cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10037811
title: Ljubljana Multi-Sensor Indoor and Outdoor PM Exposure, Environment, and Personal Health Dataset

description: This dataset was compiled between February 16, 2019, and May 25, 2019, involving 82 participants residing in the municipality of Ljubljana. Data collected before March 12th represents the "heating season," while data after April 27th corresponds to the "non-heating season." This dataset is the cleaned/filtered version with outliers caused by software or hardware errors removed.
Derived from the larger ICARUS project in Ljubljana, this dataset integrates various data sources, including:

Participant Questionnaires: Containing certain individual information, i.e., age, height, gender.
Time Activity Diaries: Providing hourly activity records for each participant.
Personal PM Monitors: Measuring indoor and outdoor PM concentrations.
Smart Activity Trackers: Recording heart rate and movement data.
Indoor Air Quality Station: Capturing indoor air quality parameters.
The dataset includes the calculation of inhalation rate based on heart rate data, allowing for the determination of inhalation rate-adjusted exposure or intake dose.
DOI: 10.5281/zenodo.8409844
title: Ljubljana Multi-Sensor Indoor and Outdoor PM Exposure, Environment, and Personal Health Dataset

description: This dataset was compiled between February 16, 2019, and May 25, 2019, involving 82 participants residing in the municipality of Ljubljana. Data collected before March 12th represents the "heating season," while data after April 27th corresponds to the "non-heating season."
Derived from the larger ICARUS project in Ljubljana, this dataset integrates various data sources, including:

Participant Questionnaires: Containing certain individual information, i.e., age, height, gender.
Time Activity Diaries: Providing hourly activity records for each participant.
Personal PM Monitors: Measuring indoor and outdoor PM concentrations.
Smart Activity Trackers: Recording heart rate and movement data.
Indoor Air Quality Station: Capturing indoor air quality parameters.
The dataset includes the calculation of inhalation rate based on heart rate data, allowing for the determination of inhalation rate-adjusted exposure or intake dose.
DOI: 10.5281/zenodo.10142677
title: Ljubljana Multi-Sensor Indoor and Outdoor PM Exposure, Environment, and Personal Health Dataset

description: This dataset was compiled between February 16, 2019, and May 25, 2019, involving 82 participants residing in the municipality of Ljubljana. Data collected before March 12th represents the "heating season," while data after April 27th corresponds to the "non-heating season." This dataset is the cleaned/filtered version with outliers caused by software or hardware errors removed.
Derived from the larger ICARUS project in Ljubljana, this dataset integrates various data sources, including:

Participant Questionnaires: Containing certain individual information, i.e., age, height, gender.
Time Activity Diaries: Providing hourly activity records for each participant.
Personal PM Monitors: Measuring indoor and outdoor PM concentrations.
Smart Activity Trackers: Recording heart rate and movement data.
Indoor Air Quality Station: Capturing indoor air quality parameters.
The dataset includes the calculation of inhalation rate based on heart rate data, allowing for the determination of inhalation rate-adjusted exposure or intake dose.
This version of the dataset is in .csv format.
DOI: 10.5281/zenodo.8409843
title: Ljubljana Multi-Sensor Indoor and Outdoor PM Exposure, Environment, and Personal Health Dataset

description: This dataset was compiled between February 16, 2019, and May 25, 2019, involving 82 participants residing in the municipality of Ljubljana. Data collected before March 12th represents the "heating season," while data after April 27th corresponds to the "non-heating season." This dataset is the cleaned/filtered version with outliers caused by software or hardware errors removed.
Derived from the larger ICARUS project in Ljubljana, this dataset integrates various data sources, including:

Participant Questionnaires: Containing certain individual information, i.e., age, height, gender.
Time Activity Diaries: Providing hourly activity records for each participant.
Personal PM Monitors: Measuring indoor and outdoor PM concentrations.
Smart Activity Trackers: Recording heart rate and movement data.
Indoor Air Quality Station: Capturing indoor air quality parameters.
The dataset includes the calculation of inhalation rate based on heart rate data, allowing for the determination of inhalation rate-adjusted exposure or intake dose.
This version of the dataset is in .csv format.
DOI: 10.6084/m9.figshare.21793256.v4
title: Database of foodstuffs and food systems in Africa, 1497-1840.

