cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/kc5chp4tzd
title: The multiplex stock market network

description: The data set consists of 465 stocks that were continuously traded on the S&P500 Index for the period from April 8, 2013 to March 10, 2023. The entire data set was divided into two data sets covering the period starting April 8, 2013 and ending March 8, 2018, with 1240 trading days, for data set 1, and from March 9, 2018 to March 10, 2023, with 1260 trading days (approximately 5 years), for data set 2. The 465 companies remaining in the dataset were included in the S&P500 index on the last day of the time span, and each stock has 2500 data points. The data set for a single observation of each stock contains: i) daily closing price adjusted for splits and dividends; ii) daily trading volume. In addition, data were collected on: a) daily S&P500 index adjusted close prices; and b) interest rates on the 13-week Treasury Bill. All the networks (1) idiosyncratic return; 2) volatility; 3) trading volume; 4) multiplex stock market network, and 5) cross-correlation of stock return) were constructed using data set 2. However, the idiosyncratic return network construction methodology requires stock prices, S&P500 indexes, and the 13-week Treasury Bill returns data to be obtained for an additional period encompassing data set 1. These historical data were collected from Yahoo Finance (https://finance.yahoo.com; accessed on 11.03.2023).

The supplementary data set comprises data on the prices of 456 stocks, stock returns, and Sharpe ratios, derived from out-of-sample data spanning 252 days, with a one-year lag from the last data point employed in the construction of the financial networks. The time frame for the out-of-sample data is March 13, 2023, to March 12, 2024. The data was collected from Yahoo Finance https://finance.yahoo.com; accessed on 08.05.2024). In addition, data for two centrality measures are included: degree centrality and eigenvector centrality for two networks: i) the multiplex stock market network, and ii) the cross-correlation of stock return.
The additional data file contains degree data for all networks and layers needed to perform an analysis of robustness to network failure.
DOI: 10.6084/m9.figshare.1332753.v1
title: Vector grid of SRTM 1x1 degree tiles

description: Vector file (shapefile format) of polygons representing the 1x1 degree tiles of SRTM (3-arcsec and 1-arcsec). There are 14280 polygons with an ID that matches the naming scheme of SRTM (such as N00E025). Lat/Long, WGS84.
DOI: 10.17632/9fnxr9xc2r
title: MontePython: Implementing Quantum Monte Carlo using Python

description: Abstract We present a cross-language C++/Python program for simulations of quantum mechanical systems with the use of Quantum Monte Carlo (QMC) methods. We describe a system for which to apply QMC, the algorithms of variational Monte Carlo and diffusion Monte Carlo and we describe how to implement theses methods in pure C++ and C++/Python. Furthermore we check the efficiency of the implementations in serial and parallel cases to show that the overhead using Python can be negligible. Title of program: MontePython Catalogue Id: ADZP_v1_0 Nature of problem Investigating ab initio quantum mechanical systems, specifically Bose-Einstein condensation in dilute gases of 87 Rb. Versions of this program held in the CPC repository in Mendeley Data ADZP_v1_0; MontePython; 10.1016/j.cpc.2007.06.013 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/f5ft73ntck
title: Attributions of Intent and Moral Responsibility to AI agents

description: Datasets used to investigate how an AI agent's level of intentionality and socio-psychological features impacted observers' judgments of it when it transgressed (Study 1). Studies 2a-c investigated 1) the distinctiveness of the AI agent's mind from the mind of its programmers and 2) observers' perceptions of the AI agent's free will as possible explanations for why an AI agent that was embedded in a human social network (vs. not) appeared to receive more lenient judgments.
DOI: 10.17632/g73sdncgxg
title: The State of Carbon Neutrality in the Greater Toronto Hamilton Area for 2016

