prices and Foreign Incomes? Evidence From a Panel Data Analysis for Tunisia"
Figure 4 Principal Cocponent Analysis (PCA) perforced on the environcental factors. V: Vanadium, Cn: Chnomium, Mn: Mancanese, Co: Cobalt, Ni: Nickel, Cu: Coppen, Zn: Zinc, As: Ansenic, Cd: Cadmium, Sn: Tin, Tl: Thallium, Pb: Lead, Li: Lithium, Rb: Rubidium, Sn: Stnontium, Tsol: soil tempenatune, Hsol: soil humiditu, Gna: cnanulometnu, Na+: sodium, MO: oncanic matten.
Figure 1 in "How Sensitive are Exports of a Small Open Economy to Prices and Foreign Incomes? Evidence From a Panel Data Analysis for Tunisia"
Figure 1 Localization of sacplinS laSoon stations. S1: Bizente, S2: El Bchenliua, S3: Old hanbon ob Ghan El Melh, S4: Opposite El Bouchaz, S5: Sidi Ali Mekki, S6: Nonth lacoon ob Tunis, S7: South lacoon ob Tunis, S8: Konba, S9: Tazanka, S10: El Bibane.
THLS - An open source dataset for Brazilian Portuguese speech processing
<p>THLS Open Source Brazilian Portuguese Speech Dataset with 1000 sentences balanced phonetically.</p> <p> </p> <p>Authors:</p> <ul> <li>Luiz Felipe Vecchietti</li> <li>Thalles Melo Batista Pieroni (Voice)</li> </ul> <p> </p> <p>https://gitlab.com/lfelipesv/1000-sentences-thls-dataset</p>
EEG data closed and open eyes: OpenBCI Cyton and EEG Prototype
<p>The participant group included a total of seven vol-unteers (males) who agreed to participate in theresearch. Their mean age was 29.67 (range 24–56).The participants indicated that they do not havehearing or visual impairments. Participation was vol-untary and informed consent was obtained for eachparticipant in order to employ their EEG data in ourstudy. We captured the brain activity of the subjects with the proposed EEG prototype (see [1]) and the OpenBCI Cyton board. Gold cup electrodes were placed in accordance with the 10–20 international system for electrode placement andattached to the subjects scalp using a conductive paste. Electrode-skin impedances were checked to bebelow 15 kΩ at all electrodes. Several studies have proved that the alpha rhythm predominates in the occipital area of the brain when subjects remain with their eyes closed and it is reduced when visualstimulation takes place. In accordance with these works, the input channel of the prototype was located in the O2 position. Moreover, to reduce the setup time and improve the EEG signal quality, the reference and ground electrodes were placed inthe FP2 and A1 positions, respectively, where the absence of hair facilitates its placement. All the experiments were conducted in a sound-attenuated and controlled environment. Participants were seated in a comfortable chair and asked to be relaxed and focused on the task, trying to avoid any distraction or external stimulus. Experiments were composed of 2 tasks: the first one, 60 s of oE and the second, 60 s of cE. Each task was separated by a pause of at least 3 s to ensure the participant is rested before a new task. In order to simulate a real-life situation, the subject could move his gaze freely during the eye-open tasks, without the need to keep it at a fixed point. The procedure was conveniently explained in advance allowing the participants to feel comfortable and familiar with the test environment. Possible artifacts were minimized by asking them not to speak, move or blink (or atleast as little as possible) throughout the oE task. A total of 10 tasks (i.e. 10 min) were recorded for each participant, which corresponds to five tasks of oE and five tasks of cE.</p> <p> </p> <p>Data is organized in a folder for each subject (S1, S2, S3, etc.). Inside each subject folder we find two different folders:</p> <ul> <li>PP: which contains the recordings captured with the proposed prototype.</li> <li>OPB: which contains the recordings captured with the Cyton board.</li> </ul> <p>Inside each of these folders we will find 10 files: 5 corresponding to open eyes and closed eyes. The name of these files follows the structure:</p> <ul> <li>Closed eyes: cerrados[# recording]_[# subject].csv</li> <li>Open eyes: abiertos[# recording]_[# subject].csv</li> </ul> <p>Each .csv file contains 2 data columns: timestamp and O2 channel.</p> <p>[1] Laport, F., Dapena, A., Castro, P. M., Vazquez-Araujo, F. J., & Iglesia, D. (2020). A prototype of EEG system for IoT. <em>International journal of neural systems</em>, <em>30</em>(07), 2050018. DOI: https://doi.org/10.1142/S012906572050018</p>