description: This dataset catalogues observations of foodstuffs and food systems in eastern Africa over a period of three centuries (1497-1840 CE). It compiles 2,698 reports of the presence/absence of foodstuffs, domesticates, and non-food crops across 278 sites. These observations were supplemented by information on foodstuff quantity in &gt;650 instances, with &gt;250 unique detailed extracts on methods of production and relative dietary, social-cultural and economic importance. Importantly, this combination of the breadth and depth of ‘big qualitative data’ within the historical record enables data on the materiality of foodstuffs to be investigated alongside social-cultural perspectives (e.g. gender, belief systems, the effects of colonial ways of knowing on indigenous knowledges), and the possible drivers of transitions and transformations in African food systems.
DOI: 10.6084/m9.figshare.24428047.v1
title: Suplementary file S1 - Realistic appeal of IA-generated images

description: We conducted a survey following a modified methodology from that employed by Göring et al. (2023), reducing the survey questions to the realistic appeal of AI-generated species images. Similarly to the survey by Göring et al. (2023), participants were randomized from both the university community and the general public. The survey's primary objective was not to compare models but solely to assess the general public's perception of how realistic an image of an animal species could be. No subject variables were added, and no personally identifiable information was collected or stored, in compliance with Brazil’s General Data Protection Law-LGPD (Law No. 13.709/2018). The questionnaire was made available online and distributed among academic groups and the general public for 48 hours, receiving a total of 621 responses (see supplementary material S1 for the database). Participants were asked to rate the images as: 1 - Completely false, 2 - False, but convincing, 3 - Not sure if it is real or not, 4 - Real, but highly modified, and 5 - Real, only processed.
DOI: 10.5281/zenodo.10066418
title: Data for 'Actis: A Strictly Local Union–Find Decoder'

description: The logical error (failure) and runtime data for the plots in the paper 'Actis: A Strictly Local Union–Find Decoder'.
DOI: 10.5281/zenodo.10066419
title: Data for 'Actis: A Strictly Local Union–Find Decoder'

description: The logical error (failure) and runtime data for the plots in the paper 'Actis: A Strictly Local Union–Find Decoder'.
DOI: 10.5281/zenodo.10075207
title: Data for 'Actis: A Strictly Local Union–Find Decoder'

description: The logical error (failure) and runtime data for the plots in the paper 'Actis: A Strictly Local Union–Find Decoder'.
DOI: 10.23729/29d2dcf8-3fd5-4945-86be-7cd9ee566e9f
title: Architectural metrics of Amazon trees

description: These data consist of architectural metrics obtained by segmenting and extracting 315 individual trees from terrestrial laser scanning (TLS) point clouds. Forest canopy measurements were made in April 2019, using a RIEGL VZ-400i. The data were collected in seven transects of Central Amazonian forests, located at the Biological Dynamics of Forest Fragments (BDFF) Project. The TLS point clouds were then processed using the RiSCAN PRO software version 2.9, and quantitative structure models (QSMs) of the woody structure of individual trees were developed to describe their topological, geometric and volumetric properties (Raumonen et al., 2013). Architectural metrics were then obtained from QSMs, and include DBH (diameter at breast height), tree height, trunk, branch and total tree volume, branch, trunk and total tree surface area, path fraction, asymmetry, average and maximum crown diameters, crown area and volume, crown base height and crown ratio, as well as trunk, branch and total tree length. A description of each metric can be found below. *Data structure*: *Region*: Forest site (Colosso versus Dimona sites) *Fragment size*: Fragment size (1, 10 or 100 ha) *Plot code*: Transect within the fragment. Multiple transects can be part of the same fragment *Distance from edges*: Distance of the base of a tree stem to the nearest forest fragment edge in meters *DBH*: Diameter at breast height estimates at 1.3 m height from quantitative structure modelling (QSM) *Tree height*: Total tree height of a tree in m *Total volume*: Total woody volume of trees in m3 *Trunk volume*: Woody volume of tree trunks in m3 *Branch volume*: Woody volume of tree branches in m3 *Total surface area*: Total surface area of woody components in m2 *Trunk area*: Trunk surface area in m2 *Branch area*: Total branch surface area in m2 *Path fraction*: The mean path length divided by the maximum path length of a tree. Unitless *Asymmetry*: Variable indicating volume asymmetry along the tree's vertical profile. Unitless *Average crown diameter*: Average crown diameter in m. Planar projection of the tree crown is divided into 18 cones and for each cone the maximum extent is computed and averaged over all cones *Maximum crown diameter*: Maximum horizontal crown diameter in m *Crown area*: Area of the projection's convex hull of the crown's planar in m2 *Crown volume*: Volume of the crown's convex hull in m3 *Crown base height*: Crown's base height in m from the ground *Crown ratio*: Ratio of the crown length to the tree height. Unitless *Trunk length*: Length of the stem in m *Branch length*: Total length of all the branches in m *Total tree length*: Total length of all the branches and stem in m References: Raumonen, P. et al. Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data. Remote Sensing 5, 491–520 (2013).
DOI: 10.5519/0031213
title: Linnaeus's Butterfly Type Specimens

