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Abstract To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolved in recent years for reproducibility purposes. In this sense, academic institutions’ adoption of these practices has encouraged researchers to publish their data and technical documentation in peer-reviewed publications such as data papers. In this study, we analyze how this broader scientific data documentation meets the needs of the ML community and regulatory bodies for its use in ML technologies. We examine a sample of 4041 data papers of different domains, assessing their coverage and trends in the requested dimensions and comparing them to those from an ML-focused venue (NeurIPS D&B), which publishes papers describing datasets. As a result, we propose a set of recommendation guidelines for data creators and scientific data publishers to increase their data’s preparedness for its transparent and fairer use in ML technologies.

Abstract This paper introduces CORE, a widely used scholarly service, which provides access to the world’s largest collection of open access research publications, acquired from a global network of repositories and journals. CORE was created with the goal of enabling text and data mining of scientific literature and thus supporting scientific discovery, but it is now used in a wide range of use cases within higher education, industry, not-for-profit organisations, as well as by the general public. Through the provided services, CORE powers innovative use cases, such as plagiarism detection, in market-leading third-party organisations. CORE has played a pivotal role in the global move towards universal open access by making scientific knowledge more easily and freely discoverable. In this paper, we describe CORE’s continuously growing dataset and the motivation behind its creation, present the challenges associated with systematically gathering research papers from thousands of data providers worldwide at scale, and introduce the novel solutions that were developed to overcome these challenges. The paper then provides an in-depth discussion of the services and tools built on top of the aggregated data and finally examines several use cases that have leveraged the CORE dataset and services.

Abstract This paper presents an open-source dataset intended to enhance the analysis and optimization of photovoltaic (PV) power generation in urban environments, serving as a valuable resource for various applications in solar energy research and development. The dataset comprises measured PV power generation data and corresponding on-site weather data gathered from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals at the inverter-level. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using the Brick schema, which simplifies data comprehension and the development of smart analytics applications. This paper provides a detailed description on the site specifications, data collection method, data records, and data validation. This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.

Abstract This paper presents a new synthetic dataset of ID and travel documents, called SIDTD. The SIDTD dataset is created to help training and evaluating forged ID documents detection systems. Such a dataset has become a necessity as ID documents contain personal information and a public dataset of real documents can not be released. Moreover, forged documents are scarce, compared to legit ones, and the way they are generated varies from one fraudster to another resulting in a class of high intra-variability. In this paper we introduce a dataset, synthetically generated, that simulates the most common, and easiest, forgeries to be made by common users of ID documents and travel documents. The creation of this dataset will help to document image analysis community to progress in the task of automatic ID document verification in online onboarding systems.

Abstract Retinopathy of Prematurity (ROP) is a critical eye disorder affecting premature infants, characterized by abnormal blood vessel development in the retina. Plus Disease, indicating severe ROP progression, plays a pivotal role in diagnosis. Recent advancements in Artificial Intelligence (AI) have shown parity with or surpass human experts in ROP detection, especially Plus Disease. However, the success of AI systems depends on high-quality datasets, emphasizing the need for collaboration and data sharing among researchers. To address this challenge, the paper introduces a new public dataset, FARFUM-RoP (Farabi and Ferdowsi University of Mashhad’s ROP dataset), comprising 1533 ROP fundus images from 68 patients, annotated independently by five experienced childhood ophthalmologists as “Normal,” “Pre-Plus,” or “Plus.” Ethical principles and consent were meticulously followed during data collection. The paper presents the dataset structure, patient details, and expert labels.

Abstract This paper documents the creation of new ‘commuting zones’ for the United States based on 2020 geographies and data. Commuting zones originated in the late 1980’s as the result of an effort by the Economic Research Service of the U.S. Department of Agriculture to provide a county-based delineation of functional regions that covered the entire U.S. and linked rural areas to their nearest economic center. The commuting zones presented here update the 2010 era definitions. Additionally, this new delineation incorporates measures of quality to facilitate comparison with earlier decades and to account for the fact that the quality of commuting-based delineations may have been substantially affected by changing patterns of work and residence during and after the covid-19 pandemic. The methodology used here seeks to replicate as nearly as possible the method employed in earlier versions of this delineation using hierarchical clustering on a proportional flows matrix generated from county to county commuting flows. The data and all scripts used to generate them and this paper are available at https://github.com/csfowler/CommutingZones2020.git .