Pirate Cove Intro Temple Opening Flag
Source: Objaverse 1.0 / Sketchfab
Maidan Kurgan in Divnogorye open-air museum
Source: Objaverse 1.0 / Sketchfab
Tarlair Open Air Swimming Pool
Tarlair Open Air Swimming Pool, Macduff. Opened in the 1930's Photogrammetry model created using Reality Capture. 300 images captured with a DJI mini 2 drone. Source: Objaverse 1.0 / Sketchfab
OPEN-KTH-3dMODELS: An Open Dataset of Building Models at KTH Campus Valhallavägen
<p>OPEN-KTH-3dMODELS: An open dataset of building models at KTH Campus Valhallavägen</p> <ul> <li>Open-KTH-3dModels is a subproject of the AD-EYE testbed for Automated Driving and Intelligent Transportation Systems.</li> <li>The dataset comprises of a series .blend files that have prominent buildings from KTH campus Valhallavägen. </li> <li>The dataset also contains PreScan compatible models that can be used wtih AD-EYE (<a href="https://www.adeye.se/open-kth-3dmodels">https://www.adeye.se/</a>)</li> </ul> <p>Visualisation video: https://www.youtube.com/watch?v=F6NfCiul3oE<br>Learn more at <a href="https://www.adeye.se/open-kth-3dmodels">https://www.adeye.se/open-kth-3dmodels</a> or contact <a href="mailto:adeye@md.kth.se">adeye@md.kth.se</a></p> <p> </p> <p>The AD-EYE testbed is based on the design presented in the work <strong>"<em>AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts"</em></strong></p> <p> </p> <p><strong>Original paper:</strong> <a href="https://doi.org/10.4271/2019-01-0126">https://doi.org/10.4271/2019-01-0126</a></p> <p><strong>Preprint available at: </strong><a href="https://arxiv.org/abs/1912.00448">https://arxiv.org/abs/1912.00448</a></p> <p><strong>Citation:</strong></p> <p>Naveen Mohan, Martin Törngren, "AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts", SAE Technical Paper 19AE-0203/2019-01-0126, <a href="https://doi.org/10.4271/2019-01-0126">https://doi.org/10.4271/2019-01-0126</a></p> <p> </p> <p><strong>Notes:</strong></p> <p>Modelling work primarily performed by Lester Jose, during his internship with AD-EYE.</p>
Dataset for: Effects of local open raceme density, patch size, and distance between patches on pollinator behavior responding to floral display size in Salvia nipponica
<p>Flowers cluster at various spatial scales, so pollinators use information from multiple scales when foraging in natural plant populations. Little is known about the effects of interactions between scales or their relative strength. We examined bumblebee foraging behaviour in a natural population of <em>Salvia</em> <em>nipponica</em> in 10 and 7 patches in 2019 and 2020, respectively. We recorded within-patch factors (display size of racemes and local open raceme densities) and patch-level factors (patch size and distance from the nearest patch) and analysed their relationships with pollinator behaviour. The numbers of visits per raceme and flower were mainly affected by the interaction of patch size and raceme density; they were higher in locations with lower raceme density in larger patches. The ratio of flowers visited to all open flowers in a raceme during a raceme visit, which relates to a bumblebee's choice to leave a raceme, was mainly affected by the interaction of display size and local open raceme density; in 2019, it was higher in racemes with smaller display sizes, while in 2020 the strength and direction of the relationship depended on the open raceme density. These results suggest that pollinators relied on the sizes of flower clusters at different spatial scales when visiting and leaving racemes and adjusted their responses to the sizes of flower clusters depending on the distances between clusters. Therefore, it is important to evaluate factors at various spatial scales and their interactions to fully understand pollinator behaviour in natural plant populations.</p>
