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Models for "A data-driven approach to studying changing vocabularies in historical newspaper collections"
<p>NOTE: This is a badly rendered version of the README within the archive.</p> <p><strong>A data-driven approach to studying changing vocabularies in historical newspaper collections</strong></p> <p>Simon Hengchen,* Ruben Ros,** Jani Marjanen,*** Mikko Tolonen***</p> <p>*<a href="https://spraakbanken.gu.se/en/about/staff/simon">Språkbanken Text</a>, University of Gothenburg, Sweden and <a href="https://iguanodon.ai">iguanodon.ai</a>, Belgium: firstname.lastname@gu.se<br> **<a href="https://www.c2dh.uni.lu/people/ruben-ros">Centre for Contemporary and Digital History (C2DH)</a>, University of Luxembourg: firstname.lastname@uni.lu<br> ***<a href="https://www.helsinki.fi/en/researchgroups/computational-history">COMHIS</a>, University of Helsinki: <a href="mailto:firstname.lastname@helsinki.fi">firstname.lastname@helsinki.fi</a>;</p> <p>These are the supplementary materials for the DH2019 paper <em>A data-driven approach to the changing vocabulary of the ‘nation’ in English, Dutch, Swedish and Finnish newspapers, 1750-1950</em>, as well as the 2021 Digital Scholarship in the Humanities publication available in OpenAccess: <a href="https://academic.oup.com/dsh/article/36/Supplement_2/ii109/6421793">https://academic.oup.com/dsh/article/36/Supplement_2/ii109/6421793</a>. If you end up using whole or parts of this resource, please use the following citation(s):</p> <ul> <li>Hengchen, S., Ros, R., and Marjanen, J. (2019). A data-driven approach to the changing vocabulary of the 'nation' in English, Dutch, Swedish and Finnish newspapers, 1750-1950. In <em>Proceedings of the Digital Humanities (DH) conference 2019, Utrecht, The Netherlands</em></li> </ul> <p>and/or:</p> <ul> <li>Hengchen, S., Ros, R., Marjanen, J. and Tolonen, M., 2021. A data-driven approach to studying changing vocabularies in historical newspaper collections. Digital Scholarship in the Humanities, 36(Supplement_2), pp.ii109-ii126.</li> </ul> <p>or alternatively use one of the following <code>bib</code>s:</p> <pre><code>@inproceedings{hengchen2019nation, title="A data-driven approach to the changing vocabulary of the 'nation' in {E}nglish, {D}utch, {S}wedish and {F}innish newspapers, 1750-1950.", author={Hengchen, Simon and Ros, Ruben and Marjanen, Jani}, year={2019}, address = "Utrecht, The Netherlands", booktitle={Proceedings of the Digital Humanities (DH) conference 2019} }</code></pre> <pre><code>@article{hengchen2021data, title={A data-driven approach to studying changing vocabularies in historical newspaper collections}, author={Hengchen, Simon and Ros, Ruben and Marjanen, Jani and Tolonen, Mikko}, journal={Digital Scholarship in the Humanities}, volume={36}, number={Supplement\_2}, pages={ii109--ii126}, year={2021}, publisher={Oxford University Press} }</code></pre> <p> </p> <p>Files</p> <p>This archive contains two folders -- one per diachronic representation method -- as well as this README. The folders each contain four folders, which contain the models for their respective languages. As can be inferred from the small datasize, most of the earlier models are not reliable and should not be used, but are still made available. This work is licensed under a <a href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.</p> <p><strong>Source material</strong></p> <p>Finnish:</p> <p>The models were created with data from the Finnish Sub-corpus of the Newspaper and Periodical Corpus of the National Library of Finland (National Library of Finland, 2011). We used everything in the corpus.</p> <p>Filesizes:</p> <pre><code>[simon@taito-login3 SGNS]$ du -h fi* 12M fi_1820_SGNS_corpus_file.gensim 89M fi_1840_SGNS_corpus_file.gensim 797M fi_1860_SGNS_corpus_file.gensim 7.0G fi_1880_SGNS_corpus_file.gensim 22G fi_1900_SGNS_corpus_file.gensim</code></pre> <p>Swedish:</p> <p>The models were created with data from the Kubhist 2 corpus (Språkbanken) -- more precisely, the data dumps available at <a href="https://spraakbanken.gu.se/lb/resurser/meningsmangder/">https://spraakbanken.gu.se</a>. After a manual evaluation of Swedish embeddings trained without pre-processing seemed to show that the embeddings were of low quality, we retrained models, only keeping sentences that were at least 10 tokens long and were constituted of at least 50% of lemmas as per the KORP processing pipeline (Borin et al, 2012).