description: ## Introduction This image database is intended primarily for taxonomists and other researchers, but the historical importance of the specimens portrayed is such that we hope they will attract a broader audience. The database contains images of the butterfly specimens upon which Linnaeus based his descriptions. Linnaeus described 305 taxa under the name _Papilio_. Of these, 241 refer to currently valid butterfly species names and two to subspecies. This site has been developed as an adjunct to a paper published on Linnaeus's butterflies, and makes images of type specimens and their labels available. Details of the publication are as follows: Linnaeus's butterflies (Lepidoptera: Papilionoidea and Hesperioidea). _Zoological Journal of the Linnean Society_, 2001, **132**: 277-399, by [Martin R. Honey](https://www.nhm.ac.uk/about-us/contact-enquiries/forms/emailform.jsp?recip=m.honey&business_title=M.Honey&email_title=Linnaean+Type+Specimens+website+enquiry) and [Malcolm J. Scoble](https://www.nhm.ac.uk/about-us/contact-enquiries/forms/emailform.jsp?recip=m.scoble&business_title=M.Scoble&email_title=Linnaean+Type+Specimens+website+enquiry). ## Sources of specimens The specimens on which Linnaeus based his descriptions are housed in several institutions. Most were found either in his personal collection, now in the Linnean Society of London, or in that of Queen Ludovica Ulrica, now in the Zoological Museum of the University of Uppsala, Sweden. Further material was examined from the collections of Carl Clerck and Charles De Geer, both housed in the Naturhistoriska Riksmuseet, Stockholm, and from the material of James Petiver, which forms part of the collection of Sir Hans Sloane, in the Natural History Museum, London. ## About the project The project was carried out at the Natural History Museum, London, in association with [The Linnean Society of London](https://www.linnean.org/), and was supported by a grant from [The Leverhulme Trust](https://www.leverhulme.ac.uk/). It is hoped that eventually all the existing primary types will be illustrated on this site. Initially, nearly all those from The Linnean Society of London have been posted. ## Labels The small labels associated with the images are by Linnaeus, while the larger ones were added subsequently by Sir James Edward Smith, who purchased Linnaeus's collection. ## Acknowledgements The authors thank the Natural Environmental Research Council for a publications grant (administered by The Linnean Society of London) that enabled this website to be developed. ## Related resources [The Cockayne database of UK butterflies](https://www.nhm.ac.uk/research-curation/projects/cockayne/). --- _This data was originally hosted at [https://www.nhm.ac.uk/research-curation/research/projects/linntypes/](https://www.nhm.ac.uk/research-curation/research/projects/linntypes/) but has been moved to the NHM Data Portal._
DOI: 10.5281/zenodo.10143096
title: Underlying dataset of Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies

description: YouTube data collection for cyberbullying studies. 
Citation:
M. Dadvar, R.B. Trieschnigg and F.M.G. de Jong,  Experts and Machines Against Bullies: A Hybrid Approach to Detect Cyberbullies. In 27th Canadian Conference on Artificial Intelligence,  University of Waterloo, Montréal, Canada, 2014
DOI: 10.5281/zenodo.10143097
title: Underlying dataset of Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies

description: YouTube data collection for cyberbullying studies. 
Citation:
M. Dadvar, R.B. Trieschnigg and F.M.G. de Jong,  Experts and Machines Against Bullies: A Hybrid Approach to Detect Cyberbullies. In 27th Canadian Conference on Artificial Intelligence,  University of Waterloo, Montréal, Canada, 2014
DOI: 10.6084/m9.figshare.24552160.v2
title: Article - MPS-Sampling