Abstract Public Transport Authorities generate large quantities of data as part of their daily operations, including vehicle positions, arrival times, and dynamic routing options. This information is essential feedback into the system for planning routes and timetables, modelling passenger demand growth and evaluating operational performance. The General Transit Feed Specification (GTFS) is a data format that allows public transport data to be consumed by a wide variety of software applications. There are barriers to widespread consumption of GTFS data related to location-specific data extensions, non-human-readable formats and error cleaning. This paper describes a flexible dataset of actual bus arrivals and departure times created with a pipeline for GTFS realtime feeds designed to address these challenges. The paper describes the pipeline, verifies the quality of the data and presents an output of 25 months of actual bus arrival and departure times for Sydney, Australia. We conclude by discussing relevance to researchers and practitioners of the pipeline outputs in general and the sample data specifically.

Abstract This paper presents the Archaeological Artefact Database of Finland (AADA) of prehistoric (covering period of almost 11,000 years) artefacts in Finland that are categorised by type and are accompanied with photos of the artefacts. The database is intended to contain all typologically classifiable prehistoric artefacts found in Finland and held in Finnish collections. This dataset provides spatio-temporal context for artefacts across different time periods and regions, as it includes approximately 38,000 single artefacts and approximately 10,000 pottery type identifications from the Early Mesolithic to the end of the Iron Age in Finland (c. 8900 calBC - 1300/1500 calAD). In addition, the artefacts are given period-based (subperiod) dating to allow their chronological affiliation. To facilitate data usage, we also offer an R-script to replicate the data visualisation provided in this paper and a Python script to merge the artefact information to the pictures. We further work towards an interactive user interface for data download and visualization.

Abstract Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.

Abstract The demand for open data and open science is on the rise, fueled by expectations from the scientific community, calls to increase transparency and reproducibility in research findings, and developments such as the Final Data Management and Sharing Policy from the U.S. National Institutes of Health and a memorandum on increasing public access to federally funded research, issued by the U.S. Office of Science and Technology Policy. This paper explores the pivotal role of data repositories in biomedical research and open science, emphasizing their importance in managing, preserving, and sharing research data. Our objective is to familiarize readers with the functions of data repositories, set expectations for their services, and provide an overview of methods to evaluate their capabilities. The paper serves to introduce fundamental concepts and community-based guiding principles and aims to equip researchers, repository operators, funders, and policymakers with the knowledge to select appropriate repositories for their data management and sharing needs and foster a foundation for the open sharing and preservation of research data.

Abstract The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018–2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.

Abstract Prolonged and over-excessive interaction with cyberspace poses a threat to people’s health and leads to the occurrence of Cyber-Syndrome, which covers not only physiological but also psychological disorders. This paper aims to create a tree-shaped gold-standard corpus that annotates the Cyber-Syndrome, clinical manifestations, and acupoints that can alleviate their symptoms or signs, designating this corpus as CS-A. In the CS-A corpus, this paper defines six entities and relations subject to annotation. There are 448 texts to annotate in total manually. After three rounds of updating the annotation guidelines, the inter-annotator agreement (IAA) improved significantly, resulting in a higher IAA score of 86.05%. The purpose of constructing CS-A corpus is to increase the popularity of Cyber-Syndrome and draw attention to its subtle impact on people’s health. Meanwhile, annotated corpus promotes the development of natural language processing technology. Some model experiments can be implemented based on this corpus, such as optimizing and improving models for discontinuous entity recognition, nested entity recognition, etc. The CS-A corpus has been uploaded to figshare.

Abstract Data quality is of crucial importance in the field of automated or digitally assisted assembly. This paper presents a comprehensive data set of triangle meshes representing electrical and electronic components obtained by scraping Computer Aided Design (CAD) models from the Internet. Consisting of a total of 234 triangle meshes with labelled vertices, this data set was specifically created for segmentation tasks. Its versatility for multimodal tasks is underscored by the presence of various labels, including vertex labels, categories, and subcategories. This paper presents the data set and provides a thorough statistical analysis, including measures of shape, size, distribution, and inter-rater reliability. In addition, the paper suggests several approaches for using the data set, considering its multimodal characteristics. The data set and related findings presented in this paper are intended to encourage further research and advancement in the field of manufacturing automation, specifically spatial assembly.

Abstract Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.

Abstract Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety. This paper reports on a new survey dataset that was created as part of the Urban Transport Modelling for Sustainable Well-Being in Hanoi (UTM-Hanoi) project. The survey of nearly 30,000 respondents gathers data on households’ demographics, perceptions, opinions and stated behaviours. The data are informative in their own right and have also been used to experiment with multi-scale spatial statistics, synthetic population generation and machine learning approaches to predicting an individual’s perceptions of potential government policies. The paper reports on the key findings from the survey and conducts a technical validation to contrast the outcomes to similar datasets that are available.