</p> <p>Filesizes:</p> <pre><code>[simon@taito-login3 SGNS]$ du -h sv* 1.6M sv_1740_SGNS_corpus_file.gensim 44M sv_1760_SGNS_corpus_file.gensim 124M sv_1780_SGNS_corpus_file.gensim 228M sv_1800_SGNS_corpus_file.gensim 678M sv_1820_SGNS_corpus_file.gensim 1.6G sv_1840_SGNS_corpus_file.gensim 4.5G sv_1860_SGNS_corpus_file.gensim 6.5G sv_1880_SGNS_corpus_file.gensim 113M sv_1900_SGNS_corpus_file.gensim</code></pre> <p>Dutch:</p> <p>The models were created with data from the Delpher newspaper archive (Royal Dutch Library, 2017), through data dumps for newspapers until and including 1876, and through API hits for articles from 1877 to 1899 (included).</p> <ul> <li>For anything pre-1877 we discarded full texts that had, in the metadata, anything else than exclusively <code>nl</code> or <code>NL</code> as a language tag.</li> <li>For the full texts between 1877 and 1899: we queried the API for all items in the “artikel” category that contained the determiner <code>de</code>.</li> </ul> <p>Our assumption was that most articles should contain <code>de</code> at least once, and those that didn't were too short to be deemed interesting. A subsequent study showed that was not exactly the case, but we were reassured by the fact that left-out articles were probably "shipping or financial reports" (thanks go to Melvin Wevers). We also did not include the colonial newspapers for our embeddings. This is motivated by our research questions. A list of removed newspapers is available on request.</p> <p>Filesizes:</p> <pre><code>[simon@taito-login3 SGNS]$ du -h nl* 6.8M nl_1620_SGNS_corpus_file.gensim 7.9M nl_1640_SGNS_corpus_file.gensim 43M nl_1660_SGNS_corpus_file.gensim 78M nl_1680_SGNS_corpus_file.gensim 138M nl_1700_SGNS_corpus_file.gensim 243M nl_1720_SGNS_corpus_file.gensim 287M nl_1740_SGNS_corpus_file.gensim 431M nl_1760_SGNS_corpus_file.gensim 825M nl_1780_SGNS_corpus_file.gensim 1.2G nl_1800_SGNS_corpus_file.gensim 1.8G nl_1820_SGNS_corpus_file.gensim 3.1G nl_1840_SGNS_corpus_file.gensim 5.2G nl_1860_SGNS_corpus_file.gensim 13G nl_1880_SGNS_corpus_file.gensim</code></pre> <p>English:</p> <p>The models were created with data from the British Library Newspapers collection (<a href="https://www.gale.com/intl/primary-sources/british-library-newspapers%5D">link</a>), the Nichols collection (<a href="https://www.gale.com/intl/c/17th-and-18th-century-burney-newspapers-collection">link</a>), and the Burney collection (<a href="https://www.gale.com/intl/c/17th-and-18th-century-nichols-newspapers-collection">link</a>). We used everything in the corpora. For English, only SGNS_ALIGN models are available. We thank Gale Cengage for their help with this project.</p> <p>Filesizes:</p> <pre><code>[simon@taito-login3 SGNS]$ du -h en* 4.3M en_1620_SGNS_corpus_file.gensim 11M en_1640_SGNS_corpus_file.gensim 11M en_1660_SGNS_corpus_file.gensim 106M en_1680_SGNS_corpus_file.gensim 409M en_1700_SGNS_corpus_file.gensim 1.7G en_1720_SGNS_corpus_file.gensim 834M en_1740_SGNS_corpus_file.gensim 2.4G en_1760_SGNS_corpus_file.gensim 5.3G en_1780_SGNS_corpus_file.gensim 5.5G en_1800_SGNS_corpus_file.gensim 15G en_1820_SGNS_corpus_file.gensim 42G en_1840_SGNS_corpus_file.gensim 65G en_1860_SGNS_corpus_file.gensim 88G en_1880_SGNS_corpus_file.gensim 26G en_1900_SGNS_corpus_file.gensim 21G en_1920_SGNS_corpus_file.gensim 6.3G en_1940_SGNS_corpus_file.gensim</code></pre> <p><strong>Word embeddings</strong></p> <p>For every language, we train diachronic embeddings as follows. We divide the data in 20-year time bins. We train SGNS_UPDATE and SGNS_ALIGN models. Current research on German (Schlechtweg et al, 2019) and English (Shoemark et al, 2019) indicates you should use the SGNS_ALIGN models. <strong>For EN, FI, NL, no tokens (including punctuation) were removed nor altered, aside from lowercasing</strong>. For SV, see above. Parameters are as follows: SGNS architecture (Mikolov et al 2013), window size of 5, frequency threshold of 100, 5 epochs, 300 dimensions (or 100 for EN).