Scientific Data最新文献

{"title":"A genus-labeled dataset of individual trees outside forests in Baden-Württemberg, Germany.","authors":"Yulia Grinblat, Heike Tost, Anastasia Benedyk, Andreas Meyer Lindenberg, Somidh Saha, Alexander Zipf, Nir Fulman","doi":"10.1038/s41597-026-08400-y","DOIUrl":"https://doi.org/10.1038/s41597-026-08400-y","url":null,"abstract":"<p><p>Detailed data on individual trees growing outside forests (TOF) are essential for arboriculture, biodiversity assessment, ecosystem services assessment, climate adaptation, and environmental health research. However, existing datasets are often incomplete and seldom provide taxonomic information beyond publicly managed trees in cities of Germany and other countries where maintaining a tree cadaster is a legal requirement. Here, we present for the first time a statewide dataset of individual TOF in the state of Baden-Württemberg, Germany, including tree locations, crown geometry, height, and genus-level labels. We mapped trees from high-resolution multi-spectral aerial imagery and height data using deep learning, with terrestrial LiDAR surveys used to create training labels within the URBORETUM project. The released dataset includes ten genus-level classes, including the aggregate categories \"Other Deciduous\" and \"Coniferous\" where assignment from aerial imagery is uncertain. The final ten-class genus model achieved a mean average precision (mAP) of 0.382 at an intersection-over-union (IoU) threshold of 0.5 (mAP@0.5) and a macro-averaged F1 score of 0.627 for taxonomic class assignment, while a separately trained one-class tree detector achieved an mAP@0.5 of 0.647, indicating stronger reliability for crown detection than for taxonomic classification. The final inventory contains 16.3 million predicted trees across the mapped non-forest domain of Baden-Württemberg. Beyond providing a statewide dataset, this work offers a scalable vision and operational blueprint for an evolving tree inventory that can be systematically improved over time through better imagery, richer local reference data, and targeted retraining.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome assembly and gene annotation of Babesia ovis Israeli strain (Piroplasmida: Babesiidae) using PacBio HiFi sequencing.","authors":"Guilherme Borges Dias, Gisele Albuquerque, Hannes Yngve, Monica L Mazuz, Ana Domingos, Sandra Antunes","doi":"10.1038/s41597-026-08397-4","DOIUrl":"https://doi.org/10.1038/s41597-026-08397-4","url":null,"abstract":"<p><p>Babesia ovis is a unicellular parasite of sheep and goats transmitted by the tick Rhipicephalus bursa. B. ovis infection is widespread and can be lethal, and therefore there is need for a deeper understanding of its biology and pathogenesis. There is currently a single genome available for a clinical isolate of B. ovis, and no laboratory strains have been sequenced so far. In this study we have sequenced and assembled the genome of the Israeli laboratory strain of B. ovis using highly accurate long-read sequencing (PacBio HiFi). The resulting assembly has high contiguity, with the four largest contigs comprising 96% of the assembly and representing putative near-complete chromosomes. Annotation identified 3,549 genes, of which 3,046 (85.6%) could be functionally annotated. BUSCO analysis on the annotated genes revealed 95.5%, 98.8% and 100% completeness of the Aconoidasida, Apicomplexa, and Alveolata lineages, respectively. Comparative analysis with other annotated piroplasmida genomes reveals the vast majority of predicted genes clustered in orthogroups. The availability of a high-quality genome assembly for a tractable laboratory strain could prove impactful in unlocking more basic and applied research avenues to be pursued for this important parasite system.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strengthening Rare Disease Data Readiness: a semantic approach to cross-registry interoperability.","authors":"Clemens M Hentrich, Jessica Vasseur, Ines Reinecke, Jens Weidner, Jens Göbel, Holger Storf, Annette Grüters-Kieslich, Franziska Krause, Mara Hartung, Martin Sedlmayr, Michele Zoch","doi":"10.1038/s41597-026-08406-6","DOIUrl":"https://doi.org/10.1038/s41597-026-08406-6","url":null,"abstract":"<p><p>Rare diseases affect an estimated 446 million people worldwide, yet reliable epidemiological data are scarce due to insufficient data sources and lack of standardization. The German National Registry for Rare Diseases (NARSE) aims to establish a comprehensive epidemiological data basis and enable data availability for research. However, limited interoperability between clinical and epidemiological data sources restricts secondary use and cross-registry research. This study assesses the semantic interoperability between NARSE and two core data sets: the German Medical Informatics Initiative (MII) core data set and the European Rare Disease Registry Infrastructure (ERDRI) core data set. Using a formal consensus process, mapping categories (exactMatch, closeMatch, broadMatch, narrowMatch, relatedMatch) were defined, analyzed, and evaluated for information loss using a heuristic scale. Most NARSE elements aligned with the MII core data set, while mapping to the ERDRI core data set revealed more challenges due to undefined value sets and inconsistent coding systems. These findings highlight the importance of consistent application of coding standards to safeguard information value and improve interoperability in rare disease research.