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724 results for “german”
Broad-Coverage German Sentiment Classification Model and Dataset for Dialog Systems
<p><a href="http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf"><strong>Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems</strong></a></p> <p>This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper.</p> <p>You can find the code for training testing the models, that was published along with the paper in this <a href="https://github.com/oliverguhr/german-sentiment">repository</a>.</p> <p>The <a href="https://github.com/oliverguhr/german-sentiment-lib"><em>germansentiment</em></a> Python package contains a easy to use interface for the model that was published with this paper.</p> <p> </p> <p> </p>
German Lombard conversation recordings
<p>These sound files contain conversations of three actors (one female and two male speakers) sitting in a cafeteria, simulating a daily life conversation of three students about six different topics in German. Lombard speech, where speaking in a noisy environment leads to an elevated amplitude and frequencies [1], was generated by providing the speakers with headphones with background noise during the recordings.</p> <p>These recordings can be used as speech material in hearing studies, for example. The recordings were used in [2].</p> <p>Files</p> <p>Lombard_conversations_script.pdf: Script of the conversations</p> <p>Wav-files (three speakers in three channels):</p> <p>bg_story_1.wav: Studying for exams</p> <p>bg_story_2.wav: Leisure activities and appointment at a restaurant</p> <p>bg_story_3.wav: Semester break plans and jobs</p> <p>bg_story_4.wav: Sports</p> <p>bg_story_5.wav: Pets</p> <p>bg_story_6.wav: Weather and appointment for a bike tour</p> <p>*.license: License abbreviation and author information, for use in the acoustic simulation tool TASCAR [3]</p> <p>two_channel_playback.m: Matlab/Octave script to play back the .wav files on two channels</p> <p>two_channel_playback.tsc: TASCAR [3] example scene playing back the .wav files on two channels</p> <p><br> </p> <p>Methods</p> <p>In preparation of the recordings, Sennheiser HDA200 headphones were calibrated with a Brüel & Kjær 4153 artificial ear and a 92 dB calibrator. A sound file containing a background noise recorded in a cafeteria was then calibrated to 75 dB. For the recordings, the background noise was played back by the Sennheiser HDA200 headphones which the speakers were wearing. Additionally, each speaker was equipped with a cardioid microphone and a script of the conversations. Then, the speakers spoke the conversations by improvising the text of the script, and the speech was recorded by the microphones. From the resulting recordings, distracting sounds such as crosstalk from the other speakers, page-flipping or verbal slips and errors were cut out and the three channels were combined and saved in sound files.</p> <p><br> </p> <p>References</p> <p>[1] Junqua, Jean‐Claude. "The Lombard reflex and its role on human listeners and automatic speech recognizers." The Journal of the Acoustical Society of America 93.1 (1993): 510-524.</p> <p>[2] Hendrikse, MME, Eichler, T Grimm, G and Hohmann, V. (in preparation) Interaction of hearing aids with self-motion and the influence of hearing impairment</p> <p>[3] Grimm, Giso; Luberadzka, Joanna; Hohmann, Volker. A Toolbox for Rendering Virtual Acoustic Environments in the Context of Audiology. Acta Acustica united with Acustica, Volume 105, Number 3, May/June 2019, pp. 566-578(13), doi:10.3813/AAA.919337</p>
Pottery from the Northwest Quarter, Jerash, Jordan excavated by the Danish-German Jerash Northwest Quarter Project
<p>This dataset consists of counts and descriptions of ceramic finds and their chronology at the archaeological site of the Northwest Quarter, Jerash, Jordan. It derives from the Danish-German Jerash Northwest Quarter Project led by. Achim Lichtenberger (University of Munster, Germany) and Rubina Raja (Aarhus University, Denmark) between 2011-2018.</p> <p>The folder includes the data analysis script developed by Iza Romanowska.</p> <p>It constitutes the supplementary information for the following publication:</p> <p>Romanowska, I., Lichtenberger, A., Raja, Rubina. 2021 “Trends in Ceramic Assemblages from the Northwest Quarter of Gerasa/Jerash, Jordan.” <em>Journal of Archaeological Science: Reports.</em></p>
A studyforrest extension, an annotation of spoken language in the German dubbed movie ``Forrest Gump'' and its audio-description (annotation)
<p>This dataset contains the annotation of speech spoken in the research cut (Hanke et al. 2014; Hanke et al., 2016) of the movie "Forrest Gump" (Zemeckis, 1994) and its audio-description that was broadcast as an additional audio track (Koop et al., 2009) for visually impaired listeners on Swiss public television. The corresponding paper is hosted on github (https://github.com/psychoinformatics-de/studyforrest-paper-speechannotation) and published in f1000research (https://doi.org/10.12688/f1000research.27621.1).</p>
Bilingual English-German word embedding models for scientific text