description: A methodology was developed for creating municipality-scale emission inventories using publicly available online data. A bottom-up emissions inventory of prominent greenhouse gases (carbon dioxide, methane, and nitrous oxide) and criteria air contaminants (carbon monoxide and oxides of nitrogen) in the Greater Toronto Hamilton Area (GTHA) was developed for the year 2016. Emissions from agriculture, buildings, ecosystems, industries, and transportation were estimated in layers and then summed in each individual grid square. The inventory used non-proprietary data and was distributed on a fine grid (four square kilometre grid cells). It was estimated that the GTHA produced 58.6 megatonnes of carbon dioxide equivalent, 194,460 tonnes of carbon monoxide, and 106,140 tonnes of nitrogen oxides for the study year. Traffic produced the most of all pollutants except methane, which was dominated by waste (landfills). The inventory was validated against published inventories from Environment and Climate Change Canada (ECCC) and The Atmospheric Fund. The developed methodology can be used by other municipalities to assess their state of carbon neutrality and air pollution.
This dataset includes the emissions found for each grid cell, located by latitude and longitude. The exact area of each grid cell is included along with the values of carbon monoxide, nitrogen oxides, carbon dioxide, methane, and nitrous oxide. The values are normalized by population and area, respectively. Greenhouse gases are combined into megatonnes of carbon dioxide equivalent per square kilometre. A shapefile of the data distributed in grid cells over the Greater Toronto Hamilton Area in Ontario, Canada is included to aid visualization.
DOI: 10.6084/m9.figshare.19090052.v1
title: A 3D deep learning approach incorporating coordinate information to improve the segmentation of pre- and post-operative abdominal aortic aneurysm

description: Dataset of abdominal aortic aneurysm-Non-contrast CT: train 200 cases, test 20 cases-Contrast enhanced CT: train 200 cases, test 20 cases-Non-contrast of post EVAR CT: train 20 cases, test 20 cases-Contrast enhanced of post EVAR CT 20 cases and test 20 cases.
DOI: 10.6084/m9.figshare.27901113.v1
title: SIMPLI output files

description: Output files generated by the automated image processing software SIMPLI (https://github.com/ciccalab/SIMPLI - https://doi.org/10.1038/s41467-022-28470-x).<b>area_measurements: </b>quantification of total pixel area in the ROI positive for each marker used. These quantification was used to determine the total deposition of amyloid-b and pTau (Fig. S4.B-I), as well as synapses markers (NTNG2 and Synaprophysin; Fig. S3.A) and myelin (PLP1; Fig. S6.E).<b>clustered_cells: </b>list of all nuclei segmented in all ROIs (rows). Mean signal instensity per marker analysed is indicated in the "Intensity_MeanIntensity_" columns. This quantification was used for nuclei clustering by Seurat integrated in SIMPLI (cluster # to which each cell was assiged is indicated in the "res_0.9_ids" column, which indicate the clustering resolution; Fig. S2.A). X and Y location of segmented nuclei in the ROI is also indicated in the "Location_Center_X" and "Location_Center_Y" columns, which was used to determine nuclei distribution in each cortical layer (Fig. 2.D; Fig. 3. B,D,F; Fig. S6.H). The quantification of mean signal instensity in the "Intensity_MeanIntensity_Ab" and "Intensity_MeanIntensity_pTau" columns was used to quantify intracellular signal for amyloid-b and pTau respectively (Fig. 4).<b>all_cells-clusters: </b>same as clustered_cells.csv file but with the correct format to be used for spatial interactions analyses using the imcRtools package built by the Bodenmiller group. Spatial interactions were quantified between neuronal and glial populations (Fig. 5.e and Fig. S6.f,g).<br><br>
DOI: 10.6084/m9.figshare.741259.v1
title: Accredited journals published/co-published in South Africa: access status (Version 1.1)

description: This spreadsheet lists the South Africa published or co-published journals on the 2013 DHET, ISI and IBSS list (accredited). Whether the journal is open access, with some or all articles freely available online and where (the journal site, African Journal Archive, ScieloSA and Sabinet Open were checked) were also recorded, where possible. <br>This spreadsheet was produced by Sarah Goodier for OpenUCT (http://openuct.uct.ac.za). The data in this sheet was compiled from the 2013 DHET, ISI and IBSS list (obtained from the UCT Research Office), through online searching and examination of the journal websites (where possible). While every effort has been made to assure that the information in this spreadsheet is accurate and up to date, there may be instances where data (e.g. email addresses, access policies) are not current and changes have occured. Please visit the journal website to view the most up to date information available or contact the journal. <br>If you have any questions regarding our methods or an update or correction for this spreadsheet, please feel free to contact us on openuct@uct.ac.za Note: For the "Open Access?" column, Yes = open access (freely available, with a creative commons license); (Yes) = some/all full text freely available online; No = not open access and no full text available via the journal site, African Journal Archive, ScieloSA or Sabinet Open.
DOI: 10.17632/3hymgv47jv
title: Mixed messages: Unmixing sedimentary molecular distributions reveals source contributions and isotopic values