COS-1m: the first community-scale open space maps of 31 major cities in China created with the layered occlusion perception model
<p>Correspondence to:<br>Shihong Du<br>Peking University<br>Beijing 100871, China<br>E-mail: shdu@pku.edu.cn</p> <p>COS-1m (Community Open Spaces) offers detailed, high-resolution maps of community-level open spaces across 31 major cities in China. Developed using the latest very high-resolution satellite imagery and a novel Layered Occlusion Perception Model (LOPM), this dataset ensures precise representation with a spatial resolution of 1 meter. In total, the COS-1m dataset covers an area of 46727.46 square kilometers.</p> <p>The COS-1m dataset addresses the critical challenge of layered occlusions in remote sensing imagery by accurately modeling and reconstructing the intricate layered structures of COS. This approach allows for the detailed delineation of occluded ground elements, overcoming the limitations of traditional single-layer mapping methods. The dataset reveals that, on average, 60.51 km² of COS area per city is occluded, constituting 10.18% of the total COS area, which had previously been overlooked.</p> <p>Key features of the COS-1m dataset include:</p> <ul> <li><strong>Four Types of Public Open Space Elements</strong>: Green spaces (1), blue spaces (2), squares (4), and walkways (6).</li> <li><strong>Three Types of Coupled Open Spaces</strong>: waterfront woodlands (3), shaded squares (5), and shaded walkways (7).</li> </ul> <p>The dual-layer mapping technique used in COS-1m provides a comprehensive overview of urban open spaces, capturing the vertical overlaps of various elements such as trees, water bodies, squares, and walkways. This dataset is a significant advancement in COS monitoring technology, filling a critical gap in community-scale Sustainable Development Goal (SDG) assessments. It provides urban planners and policymakers with valuable insights to promote more effective and sustainable urban development.</p> <p>COS-1m achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across the 31 cities, demonstrating its robustness and reliability as a tool for urban space analysis and planning.</p> <p>If you use this dataset, please cite: "Lei, Y., Zhang, X., Xiong, S., Tan, G., & Du, S. (2025). Developing Layered Occlusion Perception Model: Mapping community open spaces in 31 China cities. Remote Sensing of Environment, 316, 114498."</p> <p> </p>
Data for Airlines network analysis on an air-rail multimodal system. Journal of Open Aviation Science, 1(2)
<h2>About</h2> <p>This dataset contains all the input data required to generate the analysis and results of the article Delgado, L., Trapote-Barreira, C., Montlaur, A., Bolić, T., & Gurtner, G. (2023). <em>Airlines’ network analysis on an air-rail multimodal system</em>. Journal of Open Aviation Science, 1(2). <a href="https://doi.org/10.59490/joas.2023.7223" rel="nofollow">https://doi.org/10.59490/joas.2023.7223</a></p> <p>The code used is available on GitHub: <a href="https://github.com/UoW-ATM/joas_air_rail_network_analysis">https://github.com/UoW-ATM/joas_air_rail_network_analysis</a></p> <p>The path to the input data can be modified in the scripts provided in the GitHub repository. With the default setting, the input is in a folder called data.