Abstract Small-scale motion detection using non-invasive remote sensing techniques has recently garnered significant interest in the field of speech recognition. Our dataset paper aims to facilitate the enhancement and restoration of speech information from diverse data sources for speakers. In this paper, we introduce a novel multimodal dataset based on Radio Frequency, visual, text, audio, laser and lip landmark information, also called RVTALL. Specifically, the dataset consists of 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77 GHz frequency modulated continuous wave (FMCW) data from millimeter wave (mmWave) radar, visual and audio information, lip landmarks and laser data, offering a unique multimodal approach to speech recognition research. Meanwhile, a depth camera is adopted to record the landmarks of the subject’s lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words, and 16 sentences. The dataset has been validated and has potential for the investigation of lip reading and multimodal speech recognition.

Abstract Coaxial monitoring of the Direct Energy Deposition (DED) machines enables a real-time material deposition study. Coaxial-images contain substantial melt-pool information and incorporate situational information including the sparks’ intensity, numbers, etc. Recent studies have shown that melt-pool observations correlate directly with machine parameters and artifact properties. Therefore, the melt-pool information not only can assist in measuring the machine’s working condition and determining machine operation parameters’ reliability but also facilitates the deposition characteristics studies like print’s regime and dimensions. This information is gathered during the fabrication and can be expanded to perform various process studies and fault registration. This paper utilizes the Optomec DED machine to fabricate single-track prints with multiple process parameters, while a coaxial camera records the deposition. Each deposited track is then cut perpendicular to the print’s direction to facilitate process parameters correlation study with actual geometrical deposition measured using a microscope. The coaxial images taken during fabrication, along with their process parameters, cross-cut measurements, and a developed image-processing toolbox, are presented alongside this paper to empower future research directions.

Abstract The European Plate Observing System (EPOS) is a long-term initiative aimed at integrating research infrastructures for solid Earth science in Europe. EPOS provides a sustainable, multidisciplinary user-oriented platform - the EPOS Data Portal - that facilitates data integration, access, use, and re-use, while adhering to the FAIR principles. The paper describes the key governance, community building, and technical aspects for achieving multidisciplinary data integration through the portal. It also outlines the key portal features for aggregating approximately 250 data sources from more than ten different scientific communities. The main architectural concepts underpinning the portal, namely the rich-metadata, the service-driven data provision, and the usage of semantics, are outlined. The paper discusses the challenges encountered during the creation of the portal, describes the community engagement process, and highlights the benefits to the scientific community and society. Future work includes expanding portal functionalities to include data analysis, processing, and visualization and releasing the portal as an open-source software package.

Abstract Industrial Kitchens are very energy-intensive businesses, consuming between five and seven times more energy per square meter than other commercial spaces like office spaces and retail stores. Still, very little research has been carried out on improving the energy efficiency of this industry. This paper presents the FIKElectricity dataset, a collection of electricity data from three Portuguese restaurant kitchens during their daily operation. The duration of the datasets spans three to four weeks in each industrial kitchen and comprises aggregated and individual appliance consumption, collected at 1 Hz and $$\frac{1}{5}$$ 1 5 Hz, respectively. The public release of FIKElectricity is expected to draw more attention from the research community to this overlooked industrial sector. The data collection and post-processing methods are thoroughly described in this paper, as well as the dataset organization. Examples showing the dataset’s quality and instructions for its reuse are also provided.

Abstract Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on the automatic detection of polyps or other pathologies, but localization and navigation of the endoscope are completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence to endoscopies, data from complete procedures is needed. This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice, making secondary use of medical data. Its main purpose is to facilitate the development and evaluation of Visual Simultaneous Localization and Mapping (VSLAM) methods in real endoscopy data. The dataset contains more than 24 hours of video. It is the first endoscopic dataset that includes endoscope calibration as well as the original calibration videos. Meta-data and annotations associated with the dataset vary from the anatomical landmarks, procedure labeling, segmentations, reconstructions, simulated sequences with ground truth and same patient procedures. The software used in this paper is publicly available.

Abstract The data paper is becoming a popular way for researchers to publish their research data. The growing numbers of data papers and journals hosting them have made them an important data source for understanding how research data is published and reused. One barrier to this research agenda is a lack of knowledge as to how data journals and their publications are indexed in the scholarly databases used for quantitative analysis. To address this gap, this study examines how a list of 18 exclusively data journals (i.e., journals that primarily accept data papers) are indexed in four popular scholarly databases: the Web of Science, Scopus, Dimensions, and OpenAlex. We investigate how comprehensively these databases cover the selected data journals and, in particular, how they present the document type information of data papers. We find that the coverage of data papers, as well as their document type information, is highly inconsistent across databases, which creates major challenges for future efforts to study them quantitatively, which should be addressed in the future.

