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Open Science: New Challenges and Opportunities for the PV sector
<p>Presentation given at the European PV Solar Energy Conference, Marseille, 2019 about the development of Open Science in the context of photovoltaics</p>
Ground Truth for DCASE 2020 Challenge Task 2 Evaluation Dataset
<p><strong>Description</strong></p> <p>This data is the ground truth for the "<a href="https://zenodo.org/record/3841772">evaluation dataset</a>" for the <strong>DCASE 2020 Challenge Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring" </strong><a href="http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds">[task description]</a>. </p> <p>In the task, three datasets have been released: "<a href="http://zenodo.org/record/3678171">development dataset</a>", "<a href="https://zenodo.org/record/3727685">additional training dataset</a>", and "<a href="https://zenodo.org/record/3841772">evaluation dataset</a>". The evaluation dataset was the last of the three released and includes around 400 samples for each Machine Type and Machine ID used in the evaluation dataset, none of which have any condition label (i.e., normal or anomaly). This ground truth data contains the condition labels.</p> <p> </p> <p><strong>Data format</strong></p> <p>The ground truth data is a CSV file like the following:</p> <p>---------------------------------</p> <p>fan<br> id_01_00000000.wav,normal_id_01_00000098.wav,0<br> id_01_00000001.wav,anomaly_id_01_00000064.wav,1<br> ...</p> <p>id_05_00000456.wav,anomaly_id_05_00000033.wav,1<br> id_05_00000457.wav,normal_id_05_00000049.wav,0<br> pump<br> id_01_00000000.wav,anomaly_id_01_00000049.wav,1<br> id_01_00000001.wav,anomaly_id_01_00000039.wav,1<br> ...</p> <p>id_05_00000346.wav,anomaly_id_05_00000052.wav,1<br> id_05_00000347.wav,anomaly_id_05_00000080.wav,1<br> slider<br> id_01_00000000.wav,anomaly_id_01_00000035.wav,1<br> id_01_00000001.wav,anomaly_id_01_00000176.wav,1<br> ...</p> <p>---------------------------------</p> <p>"Fan", "pump", "slider", etc mean "Machine Type" names. The lines following a Machine Type correspond to pairs of a wave file in the Machine Type and a condition label. The first column shows the name of a wave file. The second column shows the original name of the wave file, but this can be ignored by users. The third column shows the condition label (i.e., 0: normal or 1: anomaly).</p> <p> </p> <p><strong>How to use</strong></p> <p>A system for calculating AUC and pAUC scores for the "evaluation dataset" is available on the Github repository <a href="https://github.com/y-kawagu/dcase2020_task2_evaluator">[URL]</a>. The ground truth data is used by this system. For more information, please see the Github repository.</p> <p> </p> <p><strong>Conditions of use</strong></p> <p>This dataset was created jointly by <strong>NTT Corporation</strong> and <strong>Hitachi, Ltd.</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Publication</strong></p> <p>If you use this dataset, please cite <strong>all the following three papers</strong>:</p> <p>Yuma Koizumi, Shoichiro Saito, Noboru Harada, Hisashi Uematsu, and Keisuke Imoto, "ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection," in Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. <a href="https://ieeexplore.ieee.org/document/8937164">[pdf]</a></p> <p>Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019. <a href="http://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Purohit_21.pdf">[pdf]</a></p> <p>Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada, "Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring<em>,"</em> in Proc. 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Koizumi_3.pdf">[pdf]</a></p> <p><br> <strong>Feedback</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Yuma Koizumi, <a href="mailto:koizumi.yuma@ieee.org">koizumi.yuma@ieee.org</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul>
[2019 QSM Reconstruction Challenge] Submissions Stage 1
<p>This repository contains the original, unaltered files submitted to Stage 1 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge.</p> <p>The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available <a href="https://doi.org/10.5281/zenodo.4559540">here</a>. Information about the submitted solutions and resulting analysis metrics are available <a href="https://doi.org/10.5281/zenodo.3687196">here</a>.</p> <p>The results of the challenge are fully reported in the journal article "<a href="http://doi.org/10.1002/mrm.28754">QSM Reconstruction Challenge 2.0: Design and Report of Results</a>".</p>
Global Wheat Head Dataset - 2020 challenge version
<p>The latest version is V4.</p> <p>This is the only official version of the Global Wheat Head Dataset presented in David et al. (2020) . It's a corrected version of the dataset published on Kaggle, and the one used for the Codalab challenge.</p> <p>Test labels are available on request by filling the form <a href="https://docs.google.com/forms/d/e/1FAIpQLSciaWUwQDNFP199Xb0Iqt2fY67tQI0hAZBJCCfvwd5OuIVQ3A/viewform?usp=sf_link">here </a> or contacting <strong>etienne.david@outlook.com</strong></p> <p>If you use the dataset for your paper, please cite: <a href="https://doi.org/10.34133/2020/3521852">https://doi.org/10.34133/2020/3521852</a></p> <p>If you want to benchmark your solution and get localization and counting metrics, please submit to the codalab challenge: </p>