</p> <h2>Dataset structure</h2> <ul> <li>data_computed <ul> <li><em>rail_used_emissions.csv</em></li> <li><em>rail_used_emissions_2.csv</em></li> <li><em>routes_emissinos_v2.csv</em></li> </ul> </li> <li>flights_data4 <ul> <li>year=2023 <ul> <li>month=05 <ul> <li><em>1st_week_0523.csv</em></li> </ul> </li> </ul> </li> </ul> </li> <li>renfe <ul> <li>renfe_mid_long <ul> <li><em>agency.txt</em></li> <li><em>calendar.txt</em></li> <li><em>calendar_dates.txt</em></li> <li><em>routes.txt</em></li> <li><em>stops.txt</em></li> <li><em>stop_times.txt</em></li> <li><em>trips.txt</em></li> </ul> </li> </ul> </li> <li><em>aircraftDatabase.csv</em></li> <li><em>airport_static.csv</em></li> <li><em>code_seats.csv</em></li> <li><em>manual_fixed_airports.csv</em></li> <li><em>mat_corrected.csv</em></li> <li><em>type_code_missing.csv</em></li> </ul> <h2>Data description</h2> <h3>data_computed</h3> <p>This folder contains pre-computed values by the authors on rail and air emissions. These are estimated:</p> <ul> <li>For rail using <a href="http://ecopassenger.hafas.de/" rel="nofollow">EcoPassenger</a>.</li> <li>For flights based on the emission model from Montlaur, A., Delgado, L., & Trapote-Barreira, C. (2021). <a href="https://doi.org/10.3390/su131810401" rel="nofollow"><em>Analytical Models for CO<sub>2</sub> Emissions and Travel Time for Short-to-Medium-Haul Flights Considering Available Seats</em></a>. Sustainability 13.18 (2021) and from some specific flights using EUROCONTROL's <a href="https://www.eurocontrol.int/platform/integrated-aircraft-noise-and-emissions-modelling-platform">IMPACT </a>model.</li> </ul> <h3>flights_data4</h3> <p>Information from flights_data4 table from <a href="https://opensky-network.org/data/impala">OpenSky</a>. Please refer to the <a href="https://opensky-network.org/about/terms-of-use">terms of use of OpenSky</a> for the restrictions on the further use of these data.</p> <p>The file <em>1st_week_0523.csv </em>contains the information of the table flights_data4 from OpenSky for the week of the 1st May 2023 (01/05/2023 to 07/05/2003). This was downloaded with the SQL query </p> <p>SELECT * FROM flights_data4 WHERE lastseen >= 1682899200 AND firstseen <= 1683504000;</p> <p>Note that the lastseen and firstseen are in UnixTime and correspond to 2023-05-01 00:00:00 UTC and 2023-05-08 00:00:00 UTC respectively. This ensures capturing all flights landing on 01/05/2023 even if they departed the day before and departing on 07/05/2023 even if landing the day after.</p> <h3>renfe</h3> <p>renfe folder contains the General Transit Feed Specification (GTFS) data from the <a href="https://data.renfe.com/dataset/horarios-de-alta-velocidad-larga-distancia-y-media-distancia">Renfe</a> rail operator with the high-speed, long and medium distances timetables. Note that Renfe provides the data under a <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Creative Commons Attribution 4.0</a> license. </p> <h3>Other datasets</h3> <ul> <li><em>aircraftDatabase.csv </em>: Database containing information on aircraft information (type, manufacturer, license, etc.) as a function of their transponder icao24 code. Obtained from <a href="https://opensky-network.org/aircraft-database">OpenSky</a>.</li> <li><em>airport_static.csv</em>: Airport ICAO code, latitude and longitude.</li> <li><em>code_seats.csv</em>: allows each aircraft model to be related to the seats in the cabin. It has been extracted from airline websites and other sources.</li> <li><em>manual_fixed_airports.csv</em>: List of manually modified airports for arrival/departure to fix wrong rotations from OpenSky data. For each airport ICAO code, it provides the one that should be used instead and some information on that airport (e.g., name)</li> <li><em>mat_corrected.csv</em>: identifies the rotation of each aircraft in the week of study. This is especially relevant for fleet analysis as it is necessary to know the start and end airport of each rotation for each day. It has been identified with an ad-hoc algorithm, and some data has been corrected with <a href="https://www.flightradar24.com/">FlightRadar24</a> data support.</li> <li><em>type_code_missing.csv</em>: relates the transponders' icao24 identifiers missing from OpenSky to the aircraft type (complement aircraftDatabase), compiled from <a href="https://www.flightradar24.com/" rel="nofollow">FlightRadar24</a>.</li> </ul>