<p>This data set contains three word embedding models, constructed from the same training corpus of English and German parallel scientific texts (abstracts and research project descriptions). All text was pre-processed by language-specific stemming with the Porter stemming algorithm, removing numbers, and lower-casing.</p> <p>The first model is a 1000-dimensional Latent Semantic Analysis model, constructed from concatenating the English and German texts. The input data was a m×n (297,852×923,864) document-term matrix of tf-idf weights. This was processed with truncated SVD. There are two files, the word vectors in file lsa_1000_Vmat.csv (the V* term by latent factors matrix of right singular values) and the dimension weights in lsa_1000_d_weights.csv (the 1000 values of the diagonal of the <span class="math-tex">\(\Sigma\)</span> matrix.</p> <p>lsa_1000_Vmat.csv has two fields, the term and its vector representation in LSA space, separated by a "|" character. The structure looks like this:</p> <p>tarifplural|{5.00599733151825e-08,-1.43071379136936e-08,8.32862290483082e-08,-6.08010721687266e-08,1.15831140150142e-07,-2.46470313387358e-08,3.43215595753282e-07,6.24301666802575e-07,-2.62907158945831e-07,-1.04120313981517e-07,4.5864574355164e-07,-2.31799632277312e-07,8.37354377858843e-07,8.22507467711628e-07,4.07585381069368e-07,-4.26358988941922e-08,-8.38652991154651e-07,1.98091851171759e-07,-3.94768548759816e-08,-4.28802181962385e-07, ...}</p> <p>The other two models are a basic Random Indexing and a Reflective Random Indexing model, contained in same file, RI_training.csv. Both models have 1000 dimensions. The data structure is as follows.</p> <ul> <li>language: either "en" (English) or "de" (German), the language of the term</li> <li>term: the term as a character string</li> <li>term_collection_count: integer, number of times the term occurred in the training data</li> <li>c_vector: vector of 1000 reals, RI context vector of the term. formatted like this: "{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12309149,0,0,-0.12309149,0,0,0,0,0,0,0,0,0,0,0,0,0,0, ...}"</li> <li>n_docs: integer, number of different documents which contained the term</li> <li>c_vector_o2: vector of 1000 reals, RRI context vector of the term, formatted like c_vector above</li> </ul> <p>1,034,860 rows.</p> <p>All files are aggressively compressed with GNU gzip and will require much more disk space when uncompressed. Note the special formatting of the vector numeric variables, which are different for the two models.</p>
Geolocation of German Academic Institutions
<p>The dataset has four columns; Name of the institution, Homepage, Latitude, Longitude. </p> <p>The entries in each row are delimited by a semicolon.</p>
Support of Creative Commons Licenses in German disciplinary and institutional open access repositories
<p>Which of the following open content licenses can be chosen for the metadata description of the open access full-texts (apart from the deposit license)? n=81* </p> <p>* This survey question is part of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland, see: http://nbn-resolving.de/urn:nbn:de:kobv:11-100222687 For the research data see: http://doi.org/10.5281/zenodo.10734 </p>
Output of webXray analysis of German library websites
<p>[1] was analyzed with [2]. The deposited files are the result.</p> <p> </p> <p>[1] Steeg, Fabian et al.. (2016). URLs von Webseiten mit Typ Bibliothek aus Lobid.org. Zenodo. 10.5281/zenodo.50969</p> <p>[2] Tim Libert et al.. (2016). webXray: First Release. Zenodo. 10.5281/zenodo.57272</p> <p> </p>
read_dataset_german_konzilsprotokolle
<p>This dataset arises from the READ project (Horizon 2020).<br> <br> Images were provided and enriched under the lead of Dr. Dirk Alvermann (Universitätsarchiv Greifswald - Germany).<br> All in all this dataset contains 8770 trainscribed textlines of handwritten historical documents from the late 18th century.</p> <p>Besides the images and page-files (containing geometric textline information and transcripts), lists dividing the dataset in train and test data are provided (each list element contains the corresponding image, textregion and textline identifiers and therefore an explicit mapping of a list element to a textline is possible). Furthermore sublists of the train list are given.<br> </p>
Data for: Drivers and Barriers for Microservice Adoption in the German Software Industry
<p>Microservices are an architectural style for software which currently receives a lot of attention in both industry and academia. Several companies employ microservice architectures with great success, and there is a wealth of blog posts praising their advantages. Especially so-called Internet-scale systems use them to satisfy their enormous scalability requirements and to rapidly deliver new features to their users.<br> However, microservices are not only popular with large, Internet-scale systems. Many traditional companies are also considering whether microservices are a viable option for their applications. However, these companies may have other motivations to employ microservices, and see other barriers which may prevent them from adopting microservices. Furthermore, these drivers and barriers may differ among industry sectors.<br> This dataset contains the questions and results of a survey on drivers and barriers for microservice adoption among professionals in the German software industry. In addition to overall drivers and barriers, we particularly focused on the use of microservices to modernize existing software, with special emphasis on implications for runtime performance and transactionality.</p>