Voice Conversion Challenge 2020 database v1.0
<pre>Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores. In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages. This repository contains the training and evaluation data released to participants, target speaker’s speech data in English for reference purpose, and the transcriptions for evaluation data. For more details about the challenge and the listening test results please refer to [4] and README file. </pre> <pre>[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco. [2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016. [3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018. [4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14.</pre>
Voice Conversion Challenge 2020 Listening Test Data
<pre>Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores. In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages. As for listening test, we subcontracted the crowd-sourced perceptual evaluation with English and Japanese listeners to Lionbridge TechnologiesInc. and Koto Ltd., respectively. Given the extremely large costs required for the perceptual evaluation, we selected 5 utterances (E30001, E30002, E30003,E30004, E30005) only from each speaker of each team. To evaluate the speaker similarity of the cross-lingual task, we used audio in both the English language and in the target speaker’s L2language as reference. For each source-target speaker pair, we selected three English recordings and two L2 language recordings as the natural reference for the converted five utterances. </pre> <p>This data repository includes the audio files used for the crowd-sourced perceptual evaluation and raw listening test scores. </p> <pre>[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco. [2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016. [3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018. [4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14. [5] Rohan Kumar Das, Tomi Kinnunen, Wen-Chin Huang, Zhenhua Ling, Junichi Yamagishi, Yi Zhao, Xiaohai Tian, and Tomoki Toda. "Predictions of subjective ratings and spoofing assessments of voice conversion challenge 2020 submissions." Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 99-120, DOI: 10.21437/VCC_BC.2020-15. </pre>
Tractography Challenge ISMRM 2015 High-resolution Data
<p>The ground truth of this tractography validation data set was defined based on the fiber bundle geometry of a high-quality Human Connectome Project (HCP) dataset, constructed from multiple whole-brain global tractography maps. An expert radiologist extracted 25 major tracts (i.e., bundles of streamlines) from the tractogram. These association, projection and commissural fibers covered more than 70% of the white matter across the whole brain. The dataset features a brain-like macro-structure of long-range connections, mimicking an <em>in vivo</em> HCP-quality acquisition based on a simulated diffusion signal. </p>
Challenges in Replay Detection by TDLM in Post-Encoding Resting State
<p>Data for the paper "Challenges in Replay Detection by TDLM in Post-Encoding Resting State".</p> <p>This extension of the previous dataset contains the resting state data. For each participant, a 8 minutes resting state was recorded before and after the main experiment (localizer plus learning), and before the final retrieval session.</p> <p>Two files are uploaded per participant, the pre-experiment resting state (RS1) and the post-learning resting state (RS2). All files are MaxFiltered and the head positioning has been realigned using MaxFilter movement correction to the head position during the initial localizer. The localizer data has been previously published and can be downloaded in v1 of this dataset at https://doi.org/10.5281/zenodo.8001755</p> <p>All relevant information can be found in the related publication. Behavioural data necessary to reproduce the results will be uploaded to GitHub at https://github.com/CIMH-Clinical-Psychology/DeSMRRest-TDLM-Simulation</p> <p>There are markers in the files as follows:</p> <p>###############################################################<br>## Port Trigger Table<br>## Port Code | Meaning<br>## ---------------------------------------------------<br>## 0 | don't send trigger<br>## 10 | start RS session<br>## 11 | end RS session<br>## 127 | button press has happened<br>## 255 | start and end of session<br>###############################################################</p> <p> </p>
W4RES Needs, perceptions and challenges in RHC landscape for eight regions dataset1 2021/03/30