Abstract We present an automatically generated dataset of 15,755 records that were extracted from 47,357 papers. These records contain water-splitting activity in the presence of certain photocatalysts, along with additional information about the chemical reaction conditions under which this activity was recorded. These conditions include any co-catalysts and additives that were present during water splitting, the length of time for which the photocatalytic experiment was conducted, and the type of light source used, including its wavelength. Despite the text extraction of such a wide range of chemical reaction attributes, the dataset afforded good precision (71.2%) and recall (36.3%). These figures-of-merit were calculated based on a random sample of open-access papers from the corpus. Mining such a complex set of attributes required the development of novel techniques in knowledge extraction and interdependency resolution, leveraging inter- and intra-sentence relations, which are also described in this paper. We present a new version (version 2.2) of the chemistry-aware text-mining toolkit ChemDataExtractor, in which these new techniques are included.

Abstract Modernizing existing buildings is vital to saving carbon emissions and counteracting global climate change. Many countries face the challenge of modernizing a considerable number of their buildings in the next two decades. Implementing related modernization measures requires a high number of craftworkers. However, current studies indicate that these craftworkers’ current lack will increase. Examining the effects of craftworkers shortage on modernization strategies needs data concerning the realization time of modernization measures. We collected this data based on 90 expert interviews and provide it in this paper. The interview results comprise realization times for insulation measures of the envelope and changes in the heat supply system for typical buildings. This paper describes the data collection and presents the raw data available at a repository at Figshare. The data is internationally applicable in simulation and optimization approaches for building modernization.

Abstract This paper presents the Coswara dataset, a dataset containing diverse set of respiratory sounds and rich meta-data, recorded between April-2020 and February-2022 from 2635 individuals (1819 SARS-CoV-2 negative, 674 positive, and 142 recovered subjects). The respiratory sounds contained nine sound categories associated with variants of breathing, cough and speech. The rich metadata contained demographic information associated with age, gender and geographic location, as well as the health information relating to the symptoms, pre-existing respiratory ailments, comorbidity and SARS-CoV-2 test status. Our study is the first of its kind to manually annotate the audio quality of the entire dataset (amounting to 65 hours) through manual listening. The paper summarizes the data collection procedure, demographic, symptoms and audio data information. A COVID-19 classifier based on bi-directional long short-term (BLSTM) architecture, is trained and evaluated on the different population sub-groups contained in the dataset to understand the bias/fairness of the model. This enabled the analysis of the impact of gender, geographic location, date of recording, and language proficiency on the COVID-19 detection performance.

Abstract This paper describes a dataset capturing insider trading activity at publicly traded companies. Investors and investment analysts demand this information because executives, directors and large shareholders are expected to have more intimate knowledge of their company’s prospects than outsiders. Insider stock sales and purchases may reveal information about the firm’s business not disclosed in financial statements. They may also convey new information predictive of stock price movements if insiders can better interpret public information about the firm. Since mid-2003, the Securities and Exchange Commission has made these insider trading reports available to the public in a structured format; however, most academic papers use proprietary commercial databases instead of regulatory filings directly. This makes replication challenging as the data manipulation and aggregation processes are opaque and historical records could be altered by the database provider over time. To overcome these limitations, the presented dataset is created from original regulatory filings; it is updated daily and includes all information reported by insiders without alteration.

Abstract The data described in this paper were collected from four jurisdictions in south Asia, Assam and Bihar in India and Punjab and Sindh in Pakistan. The data were collected from farmer households involved in surface water irrigation with the aim of understanding the merits of participatory irrigation management (PIM) in different settings in south Asia. The data were collected using four structured survey instruments, which comprised three paper-based surveys and one online survey collected via tablets. This data can be used by researchers to empirically analyze: overall institutional performance and its relationship to agro-economic variables; drivers of compliance; gender differences and their impact on participation in water groups and perceptions of performance; preferred charging regimes and broader institutional arrangements for managing water at the local level. These data are unique, having been collected simultaneously across the four jurisdictions.

Abstract It has been widely accepted that solar magnetic field manipulates all solar activities, especially violent solar bursts in solar corona. Thus, it is extremely important to reconstruct three-dimentional (3D) magnetic field of solar corona from really observed photospheric magnetogram. In this paper, a large-scale dataset of 3D solar magnetic fields of active regions is built by using the nonlinear force-free magnetic field (NLFFF) extrapolation from vector magnetograms of Helioseismic and Magnetic Imager (HMI) on Solar Dynamics Observatory (SDO). In this dataset, all space-weather HMI active region patches (SHARPs) with the corresponding serial numbers of national oceanic and atmospheric administration (NOAA) are included. They are downloaded from the SHARP 720 s series of JSOC every 96 minutes. In addition, each sample is labelled with a finer grained label for solar flare forecast. This paper is with the purpose of open availability of data resource and source code to the peers for refraining from repeated labor of data preparation. Meanwhile, with such a large-scale, high spatio-temporal resolution and high quality scientific data, we anticipate a wide attention and interest from artificial intelligence (AI) and computer vision communities, for exploring AI for astronomy over such a large-scale dataset.