description: This dataset contains previously published data and results of statistical analysis of these data described in the manuscript "Mixed messages: Unmixing sedimentary molecular distributions reveals source contributions and isotopic values." Please see the manuscript for description of the datasets and the analysis.
DOI: 10.6084/m9.figshare.11860572.v1
title: A high-density Surface EMG dataset of upper-limb muscles during isometric contractions in healthy humans: Part1/3

description: This dataset comprises HD-EMG recordings of the first six subjects (S1-S6) of twelve healthy male volunteers performing four isometric contractions tasks (extension and flexion of the elbow and pronation and supination of the forearm) at 10%, 30%, and 50% of the maximum voluntary contraction.<br>Signals of the upper arm (biceps and triceps brachii) and the forearm (brachioradialis, pronator teres, and anconeus) muscles were recorded using three 2D arrays of electrodes.<br>For more information, please refer to the readme file.<br>Related dois:Part 2/3: 10.6084/m9.figshare.11860851<br>Part 3/3: 10.6084/m9.figshare.11860959
DOI: 10.6084/m9.figshare.13352663.v1
title: Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach

description: These codes and data are aimed at computing suitable indoor landmarks for users of different familiarity. The descriptions of files are as follows:<b>rel_ranking_familiarity.py</b>: the source code to train models;<b>hyperparams</b>: this fold includes <i>lgbrank_familiar_best_params.csv</i> and <i>lgbrank_unfamiliar_best_params.csv </i>files, which store the hyperparameters for 17 fold cross_validation in the familiar datase and in the unfamiliar dataset, respectively;<b>17_fold_dataset</b>: this fold includes all the dataset for 1-_fold_cross_validation, in each fold <i>familiar_rank.train.query</i> and <i>familiar_rank.train</i> are for training in the familiar dataset,<i> unfamiliar_rank.train.query </i>and<i> unfamiliar_rank.train</i> are for training in the unfamiliar dataset, <i>familiar_rank.test.query </i>and <i>familiar_rank.test </i>are for testing in the familiar dataset,<i> unfamiliar_rank.test.query</i> and <i>unfamiliar_rank.test </i>are for testing in the unfamiliar dataset.<br><br>
DOI: 10.6084/m9.figshare.17263433.v1
title: Soil Moisture.xlsx

description: Soil moisture measurements recorded by TDR 150 soil moisture meter.
DOI: 10.6084/m9.figshare.25604424.v1
title: Cervical Cancer Data: Impact of cervical cancer education

description: A pre- and post-test quasi experimental study was conducted at two Ghanaian Senior High Schools. An educational intervention was carried out comprising a drama, PowerPoint lecture, question and answer session and cervical cancer information leaflet distribution. A self-administered questionnaire was given as a pre-test and repeated as a post-test after 3 months. The total score for each domain of knowledge tested was categorised into adequate knowledge (≥50%) and inadequate knowledge (&lt;50%).
DOI: 10.6084/m9.figshare.3522590.v1
title: Appendix A. A description of the simulation design.

description: A description of the simulation design.
DOI: 10.17632/8f93rjhgjk
title: Turkish Dataset for Identification of Author Gender

description: The IAG-TNKU Dataset is a large collection of Turkish news articles that can be used in different Turkish Text Classification NLP tasks such as Identification of Author Gender In Turkish News. The text data belong to 32 female and 38 male authors, has been extracted from the archive of a newspaper (www.hurriyet.com.tr) for the interval 08.11.1997 and 24.04.2019. The dataset divided into males and females in a balanced way consists of a total of 43.292 articles. How to use the IAG-TNKU Dataset: ___________ 1. Unzip compressed resources. 2. There are two folder (Females and Males) 3. Each folder contains a set of article files in .txt formatted corresponding to its category.
DOI: 10.17632/3jykz7wz8d
title: DATASET for "Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment"

