cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8867660.v2
title: Data from: Relative brain size and cognitive equivalence in fishes

description: There are two well-established facts about vertebrate brains: brains are physiologically costly organs, and both absolute and relative brain size varies greatly between and within the major vertebrate clades. While the costs are relatively clear, scientists struggle to establish how larger brains translate into higher cognitive performance. Part of the challenge is that intuitively larger brains are needed to control larger bodies without any changes in cognitive performance. Therefore, body size needs to be controlled for in order to establish the slope of cognitive equivalence between animals of different sizes. Potentially, intraspecific slopes provide the best available estimate of how an increase in body size translates into an increase in brain size without changes in cognitive performance. Here, we provide slope estimates for brain-body sizes and for cognition-body in wild-caught “cleaner” fish <i>Labroides dimidiatus</i>. The cleaners’ cognitive performance was estimated from four different cognitive tasks that tested for learning, numerical, and inhibitory control abilities. The cognitive performance was found to be rather independent of body size, while brain-body slopes from two datasets gave the values of 0.58 (MRI scans data) and 0.47 (dissection data). These values can hence represent estimates of intraspecific cognitive equivalence for this species. Furthermore, another dataset of brain-body slopes estimated from 14 different fish species, gave a mean slope of 0.5, and hence rather similar to that of cleaners. This slope is very similar to the encephalisation quotients for ectotherm higher taxa, i.e. teleost fishes, amphibians and reptiles (~ 0.5). The slope is much higher than what has been found in endotherm vertebrate species (~ 0.3). Together, it suggests that endo- and ectotherm brain organisations and resulting cognitive performances are fundamentally different.
DOI: 10.17632/h2tf5ymcp9
title: Balinese Story Texts Dataset - Characters, Aliases, and their Classification

description: This dataset consists of 120 Balinese story texts (as known as Satua Bali) which have been annotated for narrative text analysis purposes, including character identification, alias clustering, and character classification into protagonist or antagonist. The labeling involved two Balinese native speakers who were fluent in understanding Balinese story texts. One of them is an expert in the fields of sociolinguistics and macrolinguistics. Reliability and level of agreement in the dataset are measured by Cohen's kappa coefficient, Jaccard similarity coefficient, and F1-score and all of them show almost perfect agreement values (&gt;0,81).
There are four main folders, each used for different narrative text analysis purposes:
1. First Dataset (charsNamedEntity): 89,917 annotated tokens with five character named entity labels (ANM, ADJ, PNAME, GODS, OBJ) for character named entity recognition purpose
2. Second Dataset (charsExtraction): 6,634 annotated sentences for the purpose of character identification at the sentence level
3. Third Dataset (charsAliasClustering): 930 lists of character groups from 120 story texts for the purpose of alias clustering
4. Fourth Dataset (charsClassification): 848 lists of character groups that have been classified into two groups (Protagonist and Antagonist)
DOI: 10.17632/zh3rc7hvrm
title: HiPACE++: A portable, 3D quasi-static particle-in-cell code

description: Modeling plasma accelerators is a computationally challenging task and the quasi-static particle-in-cell algorithm is a method of choice in a wide range of situations. In this work, we present the first performance-portable, quasi-static, three-dimensional particle-in-cell code HiPACE++. By decomposing all the computation of a 3D domain in successive 2D transverse operations and choosing appropriate memory management, HiPACE++ demonstrates orders-of-magnitude speedups on modern scientific GPUs over CPU-only implementations. The 2D transverse operations are performed on a single GPU, avoiding time-consuming communications. The longitudinal parallelization is done through temporal domain decomposition, enabling near-optimal strong scaling from 1 to 512 GPUs. HiPACE++ is a modular, open-source code enabling efficient modeling of plasma accelerators from laptops to state-of-the-art supercomputers.
DOI: 10.6084/m9.figshare.11384136.v2
title: Building(s and) cities: Delineating urban areas with a machine learning algorithm - City & Employment Centre Boundaries (v1)