description: The data about the article of MPS-Sampling.MPS-Samping: data of the analysis from MPS-Sampling.There are two directories, one for GTDB and one for RiboDB.inputgenome_index.csvoutput4.MPS_results_directorye_value_1e-05_cov_mode_0_min_cov_0.8_min_seq_id_0.6delta_X: there is a directory for each delta from {0.05, 0.1, ... , 0.95, 1}.MPS_links.csv: A file with two columns, containing the link between each original genome (first column) and its MPS-representative (second column).MPS_representatives.csv: A file with one column, contain the MPS-representatives chosen by MPS-Sampling.phylogeny: data about the phylogenetic inference.There are four datasets: Bacillaceae, Bacterial backbone, Enterobacteriaceae and Lactobacillaceae. For each dataset, there are two files:concat_cleaned.fst: the supermatrix used for the phylogenetic inference.FastTree_iTOL.nw: the inferred phylogeny, as it was used on iTOL.
DOI: 10.5281/zenodo.7608771
title: h5ad (adata) for RNA velocity

description: adata files for each cell population and condition. The files were generated using spliced/unspliced transcripts and merged with filtered features, barcodes and counts and, pre-calculated in Seurat. One can load the datasets in python using the command below:



import scvelo as scv

adata = scv.read('uninfected_keratinocyte.h5ad')
DOI: 10.5281/zenodo.7608211
title: Processed, annotated, seurat object

description: The dataset contains an integrated, annotated Seurat v4 object. One can load the dataset into the R environment using the code below:


seurat_obj <- readRDS('PATH/TO/DOWNLOAD/seurat.rds')


The object has three assays: (I) RNA, (II) SCT and (III) integrated.
DOI: 10.5281/zenodo.7608212
title: Processed, annotated, seurat object

description: The dataset contains an integrated, annotated Seurat v4 object. One can load the dataset into the R environment using the code below:


seurat_obj <- readRDS('PATH/TO/DOWNLOAD/seurat.rds')


The object has three assays: (I) RNA, (II) SCT and (III) integrated.
DOI: 10.34740/kaggle/dsv/6981660
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.10143151
title: sheshanxuetang DAS data

description: This data is for the workshop of Sheshanxuetang in China.
For the data details please go through 
Spica, Z. J., Ajo‐Franklin, J., Beroza, G. C., Biondi, B., Cheng, F., Gaite, B., ... & Zhu, T. (2023). PubDAS: A PUBlic Distributed Acoustic Sensing Datasets Repository for Geosciences. Seismological Society of America, 94(2A), 983-998.
DOI: 10.5281/zenodo.10143152
title: sheshanxuetang DAS data

description: This data is for the workshop of Sheshanxuetang in China.
For the data details please go through 
Spica, Z. J., Ajo‐Franklin, J., Beroza, G. C., Biondi, B., Cheng, F., Gaite, B., ... & Zhu, T. (2023). PubDAS: A PUBlic Distributed Acoustic Sensing Datasets Repository for Geosciences. Seismological Society of America, 94(2A), 983-998.
DOI: 10.6084/m9.figshare.24573580.v1
title: 7SK RNA SL3 DMS-MaPseq DREEM processed data

description: Structural plasticity is integral to RNA function; however, there are currently few methods to quantitatively resolve RNAs that have multiple structural states. NMR spectroscopy is a powerful approach for resolving conformational ensembles but is size-limited. Chemical probing is well-suited for large RNAs but provides limited structural and kinetics information. Here, we integrate the two approaches to visualize a two-state conformational ensemble for the central stem-loop 3 (SL3) of 7SK RNA, a critical element for 7SK RNA function in transcription regulation. We find that the SL3 distal end exchanges between two equally populated yet structurally distinct states in both isolated SL3 constructs and full-length 7SK RNA. We rationally designed constructs that lock SL3 into a single state and demonstrate that both chemical probing and NMR data fit to a linear combination of the two states. Comparison of vertebrate 7SK RNA sequences shows either or both states are highly conserved. These results provide new insights into 7SK RNA structural dynamics and demonstrate the utility of integrating chemical probing with NMR spectroscopy to gain quantitative insights into RNA conformational ensembles.
DOI: 10.5281/zenodo.10136888
title: Rate-enhancing PETase mutations determined through DFT/MM molecular dynamics simulations†

description: Raw data for classical MD simulations ran with Gromacs 2018.3 for the two mutants Asp83Asn and Asp89Asn.
Raw data for quantum mechanics/molecular mechanics simulations ran with CP2K 6.1 for the two mutants Asp83Asn and Asp89Asn.
Distance and free energy analysis from the QM/MM MD simulations  for the wild-type and the two mutants Asp83Asn and Asp89Asn.
DOI: 10.5281/zenodo.10136887
title: Rate-enhancing PETase mutations determined through DFT/MM molecular dynamics simulations†