</p> <ul> <li>For SGNS_UPDATE: We first train a model for the first time bin <code>t</code>. To train the model for <code>t+1</code>, we use the <code>t</code> model to initialise the vectors for <code>t+1</code>, set the learning rate to correspond to the end learning rate of <code>t</code>, and continue training. This approach, closely following Kim et al (2014), has the advantage of avoiding the need for post-training vector space alignment.</li> </ul> <p>The Python snippet below, which makes use of gensim (Rehurek and Sojka, 2010), illustrates the approach. Special thanks go to Sara Budts.</p> <pre><code>## dict_files[key] is a dictionary with double decades as keys and a corresponding LineSentence object as value: https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.LineSentence count = 0 for key in sorted(list(dict_files.keys())): if count == 0: ## This is the first model. model = gensim.models.Word2Vec(corpus_file=dict_files[key], min_count=100, sg=1 ,size=300, workers=64, seed=1830, iter=5) model.save(os.path.join(data_path_final,"KIM",lang+"_"+str(timebin)+".w2v")) print("Model saved, on to the next\n") count += 1 if count > 0: ## this is for the subsequent models. print("model for double decade starting in",str(key)) model = gensim.models.Word2Vec.load(os.path.join(data_path_final,"KIM",lang+"_"+str(timebin-20)+".w2v")) print("previous model loaded") model.build_vocab(corpus_file=dict_files[key], update=True) model.train(corpus_file=dict_files[key], total_words = model.corpus_count, total_examples = model.corpus_count, start_alpha = model.alpha, end_alpha = model.min_alpha, epochs=model.epochs) model.save(os.path.join(data_path_final,"KIM",lang+"_"+str(timebin)+".w2v")) </code></pre> <ul> <li>For SGNS_ALIGN: We independently train models for all time bins. The models in this repository are <em>NOT</em> aligned, leaving you the choice of how to align them. For example, <a href="https://gist.github.com/quadrismegistus/09a93e219a6ffc4f216fb85235535faf">here</a> is a link to code by Ryan Heuser to do just that. Models were trained with the <code>count == 0</code> scenario in the snippet above.</li> </ul> <p><strong>Acknowledgments</strong></p> <p>This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant 770299 <a href="https://www.newseye.eu/">NewsEye</a>. Specials thanks go to the data providers/collection-holding institutions: the Finnish Language Bank, the Swedish Language Bank, the Royal Dutch Library, and Gale Cengage.</p> <p>The authors would like to thank the following persons and group, listed alphabetically: Antoine Doucet, Antti Kanner, Axel-Jean Caurant, Dominik Schlechtweg, Eetu Mäkelä, Elaine Zosa, Estelle Bunout, Haim Dubossarsky, Joris van Eijnatten, Krister Lindén, Lars Borin, Lidia Pivovarova, Melvin Wevers, Nina Tahmasebi, Sara Budts, Senka Drobac, Tanja Säily, the COMHIS group, and Steven Claeyssens. Computational resources were provided by CSC – IT Center for Science Ltd.</p> <p><strong>References</strong></p> <p>Borin, L., Forsberg, M., Roxendal, J. (2012). Korp-the corpus infrastructure of Spräkbanken,in: LREC. pp. 474–478.</p> <p>Kim, Y., Chiu, Y.I., Hanaki, K., Hegde, D. and Petrov, S. (2014). Temporal Analysis of Language through Neural Language Models. <em>ACL 2014</em>, p.61.</p> <p>Mikolov, T., Chen, K., Corrado, G. and Dean, J. (2013). Efficient estimation of word representations in vector space. <em>arXiv preprint arXiv:1301.3781</em>.</p> <p>National Library of Finland (2011). <em>The Finnish Sub-corpus of the Newspaper and Periodical Corpus of the National Library of Finland, Kielipankki Version</em> [text corpus]. Kielipankki. Retrieved from <a href="http://urn.fi/urn:nbn:fi:lb-2016050302">http://urn.fi/urn:nbn:fi:lb-2016050302</a>.</p> <p>Rehurek, R. and Sojka, P. (2010). Software framework for topic modelling with large corpora. In <em>Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks</em>.