description: City and employment centre boundary delineations for Spain (except Basque Country and Navarra), based on building footprint from the Cadastre, and created using the A-DBSCAN algorithm.<br>GeoJSON polygons expressed in WGS-84 (ie. lon/lat), GeoPackage polygons expressed in ETRS89 / UTM zone 30N (EPSG:25830).<br>Files included in this dataset:<br>- <code>sp_blg_adbscan_city_boundaries_v1.geojson</code>: city boundaries in GeoJSON format. Each boundary contains a city ID (<code>city_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>- <code>sp_blg_adbscan_city_boundaries_v1.gpkg</code>: city boundaries in GeoPackage format. Each boundary contains a city ID (<code>city_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>-<code> sp_blg_adbscan_emp_centre_boundaries_v1.geojson</code>: employment centre boundaries in GeoJSON format. Each boundary contains a city ID (<code>city_id</code> in the table), an employment centre ID (<code>centre_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>- <code>sp_blg_adbscan_emp_centre_boundaries_v1.gpkg</code>: employment centre boundaries in GeoPackage format. Each boundary contains a city ID (<code>city_id</code> in the table), an employment centre ID (<code>centre_id</code> in the table) and the number of building footprints it contains (<code>n_buildings</code> in the table).<br>For further reference, please see original paper:<br>Arribas-Bel, D.; Garcia-Lopez, M. A.; Viladecans-Marsal, E. (2020). "Building(s and) cities: Delineating urban areas with a machine learning algorithm". <i>Journal of Urban Economics.</i><br>
DOI: 10.6084/m9.figshare.23972991.v1
title: Nanostring CosMx

description: We collected additional tumor tissues from the initial resection HCC patients for Nanostring CosMx.
DOI: 10.6084/m9.figshare.3085162
title: Editorial

description: Editorial
DOI: 10.17632/8n9rhhyrfz
title: The Social Network Analysis in Disaster Management

description: this study aims to propose the use of Social Network Analysis (SNA) as an assessment tool to answer the following research questions: (i) Who are the stakeholders in a Humanitarian Operations Network (HON)?; (ii) Which actors exert influence on a HON?; and (iii) How does a HON forms?
DOI: 10.17632/wwb55b7tg4
title: Does Democracy Shape International Merger Activity?

description: This package includes data and code to replicate the results in the paper "Does Democracy Shape International Merger Activity?".
DOI: 10.6084/m9.figshare.19119770.v4
title: GBS sample data

description: A random subset (25M reads per file) of an existing real dataset. The dataset comprises Genotyping by Sequencing (GBS) data for 34 sheep from a high and a low selection line. The GBS library was created using a <i>Pst</i>I-<i>Msp</i>I double-digest followed by a size selection step. The dataset comprises several trios (dam, sire, progeny).Also provided are a set of bash, R, python helper scripts to go from fastq files to allelic depth information and arrive at a genomic relationship matrix.<br>Any data shared here is without warranty of any kind and in no event shall the authors or copyright holders be liable for any claim, damages or any liability.<br><br>
DOI: 10.6084/m9.figshare.22146599.v4
title: Supplementary Files for "Metagenome-assembled genomes of three Hepatoplasmataceae provide insights into isopod-mollicute symbiosis"

description: <b>mollicutes.phylogenomic.iqtree.treefile:</b> Maximum-likelihood phylogenomic tree of 155 mollicutes based on 41 single-copy proteins, provided in the Newick format<b>faa_aligned_trimmed.zip: </b>Multiple sequence alignments of 41 single-copy protein-coding genes conserved across 155 mollicutes<b>16S.iqtree.treefile:</b> Maximum-likelihood phylogenetic tree of mollicute 16S rRNA sequences, provided in the Newick format<b>16S.aligned.trimmed.fa: </b>Multiple sequence alignment of mollicute 16S rRNA sequences<b>methods.html and </b><b>methods.md</b><b>: </b>Bioinformatic scripts used in this study, provided in the HTML and Markdown formats, respectively.<b>PhyloFlash</b><b>.zip: </b>PhyloFlash output files for isopod Illumina shotgun sequencing data<b>BDV029231.1.zip: </b>ColabFold output files for the beta-barrel-containing protein BDV029231.1<b>HCTKY_2320_Ig</b><b>.zip, </b><b>TYPL_3910_sandwich</b><b>.zip, </b><b>and </b><b>HPPSJP_2240_Ig</b><b>.zip: </b>ColabFold output files for the beta-sandwich structures in <i>Hepatoplasmataceae</i> large, repetitive ORFs<br><b>Supplementary_Meterials</b><b>.pdf: </b>PDF file containing Supplementary Figure 1 and Supplementary Tables 1-5
DOI: 10.6084/m9.figshare.26964265.v1
title: <b>English housing, sociodemographic, environmental and mental health data at LSOA level</b>