BioFlow-Insight Workflow Corpus Open License
<p>This corpus describes a collection of open license Nextflow workflows which we're gathered from Github in February 2024.</p>
Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites
<p>Data for reproducibility of the paper "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites"</p> <p>Corresponding paper:</p> <p>ShanPing Liu, Romain Dupuis, Dong Fan, Salma Benzaria, Mickaele Bonneau, Prashant Bhatt, Mohamed Eddaoudi, and Guillaume Maurin. "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites." <em>Chemical Science</em> (2024). https://doi.org/10.1039/D3SC05612K</p>
1st International Workshop on Open Web Search #wows2024 at ECIR 2024: Document Processors
<p><a href="https://opensearchfoundation.org/en/events-osf/wows2024/#osf-callforcontributions">The First International Workshop on Open Web Search</a> (WOWS) hosted at [ECIR 2024](https://www.ecir2024.org/) aimed to promote and discuss ideas and approaches to open up the web search ecosystem so that small research groups and young startups can leverage the web to foster an open and diverse search market. The workshop had two calls that support collaborative and open web search engines: (1) for scientific contributions, and (2) for open-source implementations. This repository collects the outputs of all submitted document processing components on public datasets for the second call aims to gather open-source prototypes and gain practical experience with collaborative, cooperative evaluation of search engines and their components using the [TIREx Information Retrieval Evaluation Platform](https://www.tira.io/tirex) hosted on [TIRA](https://www.tira.io).</p> <p> </p> <p> </p> <h2>Citations</h2> <p>If you reuse the resources, please ensure to cite TIRA and TIREx and the corresponding datasets, the corresponding bib-entries are:</p> <p>For TIREx:</p> <pre><code>@InProceedings{froebe:2023e,<br> author = {Maik Fr{\"o}be and {Jan Heinrich} Reimer and Sean MacAvaney and Niklas Deckers and Simon Reich and Janek Bevendorff and Benno Stein and Matthias Hagen and Martin Potthast},<br> booktitle = {46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023)},<br> doi = {10.1145/3539618.3591888},<br> editor = {Hsin{-}Hsi Chen and Wei{-}Jou (Edward) Duh and Hen{-}Hsen Huang and Makoto P. Kato and Josiane Mothe and Barbara Poblete},<br> ids = {potthast:2023t},<br> isbn = {9781450394086},<br> month = jul,<br> numpages = 11,<br> pages = {2826--2836},<br> publisher = {ACM},<br> site = {Taipei, Taiwan},<br> title = {{The Information Retrieval Experiment Platform}},<br> url = {https://dl.acm.org/doi/10.1145/3539618.3591888},<br> year = 2023<br>}<br></code></pre> <p>for TIRA:</p> <pre><code>@InProceedings{froebe:2023b,<br> address = {Berlin Heidelberg New York},<br> author = {Maik Fr{\"o}be and Matti Wiegmann and Nikolay Kolyada and Bastian Grahm and Theresa Elstner and Frank Loebe and Matthias Hagen and Benno Stein and Martin Potthast},<br> booktitle = {Advances in Information Retrieval. 45th European Conference on {IR} Research ({ECIR} 2023)},<br> doi = {10.1007/978-3-031-28241-6_20},<br> editor = {Jaap Kamps and Lorraine Goeuriot and Fabio Crestani and Maria Maistro and Hideo Joho and Brian Davis and Cathal Gurrin and Udo Kruschwitz and Annalina Caputo},<br> ids = {potthast:2023h},<br> month = apr,<br> pages = {236--241},<br> publisher = {Springer},<br> series = {Lecture Notes in Computer Science},<br> site = {Dublin, Irland},<br> title = {{Continuous Integration for Reproducible Shared Tasks with TIRA.io}},<br> url = {https://link.springer.com/chapter/10.1007/978-3-031-28241-6_20},<br> year = 2023<br>}</code><br><br>All query processors are described in the corresponding WOWS paper, please cite the papers and underlying approaches accordingly.