description: Raw data for classical MD simulations ran with Gromacs 2018.3 for the two mutants Asp83Asn and Asp89Asn.
Raw data for quantum mechanics/molecular mechanics simulations ran with CP2K 6.1 for the two mutants Asp83Asn and Asp89Asn.
Distance and free energy analysis from the QM/MM MD simulations  for the wild-type and the two mutants Asp83Asn and Asp89Asn.
DOI: 10.5281/zenodo.7608772
title: h5ad (adata) for RNA velocity

description: adata files for each cell population and condition. The files were generated using spliced/unspliced transcripts and merged with filtered features, barcodes and counts and, pre-calculated in Seurat. One can load the datasets in python using the command below:



import scvelo as scv

adata = scv.read('uninfected_keratinocyte.h5ad')
DOI: 10.5281/zenodo.10060734
title: Carbon storage in old hedgerows: The importance of below-ground biomass

description: Dataset to the manuscript: Drexler, S., Thiessen, E., & Don, A. (2023). Carbon storage in old hedgerows: The importance of below-ground biomass. GCB Bioenergy. https://doi.org/10.1111/gcbb.13112

Drexler_et_al_2023-cn_biomass: contains the data on the biomass C/N measurements
Drexler_et_al_2023-overallstocks: contains the calculated carbon stocks per subplot for all carbon pools
Drexler_et_al_2023-soc_cropland: contains the calculated soil organic carbon stocks (0-100cm soil depth) of the reference cropland
Drexler_et_al_2023-soc_weight_fine_roots: contains the raw data on the dry weight of the fine roots and the raw data on the soil samples (C/N data, dry weight, stone/root fraction) per subplot and sampling depth
Drexler_et_al_2023-weight_above_ground_biomass: contains the raw data on the dry weight of the harvestable biomass and biomass of the mature trees per subplot
Drexler_et_al_2023-weight_coarse_roots_litter: contains the raw data on the dry weight of the coarse roots, litter and stumps per subplot
DOI: 10.5281/zenodo.10060735
title: Carbon storage in old hedgerows: The importance of below-ground biomass

description: Dataset to the manuscript: Drexler, S., Thiessen, E., & Don, A. (2023). Carbon storage in old hedgerows: The importance of below-ground biomass. GCB Bioenergy. https://doi.org/10.1111/gcbb.13112

Drexler_et_al_2023-cn_biomass: contains the data on the biomass C/N measurements
Drexler_et_al_2023-overallstocks: contains the calculated carbon stocks per subplot for all carbon pools
Drexler_et_al_2023-soc_cropland: contains the calculated soil organic carbon stocks (0-100cm soil depth) of the reference cropland
Drexler_et_al_2023-soc_weight_fine_roots: contains the raw data on the dry weight of the fine roots and the raw data on the soil samples (C/N data, dry weight, stone/root fraction) per subplot and sampling depth
Drexler_et_al_2023-weight_above_ground_biomass: contains the raw data on the dry weight of the harvestable biomass and biomass of the mature trees per subplot
Drexler_et_al_2023-weight_coarse_roots_litter: contains the raw data on the dry weight of the coarse roots, litter and stumps per subplot
DOI: 10.5281/zenodo.7817746
title: MedProcNER/ProcTEMIST Corpus: Gold Standard annotations for Clinical Procedures Information Extraction

description: <strong>MedProcNER</strong> stands for MEDical PROCedure Named Entity Recognition. It is a shared task and set of resources focused on the detection, normalization and indexing of clinical procedures in medical documents in Spanish. MedProcNER is complementary to the DisTEMIST corpus (https://temu.bsc.es/distemist) as they both use the same document collection, which is why it's also called ProcTEMIST. This repository includes the <strong>Training Set</strong> for Subtask 1 (NER) in two different formats (brat, .tsv). For more information, please check the attached README file. Training data for subtask 2 and 3 will be released soon. MedProcNER was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioASQ @ CLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/medprocner. <strong>Related Links:</strong> - MedProcNER website: https://temu.bsc.es/medprocner - MedProcNER Guidelines: https://doi.org/10.5281/zenodo.7817666 <strong>License</strong> This work is licensed under a Creative Commons Attribution 4.0 International License. <strong>Contact</strong> If you have any questions or suggestions, please contact us at: - Salvador Lima-López (&lt;salvador [dot] limalopez [at] gmail [dot] com&gt;)<br> - Martin Krallinger (&lt;krallinger [dot] martin [at] gmail [dot] com&gt;)