description: This section describes the mental health, housing, sociodemographic and environment variables and the data sources from which they are derived. All data comes from open-source databases. Where possible, data from 2015 has been used to match ONS data on indices of deprivation (only available in 2010, 2015 and 2019). Data from 2015 was not available for EPCs, household size, and environmental exposures.Clinical mental health data is available from the Place-based Longitudinal Data Resource (PLDR), managed by the University of Liverpool, which provides annual data from a variety of sources (e.g. hospital admissions data, budgetary data, prescribing data) at LSOA level. It is used by researchers, practitioners, and government to track changes in health indicators over time. https://pldr.org/search?type=dataset Housing and home energy efficiency data is derived from EPCs, a requirement for homes built, sold, or rented since the introduction of the policy in 2008 (HM Government 2024). EPC data were aggregated using medians, modes, and proportions at LSOA level for this study. The EPC dataset is comprised of 24,736,816 homes, with EPCs recorded between 1st October 2008 and 31<sup>st</sup> May 2023. For homes with multiple EPCs (N=3,798,313), the most recent is used, but missing data is filled from the most recent previous certificates. EPCs were linked to 2011 census LSOAs by postcode using the National Statistics Postcode Lookup (ONS 2022). https://epc.opendatacommunities.org/Demographic data such as age, gender and deprivation indicators originate from the 2011 census or other Office for National Statistics (ONS) sources.The Access to Health Assets and Hazards (AHAH v2) dataset was used for environmental exposures including air pollution and access to green space (Daras et al. 2019). AHAH is a multi-dimensional index developed by the Consumer Data Research Centre (CDRC) for Great Britain. It is used to provide an index for how ‘healthy’ a local area is. https://pldr.org/dataset/2z9yv/ahah-version-2-access-to-healthy-assets-and-hazardsFinally, HadUK-Grid data at 1x1 km resolution was used to estimate winter (December, January and February) ambient temperatures (Met Office 2023).<br><br>
DOI: 10.6084/m9.figshare.3525413.v1
title: Appendix A. A table showing summary effect sizes (Hedge’s D and 95% confidence intervals in parentheses) for each of five species interactions on the congeneric native-introduced plant pairs.

description: A table showing summary effect sizes (Hedge’s D and 95% confidence intervals in parentheses) for each of five species interactions on the congeneric native-introduced plant pairs.
DOI: 10.6084/m9.figshare.16903246.v1
title: Bird_Forest_Data

description: Data set used for the paper "Deforestation alters species interactions". Data collected from the Atlantic Forest of Brazil.
DOI: 10.6084/m9.figshare.21809361.v1
title: LTSfire_Hong_Kong_BA_polygons

description: Shapefile for burn areas (BAs) of Hong Kong between 1987 and 2020 detected by the LTSfire pipeline. Estimated date of detection is stored in the attributes of the shapefile polygons.
DOI: 10.6084/m9.figshare.24967083.v1
title: Anexo1_review.docx

description: List of review articles
DOI: 10.6084/m9.figshare.29154506.v1
title: Polllen preference score and interactions