<p>The main idea behind the survey design was to obtain a clear identification of key factors correlated to the market uptake of RHC solutions through a gender disaggregated analytical approach in eight countries (Greece, Italy, Germany, Belgium, Denmark, Slovakia, Norway, and Bulgaria). To better address different perspectives across the quadruple helix, the survey has been designed to include questions that are specifically targeted to each group. The survey has been divided into broad sections covering the most relevant topics to be addressed.</p><p>As for the interviews, to analyze needs, perceptions and challenges of market actors and stakeholders through a gender lens, the following research questions were defined:</p><p>I. Identify needs, perceptions and challenges of market actors and stakeholders across the quadruple helix in the 8 regions regarding the uptake of RHC solutions.</p><p>II. Identify needs, perceptions and challenges of market actors and stakeholders across the quadruple helix in the 8 regions regarding the role of women in RHC.</p><p>The survey was coded on the EUSurvey software and translated in the following languages: English, German, French, Bulgarian, Danish, Greek, Italian, Dutch and Slovak. As advised by ECWT, the partner based in Norway, it was agreed that disseminating the survey in English to the potential respondents operating in Norway was an appropriate strategy and that a translation in Norwegian was not necessary.</p>
ForensicVM Windows 11 Virtualization Dataset: Cloud and Local Application Data Retrieval with Virtualization and Password Bypass Challenge
<p>A Windows 11 Pro dataset designed in VirtualBox, complete with local and cloud apps, is to be virtually analyzed for crucial evidence. Bypassing the straightforward 'Bart' password is essential for access, yet original passwords should remain unchanged for others to attempt the same challenge. The task involves determining the password's nature, Bart's motives, identifying involved cloud applications, and extracting data both offline and online. The viability of dead box forensics for complete data retrieval is questioned, alongside what additional information network access could unveil. The challenge includes identifying two financial applications, extracting their data, and gathering cryptocurrency-related information, presented as an engaging forensicVM showcase by Nuno Mourinho, Mario Candeias, and Rogerio Bravo (Escola Superior de Tecnologia e Gestão de Beja, Instituto Politécnico de Beja).</p>
A challenging data set for evaluating part-of-speech taggers
<p>This data set contains 2,227 sentences, with a part-of-speech (POS) tag specified for a single word in the sentence. The data file is a tab-separated text file where each row (after the header row) is formatted as follows:</p><p><i>sentence <TAB> POS tag <TAB> (optional) motivation</i></p><p>Note that, in the sentence (= a string of space-separated characters), the POS-tagged word is indicated by the POS tag in brackets, placed just after the word to which it refers. Example:</p><p><i>The road bends [VERB] to the right . VERB</i></p><p>In this example, the optional motivation is not included, as the tagged word can easily be identified as being a verb.</p>
The evolution and future of research on Nature-based Solutions to address societal challenges
<p>This dataset comprises the bibliographic text files used to analyse the Nature-based Solutions research landscape as presented in:</p> <ul> <li>Dunlop, T., Khojasteh, D., Cohen-Shacham, E., Glamore, W., Haghani, M., van den Bosch, M., Rizzi, D., Greve, P., Felder, S. The Evolution and Future of Research on Nature-based Solutions to Address Societal Challenges. <em>Communications Earth & Environment</em>. 2024.</li> </ul> <p>Excel spreadsheets containing data for the Global Water Security Index (Gain et al., 2016) presented in Figure 2 and the data required to reproduce Figures 1 and 2 in the paper above are also shared.</p>
Challenges of constructing and selecting the "perfect" initial and boundary conditions for the LES model PALM
<p><strong>README</strong></p> <p>All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:<br>1. IBC-pre-post-process-revised.zip which contains:<br> - Radio sounding data used for vertical profile statistical and visual comparison. They are stored as "CHMU-soundings.dat" in the CHMU_soundings directory<br> - code for making the figures for vertical profile comparison between the WRF and PALM model<br> - code for performing the statistical analysis for the vertical profiles of PALM and the WRF model<br> - code for making the scatter plots of PALM and WRF vertical profiles<br> - code for making the heatmaps of the PALM model data</p> <p>2. PALM_code.zip contains the source code for the current version of the PALM model used for this experiment</p> <p>3. palm_inputs.zip contains:<br> - static driver file<br> - dynamic driver file<br> - configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr)<br>for each of the performed simulations</p> <p>4. postproc.zip contains:<br> - the code for performing statistical analysis for minimum (min), average (Avg), and maximum (max) three-day averaged differences for the WRF and PALM model outputs<br> - the code for making figures of the differences between selected pairs of WRF and PALM model outputs</p> <p>5. wrf_namelist.zip contains:<br> - list of files in which the setups/configuration for the WRF ensemble used in this experiment</p> <p><strong>PALM MODEL INSTALLATION AND USAGE GUIDE</strong></p> <p>A. Installation:</p> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <p>B. Usage:</p> <p>After a successful installation, the executables for all packages have been linked into the directory /bin and a default PALM configuration file can be found at /.palm.config.default. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p>
