| 11. |
- Latz, Meike, et al.
(författare)
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A comprehensive dataset on spatiotemporal variation of microbial plankton communities in the Baltic Sea
- 2024
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- The Baltic Sea is one of the largest brackish water environments on earth and is characterised by pronounced physicochemical gradients and seasonal dynamics. Although the Baltic Sea has a long history of microscopy-based plankton monitoring, DNA-based metabarcoding has so far mainly been limited to individual transect cruises or time-series of single stations. Here we report a dataset covering spatiotemporal variation in prokaryotic and eukaryotic microbial communities and physicochemical parameters. Within 13-months between January 2019 and February 2020, 341 water samples were collected at 22 stations during monthly cruises along the salinity gradient. Both salinity and seasonality are strongly reflected in the data. Since the dataset was generated with both metabarcoding and microscopy-based methods, it provides unique opportunities for both technical and ecological analyses, and is a valuable biodiversity reference for future studies, in the prospect of climate change.
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| 12. |
- Li, Ci, et al.
(författare)
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The Poses for Equine Research Dataset (PFERD)
- 2024
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Studies of quadruped animal motion help us to identify diseases, understand behavior and unravel the mechanics behind gaits in animals. The horse is likely the best-studied animal in this aspect, but data capture is challenging and time-consuming. Computer vision techniques improve animal motion extraction, but the development relies on reference datasets, which are scarce, not open-access and often provide data from only a few anatomical landmarks. Addressing this data gap, we introduce PFERD, a video and 3D marker motion dataset from horses using a full-body set-up of densely placed over 100 skin-attached markers and synchronized videos from ten camera angles. Five horses of diverse conformations provide data for various motions from basic poses (eg. walking, trotting) to advanced motions (eg. rearing, kicking). We further express the 3D motions with current techniques and a 3D parameterized model, the hSMAL model, establishing a baseline for 3D horse markerless motion capture. PFERD enables advanced biomechanical studies and provides a resource of ground truth data for the methodological development of markerless motion capture.
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| 13. |
- Machard, Anaïs, et al.
(författare)
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Typical and extreme weather datasets for studying the resilience of buildings to climate change and heatwaves
- 2024
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Ingår i: Scientific Data. - : Springer. - 2052-4463. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.
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| 14. |
- Mishra, Ashish Ranjan, et al.
(författare)
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SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems
- 2024
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Ingår i: Scientific Data. - : Nature Research. - 2052-4463. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality’s strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG’s resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature’s image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.
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| 15. |
- Ovaskainen, Otso, et al.
(författare)
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Global Spore Sampling Project: A global, standardized dataset of airborne fungal DNA
- 2024
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Ingår i: Scientific Data. - 2052-4463. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Novel methods for sampling and characterizing biodiversity hold great promise for re-evaluating patterns of life across the planet. The sampling of airborne spores with a cyclone sampler, and the sequencing of their DNA, have been suggested as an efficient and well-calibrated tool for surveying fungal diversity across various environments. Here we present data originating from the Global Spore Sampling Project, comprising 2,768 samples collected during two years at 47 outdoor locations across the world. Each sample represents fungal DNA extracted from 24 m3 of air. We applied a conservative bioinformatics pipeline that filtered out sequences that did not show strong evidence of representing a fungal species. The pipeline yielded 27,954 species-level operational taxonomic units (OTUs). Each OTU is accompanied by a probabilistic taxonomic classification, validated through comparison with expert evaluations. To examine the potential of the data for ecological analyses, we partitioned the variation in species distributions into spatial and seasonal components, showing a strong effect of the annual mean temperature on community composition.
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| 16. |
- Potapov, Anton M., et al.
(författare)
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Global fine-resolution data on springtail abundance and community structure
- 2024
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Ingår i: Scientific Data. - : Nature Publishing Group. - 2052-4463. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Springtails (Collembola) inhabit soils from the Arctic to the Antarctic and comprise an estimated ~32% of all terrestrial arthropods on Earth. Here, we present a global, spatially-explicit database on springtail communities that includes 249,912 occurrences from 44,999 samples and 2,990 sites. These data are mainly raw sample-level records at the species level collected predominantly from private archives of the authors that were quality-controlled and taxonomically-standardised. Despite covering all continents, most of the sample-level data come from the European continent (82.5% of all samples) and represent four habitats: woodlands (57.4%), grasslands (14.0%), agrosystems (13.7%) and scrublands (9.0%). We included sampling by soil layers, and across seasons and years, representing temporal and spatial within-site variation in springtail communities. We also provided data use and sharing guidelines and R code to facilitate the use of the database by other researchers. This data paper describes a static version of the database at the publication date, but the database will be further expanded to include underrepresented regions and linked with trait data.
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| 17. |
- Vinding, Mikkel C., et al.
(författare)
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The Swedish National Facility for Magnetoencephalography Parkinson's disease dataset
- 2024
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Parkinson's disease (PD) is characterised by a loss of dopamine and dopaminergic cells. The consequences hereof are widespread network disturbances in brain function. It is an ongoing topic of investigation how the disease-related changes in brain function manifest in PD relate to clinical symptoms. We present The Swedish National Facility for Magnetoencephalography Parkinson's Disease Dataset (NatMEG-PD) as an Open Science contribution to identify the functional neural signatures of Parkinson's disease and contribute to diagnosis and treatment. The dataset contains whole-head magnetoencephalographic (MEG) recordings from 66 well-characterised PD patients on their regular dose of dopamine replacement therapy and 68 age- and sex-matched healthy controls. NatMEG-PD contains three-minute eyes-closed resting-state MEG, MEG during an active movement task, and MEG during passive movements. The data includes anonymised MRI for source analysis and clinical scores. MEG data is rich in nature and can be used to explore numerous functional features. By sharing these data, we hope other researchers will contribute to advancing our understanding of the relationship between brain activity and disease state or symptoms.
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| 18. |
- Wu, Yu-Heng, et al.
(författare)
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A structured course of disease dataset with contact tracing information in Taiwan for COVID-19 modelling
- 2024
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 pandemic has flooded open databases with population-level data. However, individual-level structured data, such as the course of disease and contact tracing information, is almost non-existent in open databases. Publish a structured and cleaned COVID-19 dataset with the course of disease and contact tracing information for easy benchmarking of COVID-19 models. We gathered data from Taiwanese open databases and daily news reports. The outcome is a structured quantitative dataset encompassing the course of the disease of Taiwanese individuals, alongside their contact tracing information. Our dataset comprises 579 confirmed cases covering the period from January 21, to November 9, 2020, when the original SARS-CoV-2 virus was most prevalent in Taiwan. The data include features such as travel history, age, gender, symptoms, contact types between cases, date of symptoms onset, confirmed, critically ill, recovered, and dead. We also include the daily summary data at population-level from January 21, 2020, to May 23, 2022. Our data can help enhance epidemiological modelling.
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