Abstract This paper describes OSeMOSYS Global, an open-source, open-data model generator for creating global electricity system models for an active global modelling community. This version of the model generator is freely available and can be used to create interconnected electricity system models for both the entire globe and for any geographically diverse subset of the globe. Compared to other existing global models, OSeMOSYS Global allows for full user flexibility in determining the time slice structure and geographic scope of the model and datasets, and is built using the widely used fully open-source OSeMOSYS energy system model. This paper describes the data sources, structure and use of OSeMOSYS Global, and provides illustrative workflow results.

Abstract The “13th Five-Year Plan” of civil aviation energy conservation and emission reduction impacts China’s domestic aviation exchanges. However, few researchers pay attrition to the impact of the 13th Five-Year Plan on aviation emissions. This paper intends to calculate the emissions of six pollutants (CO2, CO, HC, NOx, PM2.5, and SO2) in China’s domestic airlines from 2014 to 2019 and explore the impact of the 13th Five-Year Plan on emissions changes. In this paper, the improved BFFM2-FOA-FPM method is used to unify the calculation of CO2 and other aviation emissions. The error rate between the estimate and the official data was about 6.45%. The results show that the 13th Five-Year Plan has impacted on aviation emissions, including the number of routes and airlines, aircraft configuration, air routes, and airline unit turnover emissions. Additionally, the 13th Five-Year Plan’s effect is not significant, and it does not promote the reduction of emissions on domestic routes.

Abstract Solutions for the generation of FAIR (Findable, Accessible, Interoperable, and Reusable) data and metadata in experimental tribology are currently lacking. Nonetheless, FAIR data production is a promising path for implementing scalable data science techniques in tribology, which can lead to a deeper understanding of the phenomena that govern friction and wear. Missing community-wide data standards, and the reliance on custom workflows and equipment are some of the main challenges when it comes to adopting FAIR data practices. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building, and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability, and reusability of experimental tribological data beyond typical publication practices.

Abstract This paper describes the database The global Multidimensional Poverty Index (MPI): Harmonised level estimates and their changes over time. The global MPI is an international poverty measure based on ten deprivation indicators in three dimensions: health, education, and living standards. The database contains estimates for the MPI itself (the adjusted headcount ratio); related partial indices such as headcount ratio, intensity, indicator-specific indices, and several auxiliary statistics; and changes over time for most quantities. For this database, all deprivation indicators have been harmonised over time. Our database covers estimates for 84 countries and 814 subnational regions for up to four points of observation. The estimates are based on 211 individual survey datasets, provided primarily by the Demographic Health Surveys (DHS) and the Multiple Indicator Cluster Surveys (MICS). Combining information about different dimensions of human wellbeing, the global MPI inherently invites interdisciplinary research.

Abstract In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions. However, the scarcity of publicly available PXI and CBCT datasets hampers their use in tooth segmentation tasks. Therefore, this paper presents a multimodal dataset for Semi-supervised Tooth Segmentation (STS-Tooth) in dental PXI and CBCT, named STS-2D-Tooth and STS-3D-Tooth. STS-2D-Tooth includes 4,000 images and 900 masks, categorized by age into children and adults. Moreover, we have collected CBCTs providing more detailed and three-dimensional information, resulting in the STS-3D-Tooth dataset comprising 148,400 unlabeled scans and 8,800 masks. To our knowledge, this is the first multimodal dataset combining dental PXI and CBCT, and it is the largest tooth segmentation dataset, a significant step forward for the advancement of tooth segmentation.

Abstract With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models. Despite recent progress in deep learning to ecosystem monitoring, there is a need for datasets specifically designed to analyse compound heatwave and drought extreme impact. Here, we introduce the DeepExtremeCubes database, tailored to map around these extremes, focusing on persistent natural vegetation. It comprises over 40,000 globally sampled small data cubes (i.e. minicubes), with a spatial coverage of 2.5 by 2.5 km. Each minicube includes (i) Sentinel-2 L2A images, (ii) ERA5-Land variables and generated extreme event cube covering 2016 to 2022, and (iii) ancillary land cover and topography maps. The paper aims to (1) streamline data accessibility, structuring, pre-processing, and enhance scientific reproducibility, and (2) facilitate biosphere dynamics forecasting in response to compound extremes.