Indicator for the current and future socio-ecological burden caused by the expansion of wind energy in German districts
<p>The data table lists the calculated present burden (IST-Belastungsgrad) based on the year 2014 and the maximum possible burden (MAX-Belastungsgrad) on the population caused by the further expansion of wind energy. The so-called burden level is calculated accounting for the area occupied by wind turbines, the total area of a district and the population density. Additionally the table holds data on possible future burden levels based on two scenarios for the year 2050. Each district can be identified by its key, "Regionalschlüssel", and corresponding geo data (EPSG: 25832) of the administrative area provided by the Federal Agency for Cartography and Geodesy: © GeoBasis-DE / BKG 2014 (data was changed).</p> <p>The dataset was created in the context of the interdisciplinary research project VerNetzen and is described in detail in the final project report: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 98-118, 143-145.</p> <p><strong><em>Deutsch:</em></strong></p> <p>Die Tabelle enthält u.a. den derzeitigen Belastungsgrad, festgestellt für das Jahr 2014, und den maximal möglichen Belastungsgrad je Landkreis. Der Belastungsgrad ist ein Indikator für die durch den Zubau von Windenergie betroffene Bevölkerung und berechnet sich aus der Gesamtfläche eines Landkreises, der für die Windenergie genutzten Fläche und der Bevölkerungsdichte. In der Tabelle sind ebenfalls mögliche zukünftige Belastungsgrade auf Grundlage zweier Projektszenarien für das Jahr 2050 enthalten. Die jeweiligen Landkreise können mit dem Regionalschlüssel oder den geographischen Daten (EPSG: 25832) des Bundesamtes für Kartographie und Geodäsie zugeordnet werden: © GeoBasis-DE / BKG 2014 (Daten verändert).</p> <p>Der Datensatz ist im Kontext des interdisziplinären Forschungsprojekts VerNetzen entstanden und ist ausführlich im Projektabschlussbericht beschrieben: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S.98-118, S.143-145.</p> <p> </p> <p> </p>
Indicator for the current and future socio-ecological burden caused by the expansion of wind energy in German districts - auxiliary values
<p>The table contains the population and the size of the total area for each German district as of 2013. Furthermore it contains the size of those areas per district, that potentially could be used for wind energy.</p> <p>The data on the population is provided by the Federal Statistical Office and the statistical Offices of the Länder: © Federal Statistical Office and the statistical Offices of the Länder, Regionaldatenbank Deutschland, December 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (data was changed). The total district area is derived from geo data provided by the Federal Agency for Cartography and Geodesy: © GeoBasis-DE / BKG 2014 (data was changed).</p> <p>For further information on potential areas see VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 105-109.</p> <p><em><strong>Deutsch:</strong></em></p> <p>Die Tabelle umfasst die Bevölkerungsanzahl und Flächengröße je deutschem Landkreis für das Jahr 2013. Außerdem ist die Größe jener Fläche angegeben, die potentiell für die Windenergie genutzt werden könnte.</p> <p>Die Bevölkerungszahlen werden von den Statistischen Ämtern des Bundes und der Länder zur Verfügung gestellt: © Statistische Ämter des Bundes und der Länder, Regionaldatenbank Deutschland, Dezember 2014, Datenlizenz by-2-0 (https://www.govdata.de/dl-de/by-2-0) (Daten geändert). Die Landkreisflächen werden auf Grundlage von Geodaten des Bundesamtes für Kartographie und Geodäsie berechnet: © GeoBasis-DE / BKG 2014 (Daten geändert).</p> <p>Für weitere Informationen bzgl. der Potentialflächen siehe VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). <em>VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende</em>. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S. 105-109.</p>
German results from the monitoring of pesticide residues in food
<p>This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.</p> <p>If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL)</p> <p>A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2023 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2022 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2021 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2020 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2019 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2018 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2017 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2016 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2015 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2014 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2013 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2012 - Federal Office of Consumer Protection and Food Safety</p> <p>MOPER_2011 - Federal Office of Consumer Protection and Food Safety</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br>https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Bare Chested Men - German Press Coverage Corpus