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"16S rRNA Datasets of Gut Microbiota in Stent-Based Diverting Technique and Ileostomy from A Prospective, Open-Label, Multicenter Randomized Controlled Trial.","authors":"Zhongyu Wu, Yifan Tong, Weiping Chen, Wenbin Chen, Lingfei Li, Diyu Huang, Zheyong Li, Zhangfa Song, Xiujun Cai","doi":"10.1038/s41597-026-08247-3","DOIUrl":"https://doi.org/10.1038/s41597-026-08247-3","url":null,"abstract":"<p><p>Accumulating evidence implicates gut dysbiosis in postoperative complications and delayed intestinal recovery after rectal cancer surgery. Although stent-based diverting technique (SDT) and conventional ileostomy exert divergent impacts on gut microbial balance, high-quality comparative microbiome data from randomized trials are lacking. We report a 16S rRNA amplicon dataset from 80 patients randomized to SDT (n = 37) or ileostomy (n = 43), with fecal samples collected preoperatively and on postoperative day 90. We characterized phylum-level taxonomic composition, α-diversity and intergroup β-diversity across four subgroups, and identified discriminatory taxa via LEfSe. Functional prediction detected differential metabolic pathways after surgery. We additionally performed correlation analyses to link clinical covariates with microbial signatures. This public dataset enables independent reprocessing and cross-study comparisons, facilitating exploration of diversion-strategy-specific microbial remodeling and validation of SDT's gut microbiota-preserving capacity.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13627693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nationwide annual agricultural land-use maps of Germany from 1990 to 2023 derived from satellite imagery.","authors":"Gideon Okpoti Tetteh, Vu-Dong Pham, Marcel Schwieder, Lukas Blickensdörfer, Alexander Gocht, Sebastian van der Linden, Stefan Erasmi","doi":"10.1038/s41597-026-08375-w","DOIUrl":"10.1038/s41597-026-08375-w","url":null,"abstract":"<p><p>We present German-wide annual agricultural land-use maps from 1990 to 2023, created from Landsat and Sentinel-2 images using a deep learning approach. Based on farmers' declarations from 2006 to 2022, we extracted annual training samples for 13 crop classes and one grassland class. These samples were used to train a multi-year one-dimensional convolutional neural network, which was subsequently applied to generate the annual land-use maps. Between 2010 and 2022, overall map accuracies ranged between 85% and 93%. When averaged over time, dominant classes like grassland, rapeseed, winter cereals, sugar beet, and maize were detected with high accuracy (≥90%). Conversely, minor classes such as fallow land and plantation were predicted with low accuracy (≤52%). Comparison of map areas with agricultural statistics over time revealed high correlations for most classes, particularly maize (r = 0.978). The presented maps provide an essential basis for analyzing long-term trends in agricultural land-use. They can be used to fill temporal gaps in national agricultural statistics and to disaggregate those statistics to higher spatial units.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615969/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149064238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The FunAqua dataset of global fungal biodiversity in aquatic ecosystems.","authors":"V Prins, L Tedersoo, V Mikryukov, P Paiste, M Sepp, H Grossart, V Kisand, A Laas, H Tammert, Kessy Abarenkov, A Agan, H Agasild, R Agha, J Alatalo, J Alvarez-Manjarrez, A Ameryk, J Anderson, S Anslan, A Antão-Geraldes, A Antosiak, J Arce Funck, R Arias-Real, M Ariyan, M Bahram, S Bansal, R Bao, S Beck, P Bernotas, N Berry, S Bertilsson, C Birnbaum, M Bonk, A V Borges, F Botez, F Q Brearley, J Brookes, D Bruno, A Budzyńska, G Bullerjahn, M Bundschuh, B Calheiros-Nogueira, R Calore, C Capelli, L Caputo, R Carballeira, I Chronis, F Čiampor, Z Čiamporová-Zaťovičová, D Craig, Z Csabai, N Cukrov, L da Silva, E de Eyto, J Delgado, I Dimante-Deimantovica, I Domaizon, R Dondajewska-Pielka, T Dornan, R Drenkhan, K Drouillard, S Duarte, O Dulya, A Dzhulai, D Dziga, B Egeter, M Espenberg, S Färkkilä, S Fazi, A Feckler, E Fenoy, I Fernandes, S Ferreira, V Ferreira, T Fleituch, R Fornaroli, A Freiwald, T Frenken, P Gaffney, O García-Oliva, H Geara, S Gkelis, D Gohar, R Gołdyn, F Grandjean, A Gsell, C Gutiérrez-Cánovas, P Haase, N Hagh-Doust, T Harris, A Hashem, S Havens, B Heidari, S Higgins, M