</p> <p>Royal Dutch Library (2017). <em>Delpher open krantenarchief (1.0)</em>. Den Haag, 2017.</p> <p>Schlechtweg D., Hätty A, del Tredici M., and Schulte im Walde S. (2019). A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains. In <em>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</em>, Florence, Italy. ACL.</p> <p>Shoemark, P., Liza, F.F., Nguyen, D., Hale, S. and McGillivray, B. (2019). Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings. In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 66-76)</em>, Hong Kong.</p> <p>Språkbanken. <em>The Kubhist Corpus</em>. Department of Swedish, University of Gothenburg. <a href="https://spraakbanken.gu.se/korp/?mode=kubhist">https://spraakbanken.gu.se/korp/?mode=kubhist</a>.</p>
Analysing the data-driven approach of dynamically estimating positioning accuracy (data)
<p>The train/validation/test sets used in the study <strong>"<em>Analysing the data-driven approach of dynamically estimating positioning accuracy</em>"</strong>.</p> <p>Preprint: <a href="https://arxiv.org/abs/2011.10478">https://arxiv.org/abs/2011.10478</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/9500369">https://ieeexplore.ieee.org/document/9500369</a></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>The full dataset used in this study, is the public dataset <em>lorawan_dataset_antwerp.csv </em> (and its related file <em>lorawan_antwerp_gateway_locations.json)</em> which can be access here:</p> <pre><a href="https://zenodo.org/record/3904158#.X4_h7y8RpQI">https://zenodo.org/record/3904158#.X4_h7y8RpQI</a></pre> <p>The above link is related to the following publication, in which the original dataset was published:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen; and Weyn, Maarten.</p>
Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort
<p># Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort" - Rev #1</p> <p>This is the dataset for the paper titled "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort".</p> <p>In case of questions, feel free to contact the authors, *anonymised*, ORCID: https://orcid.org/*anonymised*, current affiliation and email: *anonymised*</p> <p>## Survey 2019 ##<br> The raw survey data for the initial 2019 survey is available in the file *survey2019_anon.csv*. Note that the data is anonymised as free-text comments have been removed. Explanations on the variables and their levels are given in the files *variables_survey2019.csv* and *values_survey2019.csv*.<br> The questionnaire for the 2019 survey is contained in *survey2019_instrument.pdf*.</p> <p>## Survey 2020 ##<br> The raw survey data for the 2020 survey is available in the file *rdata_anon_survey2020.csv*. Additional scripts are supplied to reproduce the exploratory factor analysis. The main entry is the file *EFA.R*, which imports the data. The file contains some comments on the process.<br> The questionnaire for the 2020 survey is contained in *survey2020_instrument.pdf*.</p> <p>## Interviews ##<br> The interview guide used for the five interviews is available in the file *interview_instrument.pdf*.</p>
Scanning the horizon for invasive plant threats using a data-driven approach
<p>This repository holds the data and code for the manuscript "Scanning the horizon for invasive plant threats using a data-driven approach". </p> <p><strong>Contents</strong></p> <ul> <li>code: descriptions below</li> <li>data: descriptions below</li> <li>intermediate-data: datasets produced by processing original data (see code) or produced through horizon scan process (descriptions below)</li> <li>fl-plants-horizon-scan.Rproj: RStudio project for running R scripts</li> </ul> <p> </p> <table> <thead> <tr> <th scope="col">code</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>gcw_processing.R</td> <td>R script to format data downloaded from the Global Compendium of Weeds</td> </tr> <tr> <td>native_introduced_ranges.R</td> <td>R script to create map of native and introduced ranges of taxa on the final list</td> </tr> <tr> <td>random_draws_plant_families.R</td> <td>R script to evaluate over- and underrepresentation of plant families in initial and final