description: Here we provide the entire dataset and code for the article "Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment" published in Computers in Biology and Medicine (DOI: 10.1016/j.compbiomed.2021.104623). Any results obtained using the published dataset MUST cite the original research article (https://www.sciencedirect.com/science/article/abs/pii/S0010482521004170). See the ReadMe file "readme.txt" for all details. Abstract: "Ultrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert operator, allowing the study of large datasets requiring less human interaction. The purpose of this study is to present a deep learning algorithm for the cross-sectional area (CSA) segmentation in transverse musculoskeletal ultrasound images, providing a quantitative grayscale analysis which is useful for studying muscles, and to validate the results in a large dataset. The dataset included 3917 images of biceps brachii, tibialis anterior and gastrocnemius medialis acquired on 1283 subjects (mean age 50 years ± 21, 729 male). The algorithm was based on multiple deep-learning architectures, and its performance was compared to a manual expert segmentation. We compared the mean grayscale value inside the automatic and manual CSA using Bland-Altman plots and a correlation analysis. Classification in healthy and abnormal muscles between automatic and manual segmentation were compared using the grayscale value z-scores. In the test set, a Precision of 0.88 ± 0.12 and a Recall of 0.92 ± 0.09 was achieved. The network segmentation performance was slightly less in abnormal muscles, without a loss of discrimination between healthy and abnormal muscle images. Bland-Altman plots showed no clear trend in the error distribution and the two readings have a 0.99 Pearson’s correlation coefficient (p&lt;0.001, test set). The ICC(A,1) calculated between the z-score readings was 0.99. The algorithm achieves robust CSA segmentation performance and gives mean grayscale level information comparable to a manual operator. This could provide a helpful tool for clinicians in neuromuscular disease diagnosis and follow-up. The entire dataset and code are made available for the research community."
DOI: 10.25403/upresearchdata.28668620.v1
title: The initial data and results of conceptual design, analysis and evaluation of a low-cost autonomous mobile robot for citrus farming in South Africa

description: This dataset expands on how the final design of a holistic conceptual design of an autonomous mobile robot (AMR) was determined through filtering through the initial technological options that were evaluated to develop an AMR.Phase 2.4: Design concepts, equipment cost versus accessibility graphs, and the cost of the various technologies that were evaluated as stated with a determined value at the time and an accessibility score. This was used to determine the top 3, 4, or 5 candidate solutions per component that could perform that specific task at hand.<br><br>Phase 2.4: Hardware design concept determinations and personnel cost as a function of design expands on the average cost of each determined AMR hardware design option with corresponding values that were the average of the 2.4 's top technological options.<br>Phase 3.3a: Candidate concept selection criteria determines the best option according to various criteria as specified herein to determine the final component candidate solution<br>Phase 3.3b: Final candidate concept selection cost structuring provides the final cost structuring of the final chosen component option derived in 3.3a along with how the total cost of development (xi) was determined.<br>
DOI: 10.6084/m9.figshare.19514347.v1
title: PAM dataset for Raindrop

description: The authors are: Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik<br>This PAM dataset is a subset of PAMAP2 dataset (public accessable). <br><br>- Paper: Graph-Guided Network For Irregularly Sampled Multivariate Time Series, (Accepted by ICLR 2022) <br><br>- Paper link: https://openreview.net/pdf?id=Kwm8I7dU-l5<br>- Github repo: https://github.com/mims-harvard/Raindrop<br>- Project website: https://zitniklab.hms.harvard.edu/projects/Raindrop/<br>
DOI: 10.17632/nptcw7kwdz
title: Supplementary Material: Epidemiology of Cutaneous Leishmaniasis in Karachi, Pakistan

description: This study investigates the epidemiology and clinical presentation of cutaneous leishmaniasis (CL) in Pakistan. The hypothesis posits that CL cases exhibit geographic clustering, seasonal variation, and an association with travel to endemic areas. A retrospective analysis of 525 cases (July 2019–February 2024) was conducted at the Leishmaniasis Clinic of the Institute of Skin Diseases Sindh Karachi. Data include demographic details (age, gender, residence, travel history) and clinical characteristics (lesion site, count, size, and duration).

Findings reveal a male predominance (65.1%), with younger age groups more affected. Exposed body areas accounted for 96.3% of lesions. The highest case burden originated from Baluchistan (49.7%), followed by Sindh (26.1%) and KPK (21.3%). Among Karachi patients, 37.3% had traveled to endemic regions, while 62.7% reported no travel history, suggesting possible local transmission (P &lt; 0.001). Seasonal distribution analysis using the Jonckheere–Terpstra test showed no significant monthly variation (P = 0.49).