A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal
<p>This repository is linked to the following article:</p> <ul> <li>Prütz, R., Fuss, S., Lück, S. Stephan, L. & Rogelj, J., A taxonomy to map evidence on the co-benefits, challenges, and limits of carbon dioxide removal. <em>Commun Earth Environ</em> <strong>5</strong>, 197 (2024). <a href="https://doi.org/10.1038/s43247-024-01365-z">https://doi.org/10.1038/s43247-024-01365-z</a></li> </ul> <p>This repository includes: </p> <ul> <li>The literature-based data set that was compiled and used to develop the taxonomy of carbon dioxide removal side effects</li> <li>Code to run the machine learning classifier and to process and visualise the data</li> <li>Training data and an abbreviation list which is required to run the provided code</li> </ul>
Challenges to freedom of speech and journalists in Ukraine in times of war – Non-representative online expert survey of Ukrainian journalists (January 2023)
The expert survey of journalists was conducted from 18 to 27 January 2023 using a self-completion questionnaire in Google Forms. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation on the request of the Human Rights Centre ZMINA with the support of Freedom House Ukraine. A total of 132 people participated in the survey. The respondents were selected using the method of voluntary selection and snowballing to the point of saturation. The sample represents only the opinion of the respondents, but it also allows us to talk about certain trends and common assessments of certain phenomena and processes in the journalistic field. The survey includes questions about freedom of speech and self-censorship in the media environment during the Russian-Ukrainian war. The data collection contains original survey data. The Excel file (.xlsx) is the original file with the respondents' answers in Ukrainian, provided by the Ilko Kucheriv Democratic Initiatives Foundation. The documentation includes the questions and answer options of the original questionnaire in Ukrainian and English. Additionally, the data collection contains the "Summary" file, which is an analytical report prepared by the Ilko Kucheriv Democratic Initiatives Foundation and the Human Rights Centre ZMINA. The report uses data from an expert survey of journalists in 2019 and 2023, and the results of focus groups in 2022.
Dataset - SciVisContest - Materials Discovery Challenge - version 2025
<p>This the updated version of the dataset that is made available for the SciVisContest Materials Discovery Challenge.</p> <p>The data provided for this challenge was generated for the specific use case of developing a new Al-based alloy suitable for additive manufacturing by blending different available aluminum metal scrap such as automotive Al-Si piston alloys and other alloys from different sectors. Different alloy designs were initially generated based on mixing ratios between available scrap alloys. The CALPHAD method was used to perform equilibrium and non-equilibrium calculations to predict relevant thermo-physical and mechanical variables such as the content of volatile elements like Mg and Zn, phase formation and their fractions, solidification intervals, thermo-physical parameters, yield strength and hot crack sensitivity for each alloy composition.</p>
[ITU AI/ML Challenge 2021] Dataset IEEE 802.11ax Spatial Reuse
<p>This dataset has been created for the problem statement ITU-ML5G-PS-004 of the ITU AI/ML Challenge (2021 edition). More information can be found here: <a href="https://challenge.aiforgood.itu.int/">https://challenge.aiforgood.itu.int/</a> and <a href="https://www.upf.edu/web/wnrg/2021-edition">https://www.upf.edu/web/wnrg/2021-edition</a>. </p> <p>The dataset contains the information of 3.000 IEEE 802.11ax deployments (divided into two different scenarios) at which the Basic Service Set (BSS) of interest applies different possible OBSS/PD thresholds in the context of the Spatial Reuse (SR) operation. In total, 21 OBSS/PD values are considered for each deployment, and some of the deployments include data from different STA locations. The provided files are expected to be used for training Machine Learning (ML) and Federated Learning (FL) algorithms.</p> <p>More specifically, the dataset is divided as follows:</p> <ul> <li><strong>Scenario 1:</strong> 1,000 different deployments with 2-6 APs and 1 STA per AP. A minimum distance limitation is applied, so that each AP different from AP_A is located at a minimum distance of 10 meters from that one. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/output_11ax_sr_simulations_sce1.txt">output_11ax_sr_simulations_sce1.txt</a>: contains the output generated by the simulator for the deployments in Scenario 1.</li> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/simulator_input_files_sce1.zip">simulator_input_files_sce1.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 1.