Hosseyni Moghaddam, A Ibrahim, D Jerinkić, S Jones, M Kagami, S Kahar, K Kangro, K Kariman, M Kataržytė, S Kepfer-Rojas, H Khan, L B Knoll, A Knorrn, U Kõljalg, D Konstantinou, J Kotta, K Kowalczewska-Madura, A Kozak, B Kulawig, J Kupagme, I Kušan, R Laarmaa, P Laas, S Langenheder, A Lanzén, A Lateef, M Ligi, T Löffler, U Lortou, K Lujza, T MacConnell, I Maček, P Macreadie, K Maileht, F Marazzi, L Marcello, S Markovskaja, A Martin, S Martínez, V Mata, N Matočec, R McKay, K McKindles, M Mehrshad, M Menéndez, N Merino, A Mešić, S Moctar, M Moza, P Nõges, T Nõges, R O'Hanlon, B Öğlü, J Oja, W Okello, H Orav-Kotta, P Orr, I Osemwegie, J Padisák, V Pajunen, M Panou, A Papatheodoulou, M Pavlovska, J Pearman, H Pehlak, X Peng, A Pereira, R Pereira, B Pernecker, F Picazo, S Pinnow, P Pochekutova, S Põlme, X Pontevedra-Pombal, A Pošta, Y Prekrasna-Kviatkovska, J Pruuli, M Pruuli, T Pruuli, N Radoja, S Rahimlou, R Rannap, S Rasconi, A Rasmussen, W Remmers, L Reyes, G Rhodes, C Roe, O Rojas-Castillo, T Roslin, K Runnel, J Rusch, M Rybak, P Rychtecký, S Sahadevan, A Saitta, A Salehi-Najafabadi, I Santi, J Sarapuu, R B Schäfer, A Schneeweiss, B Scholz, G Selmeczy, M Smederevac-Lalić, R Sommaruga, J Stetler, E Stoica, S Stoll, D Strand, M Tamm, K Tapolczai, J Tarand, S Teurlincx, J Thompson, G Thomson-Laing, L Tiirmann, Z Tkalčec, I Trbojević, S Trevathan-Tackett, N Tsiarta, A Tuvikene, L Tuvikene, M Vacaflores-Argandoña, T Vahter, K Vaino, A L Val, M Vandergoes, A Vasemägi, A Vask, M Vasquez, J Veríssimo, J Vesamäki, L Virta, P Visser, A Viza, T Vrålstad, C S Ward, P Waryszak, J Wierenga, P Wilson, S Wood, A Woźniczka, C Wurzbacher, P Zingel, P Znachor, T E E Dela Cruz, K Panksep","doi":"10.1038/s41597-026-07729-8","DOIUrl":"10.1038/s41597-026-07729-8","url":null,"abstract":"<p><p>Advances in high-throughput DNA sequencing technologies have accelerated research on aquatic microorganisms, their diversity, and ecological functions. This has also significantly advanced research on fungi that shape aquatic ecosystems and food webs. Here, we present the FunAqua dataset, which describes global fungal biodiversity in aquatic ecosystems based on eukaryotic long-read metabarcoding data from 1,076 sediment and 1,299 filtered water samples collected across 1,452 sampling sites in 87 countries. We targeted the internal transcribed spacer (ITS) region. In total, 50,152 fungal OTUs were recorded. The sequencing data is accompanied by comprehensive sample metadata, including physicochemical data of water and sediments. The FunAqua dataset provides a new resource for analysing and better understanding the biodiversity of fungi in different types of aquatic habitats across biogeographical regions. The dataset described in this data paper will be periodically updated with additional metabarcoding data.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13612483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149040584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CTX-UXO: Contextual visual dataset for localisation, classification and identification of unexploded ordnance.","authors":"Gheorghe-Marian Craioveanu, Vasile Calofir, Grigore Stamatescu","doi":"10.1038/s41597-026-07818-8","DOIUrl":"10.1038/s41597-026-07818-8","url":null,"abstract":"<p><p>Unexploded Ordnances (UXOs) are explosive weapons that failed to detonate during deployment, posing significant threats to civilian safety and post-conflict recovery. The Contextual Vision for Unexploded Ordnances (CTX-UXO) dataset addresses this challenge by providing 3520 high-resolution RGB images, median size 2124 px square, containing UXOs collected over four years across all seasons and weather conditions. The dataset encompasses 15 449 annotated instances labeled as UXOs, with twelve subcategories for identification tasks, in which mortar bombs, projectiles and grenades constitute the predominant classes, supporting systems research and development in UXO classification, detection, instance segmentation and related applications, and providing multiple labelling formats such as YOLO and COCO. For the detection task including binary classification, an end-to-end solution for training, validation and testing is available as a reference by presenting a neural network optimized for edge deployment, Faster-RCNN with MobileNet V3 backbone, deployed for real-time inference with reduced costs on Nvidia Jetson Orin, achieving up to 23 frames per second with 62.18% Ap50 for UXO localisation and binary classification.