Abstract Walking on compliant terrains, like carpets, grass, and soil, presents a unique challenge, especially for individuals with mobility impairments. In contrast to rigid-ground walking, compliant surfaces alter movement dynamics and increase the risk of falls. Understanding and modeling gait control across such soft and deformable surfaces is thus crucial for maintaining daily mobility. However, access to the necessary equipment for modeling compliant surface walking is limited. Therefore, in this paper, we present the first publicly available biomechanics dataset of 20 individuals walking on terrains of varying compliance, using a unique robotic device, the Variable Stiffness Treadmill 2 (VST 2), designed to simulate walking on adjustable compliant terrain. VST 2 provides a consistent and reproducible environment for studying the biomechanics of walking on such surfaces within laboratory settings. The goal of this dataset is to provide insights into the muscular, kinematic, and kinetic adaptations that occur when humans walk on compliant terrain in order to design better controllers for prosthetic limbs, improve rehabilitation protocols, and develop adaptive assistive devices that can enhance mobility on compliant surfaces.

Abstract Endoscopy is vital for detecting and diagnosing gastrointestinal diseases. Systematic examination protocols are key to enhancing detection, particularly for the early identification of premalignant conditions. Publicly available endoscopy image databases are crucial for machine learning research, yet challenges persist, particularly in identifying upper gastrointestinal anatomical landmarks to ensure effective and precise endoscopic procedures. However, many existing datasets have inconsistent labeling and limited accessibility, leading to biased models and reduced generalizability. This paper introduces GastroHUN, an open dataset documenting stomach screening procedures based on a systematic protocol. GastroHUN includes 8,834 images from 387 patients and 4,729 labeled video sequences, all annotated by four experts. The dataset covers 22 anatomical landmarks in the stomach and includes an additional category for unqualified images, making it a valuable resource for AI model development. By providing a robust public dataset and baseline deep learning models for image and sequence classification, GastroHUN serves as a benchmark for future research and aids in the development of more effective algorithms.

Abstract Deep learning methods have shown significant potential in tool wear lifecycle analysis. However, there are fewer open source datasets due to the high cost of data collection and equipment time investment. Existing datasets often fail to capture cutting force changes directly. This paper introduces QIT-CEMC, a comprehensive dataset for the full lifecycle of titanium (Ti6Al4V) tool wear. QIT-CEMC utilizes complex circumferential milling paths and employs a rotary dynamometer to directly measure cutting force and torque, alongside multidimensional data from initial wear to severe wear. The dataset consists of 68 different samples with approximately 5 million rows each, includes vibration, sound, cutting force and torque. Detailed wear pictures and measurement values are also provided. It is a valuable resource for time series prediction, anomaly detection, and tool wear studies. We believe QIT-CEMC will be a crucial resource for smart manufacturing research.

Abstract Due to growing population and technological advances, global electricity consumption is increasing. Although CO2 emissions are projected to plateau or slightly decrease by 2025 due to the adoption of clean energy sources, they are still not decreasing enough to mitigate climate change. The residential sector makes up 25% of global electricity consumption and has potential to improve efficiency and reduce CO2 footprint without sacrificing comfort. However, a lack of uniform consumption data at the household level spanning multiple regions hinders large-scale studies and robust multi-region model development. This paper introduces a multi-region dataset compiled from publicly available sources and presented in a uniform format. This data enables machine learning tasks such as disaggregation, demand forecasting, appliance ON/OFF classification, etc. Furthermore, we develop an RDF knowledge graph that characterizes the electricity consumption of the households and contextualizes it with household-related properties enabling semantic queries and interoperability with other open knowledge bases like Wikidata and DBpedia. This structured data can be utilized to inform various stakeholders towards data-driven policy and business development.

Abstract This paper introduces the “NEBULA101 - Neuro-behavioural Understanding of Language Aptitude” dataset, which comprises behavioural and brain imaging data from 101 healthy adults to examine individual differences in language and cognition. Human language, a multifaceted behaviour, varies significantly among individuals, at different processing levels. Recent advances in cognitive science have embraced an integrated approach, combining behavioural and brain studies to explore these differences comprehensively. The NEBULA101 dataset offers brain structural, diffusion-weighted, task-based and resting-state MRI data, alongside extensive linguistic and non-linguistic behavioural measures to explore the complex interaction of language and cognition in a highly multilingual sample. By sharing this multimodal dataset, we hope to promote research on the neuroscience of language, cognition and multilingualism, enabling the field to deepen its understanding of the multivariate panorama of individual differences and ultimately contributing to open science.

Abstract This paper presents an experimental dataset developed for the detection of parallel arc faults in aircraft electrical systems. This dataset is based on a total of 960 experiments performed in a low-pressure chamber under different conditions using two electrodes placed on the surface of an insulating material. These experiments correspond to 2 insulating materials, 12 electrode distances, and 10 pressure conditions representative of aircraft environments. Each experimental condition was repeated four times, resulting in 960 experimental recordings, each containing one million samples of time, current, and voltage signals of the electric arc induced on the surface of the insulating material. The dataset can be used to model arc behavior under different pressure conditions, to identify patterns that indicate the presence of an arc, and to accelerate the improvement of arc identification. This dataset has the potential to be used to develop arc fault detection and identification methods for more electric and all-electric aircraft and other electric vehicles.