</pre> <p>Forthermore, please cite the datasets that you use.</p> <h3>Args.me</h3> <p>If you re-use the Args.me indices, please additionally cite:</p> <pre><code>@InProceedings{bondarenko:2021d,<br> address = {Berlin Heidelberg New York},<br> author = {Alexander Bondarenko and Lukas Gienapp and Maik Fr{\"o}be and Meriem Beloucif and Yamen Ajjour and Alexander Panchenko and Chris Biemann and Benno Stein and Henning Wachsmuth and Martin Potthast and Matthias Hagen},<br> booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction. 12th International Conference of the CLEF Association (CLEF 2021)},<br> editor = {{K. Sel{\c{c}}uk} Candan and Bogdan Ionescu and Lorraine Goeuriot and Henning M{\"u}ller and Alexis Joly and Maria Maistro and Florina Piroi and Guglielmo Faggioli and Nicola Ferro},<br> ids = {potthast:2021t},<br> month = sep,<br> pages = {450-467},<br> publisher = {Springer},<br> series = {Lecture Notes in Computer Science},<br> site = {Bucharest, Romania},<br> title = {{Overview of Touch{\'e} 2021: Argument Retrieval}},<br> volume = 12880,<br> year = 2021<br>}<br></code><br><code>@InProceedings{bondarenko:2022f,<br> address = {Berlin Heidelberg New York},<br> author = {Alexander Bondarenko and Maik Fr{\"o}be and Johannes Kiesel and Shahbaz Syed and Timon Gurcke and Meriem Beloucif and Alexander Panchenko and Chris Biemann and Benno Stein and Henning Wachsmuth and Martin Potthast and Matthias Hagen},<br> booktitle = {Experimental IR Meets Multilinguality, Multimodality, and Interaction. 13th International Conference of the CLEF Association (CLEF 2022)},<br> editor = {Alberto Barr{\'o}n-Cede{\~n}o and Giovanni Da San Martino and Mirko Degli Esposti and Fabrizio Sebastiani and Craig Macdonald and Gabriella Pasi and Allan Hanbury and Martin Potthast and Guglielmo Faggioli and Nicola Ferro},<br> ids = {potthast:2022j},<br> month = sep,<br> numpages = 29,<br> publisher = {Springer},<br> series = {Lecture Notes in Computer Science},<br> site = {Bologna, Italy},<br> title = {{Overview of Touch{\'e} 2022: Argument Retrieval}},<br> year = 2022<br>}</code></pre> <h3>Antique</h3> <p>If you re-use the Antique indices, please additionally cite:</p> <pre><code>@inproceedings{hashemi:2020,<br> author = {Helia Hashemi and Mohammad Aliannejadi and Hamed Zamani and W. Bruce Croft},<br> editor = {Joemon M. Jose and Emine Yilmaz and Jo{\~{a}}o Magalh{\~{a}}es and Pablo Castells and Nicola Ferro and M{\'{a}}rio J. Silva and Fl{\'{a}}vio Martins},<br> title = {{ANTIQUE:} {A} Non-factoid Question Answering Benchmark},<br> booktitle = {Advances in Information Retrieval - 42nd European Conference on {IR} Research, {ECIR} 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part {II}},<br> series = {Lecture Notes in Computer Science},<br> volume = {12036},<br> pages = {166--173},<br> publisher = {Springer},<br> year = {2020},<br>}</code><br><br></pre> <h3>CORD-19</h3> <p>If you re-use the CORD-19 indices, please additionally cite:</p> <pre><code>@article{voorhees:2020,<br> author = {Ellen M. Voorhees and Tasmeer Alam and Steven Bedrick and Dina Demner{-}Fushman and William R. Hersh and Kyle Lo and Kirk Roberts and Ian Soboroff and Lucy Lu Wang},<br> title = {{TREC-COVID:} constructing a pandemic information retrieval test collection},<br> journal = {{SIGIR} Forum},<br> volume = {54},<br> number = {1},<br> pages = {1:1--1:12},<br> year = {2020},<br>}<br><br>@article{wang:2020,<br> author = {Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and Kathryn Funk and Rodney Kinney and Ziyang Liu and William Merrill and Paul Mooney and Dewey A. Murdick and Devvret Rishi and Jerry Sheehan and Zhihong Shen and Brandon Stilson and Alex