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13601548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149031353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Dresden Dataset for 4D Reconstruction of Non-Rigid Abdominal Surgical Scenes.","authors":"Reuben Docea, Rayan Younis, Yonghao Long, Maxime Fleury, Jinjing Xu, Chenyang Li, André Schulze, Ann Wierick, Johannes Bender, Micha Pfeiffer, Qi Dou, Martin Wagner, Stefanie Speidel","doi":"10.1038/s41597-026-08289-7","DOIUrl":"10.1038/s41597-026-08289-7","url":null,"abstract":"<p><p>The D4D Dataset provides paired endoscopic video and high-quality structured-light geometry for evaluating 3D reconstruction of deforming abdominal soft tissue in realistic surgical conditions. Data were acquired from six porcine cadavers using a da Vinci Xi stereo endoscope and a Zivid structured-light camera, registered via optical tracking and manually curated iterative alignment methods. Three session types (whole deformations, incremental deformations, and moved-camera sessions) probe algorithm robustness to non-rigid motion, deformation magnitude, and out-of-view updates. Each clip provides rectified stereo images, per-frame instrument masks, stereo depth, start/end structured-light point clouds, curated camera poses and camera intrinsics. In postprocessing, ICP and semi-automatic registration techniques are used to register data, and instrument masks are created. The dataset enables quantitative geometric evaluation in both visible and occluded regions, alongside photometric view-synthesis baselines. Comprising over 300,000 frames and 369 point clouds across 98 curated sessions, this resource can serve as a comprehensive benchmark for developing and evaluating non-rigid SLAM, 4D reconstruction, and depth estimation methods.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13594231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149017967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spatiotemporally harmonized dataset of utility-reported power outages and NWS VTEC weather warnings for the contiguous United States (2015-2024).","authors":"Shah Saki, Giulia Sofia, Bandana Kar, Emmanouil Anagnostou","doi":"10.1038/s41597-026-08356-z","DOIUrl":"10.1038/s41597-026-08356-z","url":null,"abstract":"<p><p>Power grids across the United States are increasingly exposed to weather-related disruption, yet reproducible analyses are limited by differences in the spatial and temporal structure of outage and meteorological records. This Data Descriptor presents a harmonized, event-based archive linking county-level customer power-outage records from the U.S. Department of Energy EAGLE-I platform with National Weather Service watches, warnings and advisories encoded through the Valid Time Event Code system. The archive spans the contiguous United States from 2015 through 2024. The primary released file contains hourly indexed, positive processed outage observations that overlap at least one VTEC warning and include event identifiers, matched warning types, and source-record availability diagnostics. Companion files provide the consolidated warning archive, daily county-level EAGLE-I source-record availability and sensitivity summaries evaluating missing-record treatment, temporal aggregation, and recovery persistence. Technical validation quantifies the consequences of preprocessing assumptions and documents spatial, temporal and warning-matching consistency. The dataset supports reproducible analysis of spatiotemporal co-occurrence between reported power disruptions and operational weather warnings while explicitly retaining limitations associated with the underlying outage record.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149114698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The ParlLawSpeech Parliamentary Text Data Collection > 3 Million Parliamentary Bills, Laws, and Speeches from 8 Legislatures.","authors":"Jan Schwalbach, Lukas Hetzer, Sven-Oliver Proksch, Christian Rauh, Miklós Sebők","doi":"10.1038/s41597-026-08335-4","DOIUrl":"10.1038/s41597-026-08335-4","url":null,"abstract":"<p><p>Parliamentary texts are central sources for studying democratic representation, policy-making, and institutional decision-making, and they also constitute a highly valuable domain for natural language processing research. Despite their importance, access to comprehensive, clean, and machine-readable parliamentary corpora remains limited. This paper introduces the ParlLawSpeech (PLS) collection, a large-scale, curated corpus comprising more than three million documents from eight European parliaments, each covering over two decades. PLS extends existing parliamentary text resources in two key ways. First, it integrates full texts and metadata not only for parliamentary speeches but also for legislative bills and finally adopted laws. Second, it provides systematic data linkage across these document types, enabling researchers to trace the full parliamentary decision-making process from draft legislation through debate to adoption. Covering seven national parliaments and the European Parliament, PLS offers a uniquely rich resource for political science and advanced NLP research.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"13 1","pages":""},"PeriodicalIF":7.2,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13589736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149008782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}