Abstract Northern elephant seals (Mirounga angustirostris) have been integral to the development and progress of biologging technology and movement data analysis, which continue to improve our understanding of this and other species. Adult female elephant seals at Año Nuevo Reserve and other colonies along the west coast of North America were tracked annually from 2004 to 2020, resulting in a total of 653 instrument deployments. This paper outlines the compilation and curation process of these high-resolution diving and location data, now accessible in two Dryad repositories. The code used for data processing alongside the corresponding workflow is available through GitHub and Zenodo. This data set represents 3,844,927 dives and 596,815 locations collected from 475 individual seals with 178 repeat samplings over 17 years. We anticipate that these data will stimulate further analysis and investigation into elephant seal biology and aid in developing new analytical approaches for large marine predators.

Abstract The Qilian Mountain Area (QMA) serves as a crucial ecological barrier and strategic water conservation zone in China. Recent years have seen heightened social attention to environmental issues within the QMA, underscoring the need for accurate and continuous land cover maps to support ecological monitoring, analysis, and forecasting. This paper presents the QMA_LC30 dataset, which includes 9 land cover categories and spans the period from 1990 to 2020, with updates every 5 years. The dataset primarily utilizes 30 m Landsat series data and features: 1) High precision, achieved through a geographical division and hierarchical classification decision tree approach, complemented by visual interpretation. 2) Robust consistency, ensured by a change detection method based on a benchmark map. The QMA_LC30 dataset undergoes rigorous accuracy validation, achieving an overall accuracy of over 0.92 for all 7 periods of land cover maps. Compared to GlobeLand30, ESA WorldCover, ESRI 2020 Land Cover, FROM_GLC30, and GLC_FCS30, QMA_LC30 demonstrates the highest consistency with remote sensing images.

Abstract This paper presents an open-access repository collecting information on measles virus infections and flight passenger movements in European countries from 2011 to 2023. It provides a comprehensive overview of reported measles cases and measles-mumps-rubella (MMR) vaccination coverage from authoritative organizations such as the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC). In addition, the dataset includes detailed data on passenger movements between countries, facilitating analysis of cross-border disease transmission. This resource enables more precise spatial analyses for monitoring and forecasting measles outbreaks, underscoring the importance of adequate vaccination coverage and sustained international surveillance to prevent the spread of the disease.

Abstract Inter-regional input-output (IRIO) tables are essential for socioeconomic and environmental analysis. This paper compiled a continuous time series of Chinese IRIO tables with a detailed regional and sectoral classification, covering a longer period from 1987 to 2017 than existing Chinese IRIO tables. Additionally, we integrated the China provincial CO2 emission inventory data (1987–2017) to analyze trends and identify the main driving factors behind regional economic linkages and carbon emissions changes. Specifically, we adjusted the initial values of the international and interprovincial imports and exports using customs data. Subsequently, we employed the minimum cross-entropy model to balance the adjusted input-output tables (IOTs) from both row and column perspectives. Following this, we estimated the interprovincial trade flow for each sector using the gravity model. Then, we balanced these estimates with the minimum cross-entropy model to form the multi-regional input-output (MRIO) table. Finally, we constructed the IRIO table based on the proportion assumption. The compiled IRIO tables, integrated with carbon emission data, have significant applications in regional economic analysis, policy evaluation, and environmental studies.

Abstract This paper introduces and describes a dataset representing United States carbon dioxide (CO2) emissions from electricity consumption (scope 2 CO2 emissions) for the 2019–2021 time period separately for the residential, commercial, and industrial sectors. The spatial resolution is the U.S. census block group at annual time resolution. We also provide state-scale aggregate data. Given the increased interest in greenhouse gas mitigation at the urban landscape scale, this data offers the opportunity to better understand the emitting landscape and craft more targeted, efficient policy instruments. The approach starts with well-established estimates of scope 2 CO2 emissions at larger scales and performs a series of downscaling steps to allocate scope 2 emissions into block groups. This open-access scope 2 CO2 emissions dataset and methodology is valuable for a wide range of multidisciplinary studies, including climate science, environmental policy, urban planning, and socioeconomic research.

Abstract The role of humans in industry is changing due to the technological transformation of production processes, which represents a potential source of stress for workers. In this context, the NO-STRESS project aims to combine objective and subjective data collected during task execution to explore causes of work-related stress and propose solutions to mitigate them. This paper describes the within-subjects study design used in the project, where each participant was exposed to all experimental conditions, and outlines the workflow developed during the study. Moreover, it describes the behavioural, performance and perceptual indicators of participants’ stress collected during the execution of full-kitting, component quality control, assembly and product quality monitoring tasks. The data collection sessions were conducted in different manufacturing contexts. The collected data may be reused to compare stress intensity among different subjects considering individual factors and experimental conditions. In addition, the results of stress in manufacturing may be compared to the intensity of this phenomenon in other sectors. The consequent outcomes may have practical implications for work contexts and benefits for society as a whole.