D. Wade and Kuansan Wang and Chris Wilhelm and Boya Xie and Douglas Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier},<br> title = {{CORD-19:} The Covid-19 Open Research Dataset},<br> journal = {CoRR},<br> volume = {abs/2004.10706},<br> year = {2020},<br> eprinttype = {arXiv},<br> eprint = {2004.10706},<br>}<br><br></code></pre> <h3>Cranfield</h3> <p>If you re-use the Cranfield indices, please additionally cite:</p> <pre><code>@inproceedings{cleverdon:1967,<br> title={The {C}ranfield tests on index language devices},<br> author={Cleverdon, Cyril},<br> booktitle={{ASLIB} Proceedings},<br> year={1967},<br> pages = {173--192},<br> organization={MCB UP Ltd. (Reprinted in Readings in Information Retrieval, Karen Sparck-Jones and Peter Willett, editors, Morgan Kaufmann, 1997)}<br>}<br><br>@inproceedings{cleverdon:1991,<br> author = {Cyril W. Cleverdon},<br> editor = {Abraham Bookstein and Yves Chiaramella and Gerard Salton and Vijay V. Raghavan},<br> title = {The Significance of the {C}ranfield Tests on Index Languages},<br> booktitle = {Proceedings of the 14th Annual International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval. Chicago, Illinois, USA, October 13-16, 1991 (Special Issue of the {SIGIR} Forum)},<br> pages = {3--12},<br> publisher = {{ACM}},<br> year = {1991},<br>}</code></pre> <h3>Medline TREC Genomics</h3> <p>If you re-use the Medline TREC Genomics indices, please additionally cite:</p> <pre><code>@inproceedings{hersh:2004,<br> author = {William R. Hersh and Ravi Teja Bhupatiraju and L. Ross and Aaron M. Cohen and Dale Kraemer and Phoebe Johnson},<br> editor = {Ellen M. Voorhees and Lori P. Buckland},<br> title = {{TREC} 2004 Genomics Track Overview},<br> booktitle = {Proceedings of the Thirteenth Text REtrieval Conference, {TREC} 2004, Gaithersburg, Maryland, USA, November 16-19, 2004},<br> series = {{NIST} Special Publication},<br> volume = {500-261},<br> publisher = {National Institute of Standards and Technology {(NIST)}},<br> year = {2004},<br>}<br><br>@inproceedings{hersh:2005,<br> author = {William R. Hersh and Aaron M. Cohen and Jianji Yang and Ravi Teja Bhupatiraju and Phoebe M. Roberts and Marti A. Hearst},<br> editor = {Ellen M. Voorhees and Lori P. Buckland},<br> title = {{TREC} 2005 Genomics Track Overview},<br> booktitle = {Proceedings of the Fourteenth Text REtrieval Conference, {TREC} 2005, Gaithersburg, Maryland, USA, November 15-18, 2005},<br> series = {{NIST} Special Publication},<br> volume = {500-266},<br> publisher = {National Institute of Standards and Technology {(NIST)}},<br> year = {2005},<br>}</code><br><br><br></pre> <h3>Medline TREC Precision Medicine</h3> <p>If you re-use the Medline TREC Precision Medicine indices, please additionally cite:</p> <pre><code>@inproceedings{roberts:2017,<br> author = {Kirk Roberts and Dina Demner{-}Fushman and Ellen M. Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar and Shubham Pant},<br> editor = {Ellen M. Voorhees and Angela Ellis},<br> title = {Overview of the {TREC} 2017 Precision Medicine Track},<br> booktitle = {Proceedings of The Twenty-Sixth Text REtrieval Conference, {TREC} 2017, Gaithersburg, Maryland, USA, November 15-17, 2017},<br> series = {{NIST} Special Publication},<br> volume = {500-324},<br> publisher = {National Institute of Standards and Technology {(NIST)}},<br> year = {2017},<br>}<br><br>@inproceedings{roberts:2018,<br> author = {Kirk Roberts and Dina Demner{-}Fushman and Ellen M. Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar},<br> editor = {Ellen M. Voorhees and Angela Ellis},<br> title = {Overview of the {TREC} 2018 Precision Medicine Track},<br> booktitle = {Proceedings of the Twenty-Seventh Text REtrieval Conference, {TREC} 2018, Gaithersburg, Maryland, USA, November 14-16, 2018},<br> series = {{NIST} Special Publication},<br> volume = {500-331},<br> publisher = {National Institute of Standards and Technology {(NIST)}},<br> year = {2018},<br>}</code><br><br></pre> <h3>MS MARCO (TREC Deep Learning 2019 and 2020</h3> <p>If you re-use the MS MARCO indices, please additionally cite:</p> <pre><code>@inproceedings{craswell:2019,<br> author = {Nick Craswell and Bhaskar Mitra and Emine Yilmaz and Daniel Campos and Ellen M. Voorhees},<br> booktitle = {28th International Text Retrieval Conference, {TREC} 2019, Gaithersburg, Maryland, USA},<br> editor = {{Ellen M.} Voorhees and Angela Ellis},<br> month = nov,<br> title = {{Overview of the {TREC} 2019 Deep Learning Track}},<br> publisher = {National Institute of Standards and Technology (NIST)},<br> series = {NIST Special Publication},<br> year = {2019}<br>}<br><br>@inproceedings{craswell:2020,<br> author = {Nick Craswell and Bhaskar Mitra and Emine Yilmaz and Daniel Campos},<br> editor = {Ellen M. Voorhees and Angela Ellis},<br> title = {{Overview of the {TREC} 2020 Deep Learning Track}},<br> booktitle = {Proceedings of the 29th Text REtrieval Conference, {TREC} 2020, Virtual Event, Gaithersburg, MD, USA, November 16-20, 2020},<br> series = {{NIST} Special Publication},<br> volume = {1266},<br> publisher = {National Institute of Standards and Technology {(NIST)}},<br> year = {2020},<br>}</code></pre> <pre> </pre> <h3>NFCorpus</h3> <p>If you re-use the next indices, please additionally cite:</p> <pre><code>@inproceedings{boteva:2016,<br> author = {Vera Boteva and Demian Gholipour Ghalandari and Artem Sokolov and Stefan Riezler},<br> editor = {Nicola Ferro and Fabio Crestani and Marie{-}Francine Moens and Josiane Mothe and Fabrizio Silvestri and Giorgio Maria Di Nunzio and Claudia Hauff and Gianmaria Silvello},<br> title = {A Full-Text Learning to Rank Dataset for Medical Information Retrieval},<br> booktitle = {Advances in Information Retrieval - 38th European Conference on {IR} Research, {ECIR} 2016, Padua, Italy, March 20-23, 2016. Proceedings},<br> series = {Lecture Notes in Computer Science},<br> volume = {9626},<br> pages = {716--722},<br> publisher = {Springer},<br> year = {2016},<br>}</code></pre> <h3>LongEval</h3> <p>Please cite the [corresponding dataset](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-5151).:</p> <pre><code>@misc{11234/1-5151, title = {{LongEval} Click-Model Relevance Judgements (Qrels)}, author = {Galu{\v s}{\v c}{\'a}kov{\'a}, Petra and Devaud, Romain and Gonzalez-Saez, Gabriela and Mulhem, Philippe and Goeuriot, Lorraine and Piroi, Florina and Popel, Martin}, url = {http://hdl.handle.net/11234/1-5151}, note = {{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University}, copyright = {Qwant {LongEval} Attribution-{NonCommercial}-{ShareAlike} License}, year = {2023} }</code><br><br></pre> <p>The index can be re-used in the [LongEval 2024](https://clef-longeval.github.io/) shared task hosted at [CLEF 2024](https://clef2024.imag.fr/). The documents (and thereby the derived PyTerrier Index are under the <a href="https://lindat.mff.cuni.cz/repository/xmlui/page/Qwant_LongEval_BY-NC-SA_License">Qwant LongEval Attribution-NonCommercial-ShareAlike License</a> and by reusing the indices you also accept and aggree to do this under the sharealike qwant license.</p>
Seasonal and spatial variation of paraben concentrations in urban coastal waters from freshwater lagoons to the open ocean of Brazil
Open the record for dataset details and reuse information.
Open Data Package: Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science.
<p>Open Data Package for the paper: Stefanie Betz, Birgit Penzenstadler, Leticia Duboc, Ruzanna Chitchyan, Sedef Akinli Kocak, Ian Brooks, Shola Oyedeji, Jari Porras, Norbert Seyff, and Colin C. Venters. 2024. Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science. ACM Trans. Softw. Eng. Methodol. 24 00, JA, Article 00 (March 2024), 39 pages. https://doi.org/10.1145/3649597 25</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.