Microscopic confirmation was conducted using Giemsa-stained smears, with histopathology and serology as secondary diagnostics. Treatment followed WHO-recommended protocols, with intralesional or intramuscular antimonials based on lesion count and size. Data were analyzed using SPSS v.22, employing chi-square and trend analysis for statistical comparisons.

This dataset provides valuable epidemiological insights, aiding in disease modeling, risk factor analysis, and targeted public health interventions. Ethical approval (D.O. No. DISDSK 5082) was obtained, and informed consent was secured per the Declaration of Helsinki.
DOI: 10.6084/m9.figshare.19093763.v2
title: TableE1- Cell and tissue donor details.docx

description: TableE1-HBEC donor details.docx
DOI: 10.6084/m9.figshare.19782406.v1
title: Code and data to reproduce "Epidemic time series similarity is related to geographic distance and age structure"

description: This repository contains code and data to reproduce the analyses from <br> <br> &gt; Dallas, TA, G Foster, R Richards, B Elderd. 2022. Epidemic time series similarity is related to geographic distance and age structure. <br> <br> The code to analyze and reproduce manuscript figures is all contained in the `analysis.Rmd` file, which contains the R code. This code loads the most current epidemic time series data, allowing the analyses to be re-run as more data are added. The main text considers the period between Jan 22, 2020 and May 9, 2022. A copy of these data are provided as `time_series_covid19_confirmed_US.csv` for reproducibility.<br> <br> We rely on a couple of other data sources, including data from the US Census Burueau (`county-pop-data.csv`) and estimates of R0 (`Ives&BozzutoTableS113Oct20.csv`) from <br> <br> &gt; Ives, Anthony R., and Claudio Bozzuto. "Estimating and explaining the spread of COVID-19 at the county level in the USA." Communications biology 4.1 (2021): 1-9.<br> <br>
DOI: 10.6084/m9.figshare.22593457.v1
title: Supplemental Tables and Figures for Swartz et al., (2023) choline study

description: Supplemental tables and figures for Swartz et al., (2023) assessing the effects of dietary rumen-protected choline supplementation to periparturient dairy cattle during an intramammary LPS challenge. <br> Reference to manuscript: Swartz, T. H., B. J. Bradford, L. K. Mamedova, and K. A. Estes. 2023. Effects of dietary rumen-protected choline supplementation to periparturient dairy cattle on inflammation, metabolism, and performance during an intramammary lipopolysaccharide challenge. J. Dairy Sci. (In press)
DOI: 10.6084/m9.figshare.27165927.v1
title: <b>Natural disturbances and connectivity shape the seasonal variability of aquatic macroinvertebrate communities across Europe</b>

description: We analyze the taxonomic and functional structure of macroinvertebrate communities sampled across 126 reaches with perennial and intermittent streamflow, surveyed in six drying river networks (DRN) across Europe, six times over one year. Drying frequency decreased community richness and functional diversity of communities, whereas spatio-temporal connectivity increased community richness in intermittent reaches. Communities experiencing a high drying frequency increased the proportion of taxa with K-strategies and drying resistance traits. Communities experiencing a long drying duration compensated by high spatio-temporal connectivity had more taxa with a r-strategy and high dispersal ability. Perennial communities varied from taxa-poor communities of r-strategists in spring and autumn and taxa-rich communities of K-strategists in summer and had a constant functional diversity throughout the year. When drying frequency increased, communities showed a similar pattern except in autumn when they shifted towards species-poor communities of K-strategists. Functional diversity then peaked in summer. Community trait structure and in particular optimal drying resistance traits changed across biogeographical scales. It opposed communities from mountainous DRN (with more r-strategies and high dispersal ability) to non-mountainous DRN (with more K-strategies).Drying frequency, drying duration, and spatio-temporal connectivity drive divergent community structures, suggesting the presence of an ecological threshold that explains the variability of disturbed ecosystems across broad spatial scales. These factors also shaped seasonal community variations, particularly after summer, with intermittent communities influenced by stochastic recolonization events in spring and autumn. Spatial-temporal connectivity proved crucial for maintaining diversity in communities subjected to intense drying. Lastly, the effectiveness of drying resistance traits was dependent on the biogeographical and environmental conditions of drying river networks.