Abstract Ionic liquids (ILs) are structurally tunable salts with applications ranging from chemical synthesis to batteries, novel materials and medicine. Despite their potential, the toxicity of ILs poses significant environmental and biological challenges. This study introduces a comprehensive dataset of cytotoxicity of 1227 ILs, compiled from 151 research papers and encompassing 3837 data entries. For each entry, the following information is provided: substance name, empirical formula, CAS, SMILES, molecular weight, cytotoxicity value, details on the experimental setup (incubation time, cell line, assay used, etc.), and reference to the original publication. This dataset can be used for deriving structure‒activity relationships and establishing the major structural elements that govern the cytotoxic effects of ILs on eukaryotic cells. The dataset is available freely to all researchers.

Abstract Polarization multispectral imaging has advanced significantly due to its robust information representation capability. Imaging application requires rigorous simulation evaluation and experimental validation using standardized datasets. However, the current full-Stokes polarization multispectral images (FSPMI) dataset, while providing simulation data, is limited by image drift and spectral bands. To overcome these limitations and supplement experimental data, this paper introduces the multi-angle and full-Stokes polarization multispectral images (MAFS-PMI) dataset. The imaging system utilizes a rotatable quarter-wave plate (QWP) and a fixed liquid crystal tunable filter (LCTF) to modulate polarization information. Meanwhile, the LCTF allows switching between multiple spectral bands. The acquired multi-angle polarization multispectral images facilitate the experimental validation of encoding strategies and reconstruction algorithms. Additionally, the derived full-Stokes polarization multispectral images enable the simulation evaluation of imaging methods. The MAFS-PMI dataset involves 73 fast axis angles (0° to 180°), four Stokes parameters, five polarization parameters, 35 spectral bands (520 nm to 690 nm), 400 × 400 pixels, and 12 distinct objects. This dataset offers a valuable resource for developing advanced imaging methods.

Abstract In this paper, we describe the dataset captured with our proprietary data capture solution mounted on top of a Land Rover Defender vehicle. The captured data are the real data of drives on various Slovak roads. The total dataset consist of almost 33 hours of driving with a automotive grade FPD Link camera with 30 fps and with additional sensors such as high-precision GNSS sensor and modem towards mobile data connectivity LTE and 5 G. There are various road types and weather and conditions. Road types are 2-lane highway, 3-lane highway, urban environment, 1st class road (main, national), 2nd class road (main, regional), 3rd class road (local). Weather and light conditions are direct sunlight, normal daylight, rain/fog and dawn/twilight. All frames have captured its position, velocity, heading and connectivity information.

Abstract Governments procure large amounts of goods and services to help them implement policies and deliver public services; in Italy, this is an essential sector, corresponding to about 12% of the gross domestic product. Data are increasingly recorded in public repositories, although they are often divided into multiple sources and not immediately available for consultation. This paper provides a description and analysis of an effort to collect and arrange a legal public administration database. The main source of interest involves the National Anti-Corruption Authority in Italy, which describes more than 3 million tenders. To improve usability, the database is integrated with two other relevant data sources concerning information on public entities and territorial units for statistical purposes. The period identified by domain experts covers 2016–2023. The analysis also identifies key challenges that arise from the current Open Data catalogue, particularly in terms of data completeness. A practical application is described with an example of use. The final dataset, called Italian Tender Hub (ITH), is available in a repository with a description of its use.

Abstract The paper describes the production and evaluation of global grassland extent mapped annually for 2000–2022 at 30 m spatial resolution. The dataset showing the spatiotemporal distribution of cultivated and natural/semi-natural grassland classes was produced by using GLAD Landsat ARD-2 image archive, accompanied by climatic, landform and proximity covariates, spatiotemporal machine learning (per-class Random Forest) and over 2.3 M reference samples (visually interpreted in Very High Resolution imagery). Custom probability thresholds (based on five-fold spatial cross-validation) were used to derive dominant class maps with balanced user’s and producer’s accuracy, resulting in f1 score of 0.64 and 0.75 for cultivated and natural/semi-natural grassland, respectively. The produced maps (about 4 TB in size) are available under an open data license as Cloud-Optimized GeoTIFFs and as Google Earth Engine assets. The suggested uses of data include (1) integration with other compatible land cover products and (2) tracking the intensity and drivers of conversion of land to cultivated grasslands and from natural / semi-natural grasslands into other land use systems.