list</td> </tr> <tr> <td>review_process_comparison.R</td> <td>R script to evaluate differences in scores before and after peer-review and consensus-building</td> </tr> <tr> <td>scores_certainty_pathways.R</td> <td>R script to create figures of scores, certainty, and pathways for final list</td> </tr> <tr> <td>risk_scores_analys.R</td> <td>R script to evaluate final risk scores</td> </tr> <tr> <td>pathways_process.R</td> <td>R script to process pathways to introduction data</td> </tr> <tr> <td>taxa_list_processing.R</td> <td>R script to create list used for rapid risk assessments from an initial list</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th scope="col">data</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>cab_list_full.csv</td> <td>list of potential invasive species to Florida generated by CABI Horizon Scan Tool on November 15, 2019</td> </tr> <tr> <td>GCW_full_list_020420.csv</td> <td>Global Compendium of Weeds downloaded on February 4, 2020</td> </tr> <tr> <td>PlantAtlasDataExport-20191211-194219.csv</td> <td>Atlas of Florida plants downloaded December 11, 2019</td> </tr> <tr> <td>Taxon_x_List_GloNAF_vanKleunenetal2018Ecology_121119.csv</td> <td>GloNAF 1.2 database downloaded December 11, 2019</td> </tr> <tr> <td>the-plant-list</td> <td>The Plant List Database downloaded August 3, 2021</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th scope="col">intermediate-data</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>federal_noxious_weed_list.csv</td> <td>manually formatted version of the USDA Federal Noxious Weed List downloaded March 16, 2020</td> </tr> <tr> <td>first_round_assessments_050120.csv</td> <td>rapid risk assessments for horizon scan pre-peer-review</td> </tr> <tr> <td>fl_prohibited_plants.csv</td> <td>manually compiled list of prohibited plants in Florida based on the Florida Noxious Weed List, Florida Prohibited Plants list, and Florida Invasive Species Council (all downloaded March 9, 2020)</td> </tr> <tr> <td>horizon_scan_plants_full_reviews_080321.csv</td> <td>rapid risk assessments for horizon scan post-peer-review and consensus-building</td> </tr> </tbody> </table> <p> </p>
Dataset supporting the paper: Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach
<p>The necessary image files for the paper titled "Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach"</p>
Data and Code for "Quantifying spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach"
<p>This is a zipped file of all associated code and data for the submitted paper entitled "Quantifying spatio-temporal risk of Harmful Algal Blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach".</p>
Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures
<p>This dataset comes from the following paper:</p> <p>Chiara Pasini, Oscar Ramponi, Stefano Pandini, Luciana Sartore, Giulia Scalet, Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures, J. of Materi Eng and Perform, 2024. <a href="https://doi.org/10.1007/s11665-024-10199-x">https://doi.org/10.1007/s11665-024-10199-x</a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. m of the neural network</li> <li>. inp of the Abaqus finite element simulations</li> </ul>
Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
<p>20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.</p>
Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews
<p>6000 French user reviews from three applications on Google Play (Garmin Connect, Huawei Health, Samsung Health) are labelled manually. We selected four labels: rating, bug report, feature request and user experience.</p> <ul> <li><strong>Ratings</strong> are simple text which express the overall evaluation to that app, including praise, criticism, or dissuasion.</li> <li><strong>Bug reports</strong> show the problems that users have met while using the app, like loss of data, crash of app, connection error, etc.</li> <li><strong>Feature requests</strong> reflect the demande of users on new function, new content, new interface, etc.</li> <li>In <strong>user experience</strong>, users describe their experience in relation to the functionality of the app, how does certain functions be helpful.