<p>A list of articles published in German gaming magazines in the 1980s and 1990s about the following games:</p><ul><li><a href="https://www.mobygames.com/game/1033/death-sword/">Barbarian I</a> (1987)</li><li><a href="https://www.mobygames.com/game/12167/axe-of-rage/">Barbarian II</a> (1988)</li><li><a href="https://research.swissdigitization.ch/?p=613">DragonSlayer</a> (1989, unreleased)</li><li><a href="https://www.mobygames.com/game/54344/torvak-the-warrior/">Torvak the Warrior</a> (1990)</li><li><a href="https://www.mobygames.com/game/6182/conan-the-cimmerian/">Conan the Cimmerian</a> (1991)</li><li><a href="https://www.mobygames.com/game/1618/commando/">Commando</a> (1985)</li><li><a href="https://www.mobygames.com/game/6739/ikari-warriors/">Ikari Warriors</a> (1986)</li><li><a href="https://www.mobygames.com/game/23105/leatherneck/">Leatherneck</a> (1988)</li><li><a href="https://www.mobygames.com/game/16149/dogs-of-war/">Dogs of War</a> (1989)</li></ul>
The German Protest Registrations Dataset
<p>The <strong>German Protest Registrations Dataset</strong> covers protests that have been registered with demonstration authorities in 16 German cities. The data has been compiled from <i>Freedom of Information</i> requests and covers dates, organizers, topics, the number of registered participants, and for some cities the number of observed participants. Covered date ranges vary, with all cities covered in 2022, and 5 cities covered consistently from 2018 to 2022. In comparison to previous datasets that are largely based on newspaper reports, this dataset gives an unprecented level of detail, and is the largest dataset on protest events in Germany to date. <a href="https://github.com/davidpomerenke/german-protest-registrations/releases/download/v1.0.0/report.pdf">The report</a> gives an overview over existing datasets, explains the data retrieval and processing, displays the properties of the dataset, and discusses its limitations. Code and data are available <a href="https://github.com/davidpomerenke/german-protest-registrations">on Github</a>.</p><p>The dataset is provided in three subsets:</p><ol><li>The <strong>2018-2022 dataset</strong> contains only cities that have coverage throughout 2018-2022, and that also have the number of registered participants available. This results in a large dataset (about 40.000 entries) that strikes a balance between regional diversity (5 cities) and time coverage (5 years).</li><li>The <strong>2022 dataset</strong> contains all cities that have data available in 2022, and also have the number of registered participants available. With 13 cities covered but only 12.500 overall entries, it is useful especially for the study of geographic variations.</li><li>The <strong>unfiltered dataset</strong> contains all data without restrictions on included cities and time ranges, overall 57.000 entries. It also contains cities where the number of registered participants is not available. 16 cities are included, with varying time ranges between 2012 and 2022. This dataset should <i>not</i> be used for direct analysis; but it may be used for the creation of alternative consistent sub-datasets similar to the two ones above.</li></ol>
Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen'
<p>Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' consisting of (1) the complete data set of all data analyzed for the project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' ('Data_Brain-IT-Validation-Qmci_for-publication.xlsx'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Supplementary data: "Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario"
<p>This repository contains result data for the paper <i> "Influence of flexibility options on the German transmission grid — A sector-coupled mid-term scenario"</i>.</p><p>The published data includes optimization results of the three main scenarios in the mentioned publication. </p><p>The data for each scenario is stored as csv-files, which allows analysing it with many different tools. In addition, the data can be imported in Python as a network object of the open-source tool PyPSA by using the function <a href="https://pypsa.readthedocs.io/en/latest/api_reference.html#pypsa.Network.import_from_csv_folder">import from csv folder </a>. </p><p> </p><p>The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).</p>
Fig. 41 in Georg Bojung "Scato" Lantzius-Beninga and his contributions on the anatomy of moss capsules: a transliteration from the original German texts
Fig. 41. Transverse section of the base of the peristome of Ceratodon purpureus at xx where it is not yet separated in two cords as it is the case in Fig. 40. Both figures are drawn by a magni- fication x 170.
Fig. 39 in Georg Bojung "Scato" Lantzius-Beninga and his contributions on the anatomy of moss capsules: a transliteration from the original German texts
Fig. 39. Part of a transverse section of peristome teeth of a capsule of Polytrichum urnigerum, magnification x 500.
Fig. 40 in Georg Bojung "Scato" Lantzius-Beninga and his contributions on the anatomy of moss capsules: a transliteration from the original German texts
Fig. 40. Part of a transverse section of Ceratodon purpureus, cut in the middle of the peri- stome, magnification x 170.
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.