description: This excel file contains the preference score of each bee species that is/will be modelled in ALMaSS. Each pollen score was calculated according to the following strategy: 1. Plant-pollinator interactions will set the baseline for the pollen preference. If a pollinator has never interacted with the plant species, a preference score of 0 is set and no further analysis is performed. If a pollinator interacts with a particular plant species, a score of 1 is assigned.2. The P:L ratio is then applied according to the literature data on pollinator preferences. If the P:L ratio is within the range of pollinator preferences, a score of 1 is added and the plant species will have a higher preference value (2). If not, it retains the same score as before (1).3. The minerals content is then used by giving preference to lower K:Na ratios. Taking into account the plant species visited by the pollinator (point 1), and that bees prefer to visit plants with a lower K:Na ratio, plant species with K:Na ratio lower than the mean are given a score of 1. Plant species with higher than the mean K:Na ratio do not receive any score. In the end, each plant species can have a score from 0 to 3, which determines the level of preference of the pollinator: 0 = no interaction, 1 = poorly preferred, 2 = moderately preferred and 3 = Strongly preferred.Since was not possible to use the nutritional approach for the other species that are being modelled (<i>Eristalis tenax, Sphaerophoria scripta, Noctua pronuba </i>and<i> Pieris napi</i>), the excel file only contains the presence/absence data of interactions.
DOI: 10.25452/figshare.plus.17871707.v1
title: Side low color images supporting "Functional movement screen dataset collected with two Azure Kinect depth sensors"

description: This is color images of Azure Kinect sensor in side low position. <br> This dataset supports the following publication: Xing, QJ., Shen, YY., Cao, R. et al. Functional movement screen dataset collected with two Azure Kinect depth sensors. Sci Data 9, 104 (2022). https://doi.org/10.1038/s41597-022-01188-7 <br>See related materials in collection at: https://doi.org/10.25452/figshare.plus.c.5774969 <br>Collection Description: This presents a dataset for vision-based autonomous functional movement screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up, and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, and 3D human skeleton joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, three-dimensional trajectories, quaternions, and 2D pixel trajectories of 32 joints were recorded. Our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 158 GB. As a supplement, we also provide color image data from the other two cameras (back and side low positions). This dataset provides the opportunity for automatic action quality evaluation of FMS.
DOI: 10.6084/m9.figshare.17091053.v1
title: Supplementary Material for: Safely Inserting Neonatal Chest Drains

description: <b><i>Background:</i></b> Inserting a chest drain for a left-sided neonatal pneumothorax carries a risk of penetrating the pericardium. We identified reference ranges for the chest wall thickness (CWT) and distance between the pericardium and parietal pleura to improve safety of chest tube insertion. <b><i>Method:</i></b> We prospectively measured the CWT using ultrasound in 20 neonates (body weight [BW] 640–2,700 g, age &lt;10 days) at the usual site of puncture in the 4th and 5th intercostal space (ICS). Furthermore, we measured the minimal distance between the parietal pleura and the cardiac silhouette in 131 neonatal chest X-rays (birth weight, 420–4,930 g [divided into 11 weight groups]; age &lt;10 days). Both data sets were transformed into weight-dependent percentiles (Ps). We considered the difference between the sum of P 2.5 for the CWT plus P 2.5 for pleura-heart distance minus P 97.5 for the CWT as a safe corridor for placing the tip of the needle. <b><i>Results:</i></b> At both ICSs, curves for the above metrics did not cross, indicating a narrow but safe corridor for each BW with at least 97.5% probability. This safety corridor was 4.6–5.2 mm wide for the 4th and 2.8–3.4 mm for the 5th ICS. <b><i>Conclusion:</i></b> These data offer a reference for left-sided chest drain insertion for BW &lt;2,700 g, which may help to improve safety of the procedure.
DOI: 10.6084/m9.figshare.17903834.v4
title: Rat 3D Tracking & E-Phys KISN 2020 Dataset

description: This dataset was collected in 2020 in the Whitlock Lab at the Kavli Institute for Systems Neuroscience (KISN) in Norway by Bartul Mimica and Tuce Tombaz. In the "data_files" directory, each pickle file is a separate (~20 min) recording session of an individual rat moving freely in a 2x2 m octagonal arena, with seven 3D motion captured body points, head and back Euler angles derived from such tracked points, inertial measurement unit-derived head Euler angles, sound stimulus presentation, spiking activity etc. In the "cluster_group_identity_files" directory, one can find whether individual clusters were classified as single units ("good") or multi-unit activity ("mua"). Further details about data structure can be found in the "data_description.pdf" file.
DOI: 10.6084/m9.figshare.23546376.v1
title: Raw data of American Toad acoustic analyses