</li> </ol> </li> <li><strong>Scenario 2: </strong>1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/output_11ax_sr_simulations_sce2.txt">output_11ax_sr_simulations_sce2.txt</a>: contains the output generated by the simulator for the deployments in Scenario 2.</li> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/simulator_input_files_sce2.zip">simulator_input_files_sce2.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 2.</li> </ol> </li> <li><strong>Scenario 3: </strong>1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax, and for up to 20 different locations of different STAs of the BSS of interest ("BSS_A"). Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/output_11ax_sr_simulations_sce3.txt">output_11ax_sr_simulations_sce3.txt</a>: contains the output generated by the simulator for the deployments in Scenario 3.</li> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/simulator_input_files_sce3.zip">simulator_input_files_sce3.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 3.</li> </ol> </li> <li><strong>Test:</strong> 1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. A random OBSS/PD threshold is applied. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/22d1265c-0dea-4bf8-a223-e61c45deb076/output_11ax_sr_simulations_test.txt?versionId=91ccbe70-a821-4601-8db7-1763883dc984">output_11ax_sr_simulations_test.txt</a>: contains the output generated by the simulator for the evaluation deployments. The label (throughput) has been replaced with "0s".</li> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/simulator_input_files_test.zip?versionId=b1581c0b-419c-422b-bb2d-4dbf77f05dd2">simulator_input_files_test.zip</a>: contains the input files used by the simulator to simulate the evaluation deployments.</li> </ol> </li> </ul>
Helsinki Deblur Challenge 2021 test dataset
<p>This dataset was primarily designed and captured to be used for the testing part of the Helsinki Deblur Challenge 2021 but it can be used for any testing and benchmarking purposes of image deblurring algorithms.</p> <p>The dataset contains photographs of random strings of text (including numbers), natural images and QR codes with varying levels of blur caused by misfocusing the camera. Each photo has both a blurred and sharp version.</p> <p>The images are split into 4 separate zip files each having 5 steps of different blur. Altogether there are 20 steps. Each one of the zip files contains two folders, one folder named CAM1_focused (Camera 1) with the sharp images and one named CAM2_blurred (Camera 2) with the blurred images. For each step there are 40 images of text character targets, 15 natural image targets, one QR-code target and 3 images of technical targets. Each one of the text target images is accompanied by a text file (same file name but .txt extension) containing the correct transcription of that particular text target.</p> <p>As a difference to the HDC training data (<a href="https://doi.org/10.5281/zenodo.4916176">https://doi.org/10.5281/zenodo.4916176</a>) the test data character targets include also numbers. </p> <p>A detailed description of the training dataset that was acquired in the exact same way as this test dataset can be found here: <a href="http://arxiv.org/abs/2105.10233">http://arxiv.org/abs/2105.10233</a></p> <p>Here is a link to the official webpage of the Helsinki Deblur Challenge 2021: <a href="https://www.fips.fi/HDC2021.php">https://www.fips.fi/HDC2021.php</a></p>
Reducing False Arrhythmia Alarms in the ICU - The PhysioNet Computing in Cardiology Challenge 2015
<p>This dataset is part of the available dataset for <em>The PhysioNet Computing in Cardiology Challenge 2015</em>, available at https://www.physionet.org/content/challenge-2015/1.0.0/training.zip (last accessed today 2021-03-24).</p> <p>The dataset is licensed under GNU GPL license Version 3:</p> <p>Permissions:</p> <ul> <li>Commercial use</li> <li>Distribution</li> <li>Modification</li> <li>Patent use</li> <li>Private use</li> </ul> <p>Conditions:</p> <ul> <li>Disclose source</li> <li>License and copyright notice</li> <li>Same license</li> <li>State changes</li> </ul> <p>Limitations:</p> <ul> <li>Liability</li> <li>Warranty</li> </ul> <p>For more information about the license, check: https://choosealicense.com/licenses/gpl-3.0/</p> <p>The following modifications were made:</p> <ul> <li>Only the targets folder is used</li> <li>Only the files *.hea and *.mat are used, being the latter converted to CSV and compressed in .bz2 format</li> <li>Added the LICENSE.txt file as required.</li> </ul>
Model Checkpoints for AE Studio's AESMTE3 Submission to NLB 2021 Challenge
<p>This dataset contains all model checkpoints acquired while training <a href="https://ae.studio/">AE Studio</a>'s AESMTE3 submission for the <a href="https://neurallatents.github.io/">NLB 2021 Challenge</a>. The models are <a href="https://github.com/snel-repo/neural-data-transformers">neural-data-transformers</a> and were trained using AE's <a href="https://github.com/agencyenterprise/ae-nlb-2021">fork</a> of the neural-data-transformers repo.</p> <p>These model checkpoints are intended to be used by the NLB organizers in order to validate AE's submission.</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.