</li> </ul> <p>As we can observe from the following table, that shows examples of labelled user reviews, each review belongs to one or more categories.</p> <table> <tbody> <tr> <th>App</th> <th>Total</th> <th>Rating</th> <th>Bug report</th> <th>Feature request</th> <th>User experience</th> </tr> </tbody> <tbody> <tr> <td>Garmin Connect</td> <td>2000</td> <td>1260</td> <td>757</td> <td>170</td> <td>493</td> </tr> <tr> <td>Huawei Health</td> <td>2000</td> <td>1068</td> <td>819</td> <td>384</td> <td>289</td> </tr> <tr> <td>Samsung Health</td> <td>2000</td> <td>1324</td> <td>491</td> <td>486</td> <td>349</td> </tr> </tbody> </table> <p> </p> <h2>New Dataset</h2> <p>Based on this dataset, we developed a labeled dataset containing 6,000 English and 6,000 French reviews for classification, as well as 1,200 bilingual reviews for clustering. The new dataset has been made publicly available on Zenodo at the following link: <a href="../records/11066414">https://zenodo.org/records/11066414</a></p>
name lists for 'Detecting intersectionality in NER models: A data-driven approach'
<p>Name lists used for data augmentation for testing biases (in terms of error disparities) of Name Entity Recognition in Danish NLP pipelines.</p> <p>The following lists are from <a href="https://www.dst.dk/da/Statistik/emner/borgere/navne/navne-i-hele-befolkningen">Statistics Denmark</a>:</p> <ul> <li>majority_first_names_2023_men.csv</li> <li>majority_first_names_2023_women.csv</li> <li>majority_last_names_2023.csv</li> </ul> <p>The following lists are from Eva Villarsen Meldgaard. 2005. <a href="https://nors.ku.dk/publikationer/webpublikationer/muslimske_fornavne/">Muslimske fornavne i danmark</a>. Publisher: Københavns Universitet</p> <ul> <li>minority_first_names_men.csv</li> <li>minority_first_names_men.csv</li> </ul> <p>The list majority_unisex_names.csv is retrieved from <a href="https://familieretshuset.dk/navne/navne/godkendte-fornavne">The Agency of Family Law</a> in Denmark, and the numbers are retrieved from the above lists from Statistics Denmark.</p> <p>The list minority_last_names.csv is retrieved from <a href="https://www.familyeducation.com/baby-names/surname/origin/muslim">FamilyEducation</a>.</p> <p>The list overlapping_names.csv contains first names, which both occur on the list of majority names and the list of minority names.</p>
A data-driven supervised machine learning approach to estimating global ambient air pollution concentrations with associated prediction intervals
Open the record for dataset details and reuse information.
Dataset for Manthan: A Data Driven Approach for Boolean Function Synthesis (CAV-20 paper)
<p>The data set consists of 609 benchmarks used for experimental evaluation of <a href="https://arxiv.org/abs/2005.06922">paper</a> titled 'Manthan: A Data-Driven Approach for Boolean Function Synthesis" published in International Conference of Computer-Aided Verification (CAV), 2020 . The benchmarks are from following sources:</p> <ul> <li>Prenex-2QBF track of<a href="http://www.qbflib.org/qbfeval17.php"> QBFEval-17</a> and <a href="http://www.qbflib.org/qbfeval18.php">QBFEval-18</a></li> <li>Arithmetic set (Srivastava, Gulwani, Foster 2013).</li> <li>Disjunctive set (Akshay, Charkraborty, John, Shah 2017).</li> <li>Factorization set (Akshay, Charkraborty, John, Shah 2017).</li> </ul>
The cognitive and perceptual correlates of ideological attitudes: a data-driven approach
<p>Data and analysis scripts for: </p> <p>Zmigrod L, Eisenberg IW, Bissett PG, Robbins TW, Poldrack RA. 2021 The cognitive and perceptual correlates of ideological attitudes: a data-driven approach. Phil. Trans. R. Soc. B 20200424. https://doi.org/10.1098/rstb.2020.0424</p>
Supplementary data for "The mechanistic functional landscape of Retinitis Pigmentosa: an ML-driven approach to drug repurposing"
<p>Supplementary data for "The mechanistic functional landscape of Retinitis Pigmentosa: an ML-driven approach to drug repurposing"</p><p> </p><p>version: 10.5281/zenodo.10203479</p><ul><li>added missing file: "drug_actions_withSimplAction.csv"</li></ul>
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties
<p>This repository contains materials for the paper, "Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties", published in <a href="https://onlinelibrary.wiley.com/doi/10.1002/minf.202300249">Molecular Informatics.</a></p> <p>`data.zip` contains vocabulary and dataset files for identifying chemical vocabularies and key chemical words associated with protein ligand binding. </p> <p>`results.zip` comprises outputs specific to vocabularies and datasets, as well as various related statistics.</p> <p> </p> <p> </p>
Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach - Data Supplement
<p>This contribution contains data and analysis scripts for the manuscript "Projecting climate and socioeconomic contributions to global flood-induced displacements using a data-driven approach" by Lenin Del Rio Amador, Mathieu Boudreault and David A. Carozza.</p>
Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
<p> </p> <div> <div><a href="https://arxiv.org/search/cs?searchtype=author&query=Tello,+A">Andres Tello*</a><em>, </em><a href="https://arxiv.org/search/cs?searchtype=author&query=Truong,+H">Huy Truong*</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lazovik,+A">Alexander Lazovik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Degeler,+V">Victoria Degeler</a>. Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks. Engineering Proceedings. 2024; 69(1):50. <a href="https://doi.org/10.3390/engproc2024069050">https://doi.org/10.3390/engproc2024069050</a></div> <br> <div>(*) Both authors contributed equally.<br><br></div> <h2>Update</h2> <div>(04/09/2024): Citation is updated.<br>We have added headers for CSVs and auxiliary data (duration time, edge list, ordered names.. ) in the configuration file (JSON format). As such, corresponding INP files can be omitted when working with this version. <br>The EXN network has been included in this version, so the total number of processed networks is 11.<br>For more details, please read ZENODO_README.md.</div> <h2>Contact</h2> <div>For dataset-related questions: <a href="mailto:h.c.truong@rug.nl" target="_blank" rel="noopener">Huy Truong</a></div> <br> <div>For data acquisition: <a href="mailto:a.tello@rug.nl" target="_blank" rel="noopener">Andres Tello</a></div> <br> <div>If you use this dataset, please cite:</div> <blockquote>@article{tello2024largescale,<br> AUTHOR = {Tello, Andrés and Truong, Huy and Lazovik, Alexander and Degeler, Victoria},<br> TITLE = {Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks},<br> JOURNAL = {Engineering Proceedings},<br> VOLUME = {69},<br> YEAR = {2024},<br> NUMBER = {1},<br> ARTICLE-NUMBER = {50},<br> URL = {https://www.mdpi.com/2673-4591/69/1/50},<br> ISSN = {2673-4591},<br> DOI = {10.3390/engproc2024069050}<br>}</blockquote> </div>
Characterizing the spatial correlation of coseismic slip distributions: A data driven Bayesian approach
<p>Slip models for the simulated case and the Illapel earthquake are provided. The zip file contains processed data, predictions, and uncertainty estimates for the Illapel event.</p>
Research data supporting: "A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields"
<p>This repository contains the data used in the paper of Capelli <em>et al. </em>"A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields", published on Journal of Physical Chemistry B (DOI: 0.1021/acs.jpcb.1c02503).<br> <br> The archive traj_processed.tar.gz contains the trajectories converted in xyz format with the dimensions of the box.</p> <p>The archive trajectories_xtc.tar.gz contains the raw trajectories (of the membranes without solvent) in gromacs xtc format with a .tpr binary file. <br> </p>
Data for "Light Curves of Type IIP Supernovae from Neutrino-driven Explosions of Red Supergiants Obtained by a Semi-analytic Approach"
<p>Data in the form of Table 1 in the accompanying paper "Light Curves of Type IIP Supernovae from Neutrino-driven Explosions of Red Supergiants Obtained by a Semi-analytic Approach" at arXiv.org</p> <p>See the README file on the usage</p>
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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.