description: Raw data employed for tables and analyses.
DOI: 10.6084/m9.figshare.24266164.v1
title: Test data of VPP providing regulation

description: Test data used in the case study of my paper "Exploiting Heterogeneous Storage-like Resources: Optimal Operating Strategy for Virtual Power Plants to Provide Regulation Services".
DOI: 10.17632/kbdpg6c2wz
title: Consumer Perception towards Electric Vehicle - with special reference to Chennai city

description: Every industry has been modernized through technological innovation. The auto industry is also impacted in a similar 
way. In order to prevent global warming and climate change, the Indian government mandates that people move from internal 
combustion to electric automobiles. This is because electric vehicles are the future. The automobile manufacturers slowly shifting 
their production from tradition automobiles to electric one. By 2030, Government of India plans to have majority of electric vehicle 
in India. The government also motivate the people by providing incentives and tax benefits. If automobiles are electrified, even the 
import of fuels will be reduced. The high price of gasoline and environmental concern might be few reasons which intends public 
to adopt electric vehicle. The present study throws a light on consumer awareness towards electric vehicle and it also tries to 
identify the factors that influence the consumer perception towards Electric Vehicle. This study uses a descriptive approach to 
gather information from consumers by using a structured questionnaire. For the purpose of the study, 123 samples are collected 
from the Chennai city. The information gathered will be then analyzed to determine the expected outcomes of the study. The findings 
of the study will be beneficial to the government, automotive manufacturers, dealers and marketer. In this study, the researchers found that young females were major respondents. Nearly 87% of them were aware about Electric Vehicle and they get to know about Electric Vehicle through Social Media platforms followed by Television, Friends & Relatives. The First Factor is Conducive Factor. It shows that consumers were motivated by certain factors such as Easy to drive, 
Environmental Friendliness, Reduced dependence on Fossil Fuels, Government Incentives & Tax Benefits and therefore these 
variables create positive perception among the consumers to adopt Electric Vehicle.
The Second Factor is Hindering Factor. It shows that the consumers were discouraged by the certain factors such as Lack of 
charging station, Maintenance cost, Price of the Vehicle, Inconvenience to use for longer distance and these variables create negative 
perception about Electric Vehicle among the consumers. The findings revealed that there is significant relationship between age group and the conducive factor affecting consumers' 
perceptions toward electric vehicles. And found that, Conducive Factor highly influences the young adults followed by other age 
groups. Since, young adults are the future pillars of the society, they are having positive attitude towards Electric Vehicle and it is 
believed that they may adopt Electric Vehicle in the future.
India's EV drive will open up a lot of business opportunities in the mobility, infrastructure, and nd energy sectors if electric vehicle 
automobiles take off. And now consumers are switching from fuel-powered automobiles to electric vehicles.
DOI: 10.6084/m9.figshare.1304173.v4
title: Klout Score-ranking of the top 15 science stars of Twitter

description: The Klout scores of the top fifteen scientists using Twitter (based on the number of Twitter followers; source: http://news.sciencemag.org/scientific-community/2014/09/top-50-science-stars-twitter) was retrieved (on 7th of February, 2015; source: https://klout.com/home) and used for ranking in ascending order (with number one corresponding to the highest Klout score).
DOI: 10.6084/m9.figshare.29137331.v1
title: GIS files right side

description: GIS files right side
DOI: 10.6084/m9.figshare.4519649.v1
title: Effects of Self-Control Training Data

description: Data from a study examining the reliability of self-control training effects. Participants were randomly assigned to either a training or control group and measures were taken. Participants then engaged in training or control condition-specific instructions for a period of two weeks, and received compliance reminders and provided compliance self-reports. After two weeks, measures were re-taken. Participants were told to stop engaging in the condition specific instruction, and then returned two weeks later for a final, third measure.<br>