cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29137772.v1
title: Digital Health Literacy for All: addressing digital health literacy challenges in the Latino community with diabetes in The Bronx

description: Diabetes, the seventh leading cause of death in New York City, affects nearly one million residents and disproportionately impacts Latino communities, where prevalence reaches 17% compared to 10% in non-Latino populations. The Bronx, home to NYC’s largest Latino population, exhibits one of the city’s highest diabetes rates (13.6%), further complicated by widespread Limited English Proficiency (LEP) and low digital health literacy. These barriers hinder access to critical health information and care—especially in the context of a healthcare system increasingly reliant on digital platforms. This study evaluates the accessibility and quality of Spanish-language diabetes information on the websites of Federally Qualified Health Centers (FQHCs) in the Bronx. Using the digital.gov guidelines for language accessibility, the Brief DISCERN questionnaire for credibility, and the Flesch-Kincaid readability tool adapted for Spanish, 13 FQHC websites were assessed. Results show that nearly half the sites scored above the 11th-grade reading level, while only 13% were within the recommended 6th–8th-grade range. Moreover, 27% of sites scored poorly on credibility, and several lacked Spanish-language options or were not operational. These findings reveal significant gaps in the digital health landscape, with inadequate Spanish-language diabetes content contributing to digital health inequities for LEP Latino populations in the Bronx. To support effective diabetes management and meet NYC’s goal of reducing diabetes prevalence by 5% by 2030, FQHCs must improve digital accessibility, ensure culturally and linguistically appropriate content, and engage LEP communities in usability testing and education initiatives. Study has limitations as it is a single evaluator.
DOI: 10.6084/m9.figshare.14216210.v1
title: A new Late Cretaceous leaf mine <i>Leucopteropsa spiralae</i> gen. et sp. nov. (Lepidoptera: Lyonetiidae) represents the first confirmed fossil evidence of the Cemiostominae

description: A new fossil leaf mine ichnogenus and species, <i>Leucopteropsa spiralae</i> gen. et sp. nov. (Lepidoptera: Lyonetiidae), from the Upper Cretaceous Kaiparowits Formation (Campanian age, <i>c.</i> 76.6 to 74.5 Ma) in Utah, USA, is the earliest record (75.6 ± 0.18 Ma) and only fossil evidence of a lyonetiid-like leaf-mining moth, as well as one of the oldest known fossils within the Yponomeutoidea-Gracillarioidea clade. The blotch-style mine consists of a central oviposition, or egg laying, site with a spiral trail packed with faecal pellets that concentrically spirals outwards. The trail increases in width from oviposition site to terminus, the trajectory of the trail does not cross itself, and there is no apparent pupation chamber present. The morphology of the fossil mine is reliably associated with Cemiostominae and is most similar to mines produced by extant members of the genus <i>Leucoptera</i>, such as the mountain ash bent-wing moth, <i>Leucoptera malifoliella</i>, and the laburnum leaf miner <i>L. laburnella</i>. The leaf-mining moth responsible for <i>L. spiralae</i> may be an early member of the genus <i>Leucoptera</i> or other Cemiostominae genera, and the new ichnogenus <i>Leucopteropsa</i> is erected because the morphology, and therefore the taxonomy, of the moth specifically responsible for <i>L. spiralae</i> mines was not preserved in association with the leaf mine. Despite the phylogenetic analysis of 15 lyonetiids undertaken in this study and the need for further phylogenetic work for the Lepidoptera, particularly the placement of the family Lyonetiidae and the subfamily Cemiostominae within the larger Yponomeutoidea-Gracillarioidea phylogeny, this fossil provides an important Late Cretaceous (∼76 Ma) calibration point for lepidopteran phylogeny and serves as an indicator for the antiquity of the most diverse lepidopteran group, Ditrysia. Because lepidopteran body fossils are extremely rare, owing to their small and lightly sclerotized bodies, this discovery also underscores the importance of ichnofossils in the lepidopteran fossil record. http://zoobank.org/urn:lsid:zoobank.org:pub:52B20B15-5C1A-4916-9096-A490DFED7D1C
DOI: 10.17632/tmyr97pgd8
title: Supplementary material (methodology)

description: Supplemental material for JAAD manuscript titled "Unseen steroids, seen risks: Skin-Cap still contains high-potency glucocorticosteroids 26 years after global scandal".
DOI: 10.6084/m9.figshare.10308443
title: Lower Limb Kinetic and Kinematic Data of 18 Above Knee Amputees

description: This data has full body motion capture collection of 18 above knee amputees walking on an instrumented treadmill at 5 different walking speeds. Subjects were divided into two group, one group walked at speeds of 0.4, 0.5, 0.6, 0.7, 0.8 m/s and the other group walked at speeds of 0.6, 0.8, 1.0, 1.2, 1.4 m/s. The kinetics and kinematics were calculated for the ankle, knee and hip joints of both the ipsilateral (prosthesis side) and contralateral (sound side) limb and stored in a matlab structure.
DOI: 10.6084/m9.figshare.21561870.v3
title: ASV relative abundance data of a randomized clinical trial for functional constipation

description: This is a dataset including the ASV taxon relative abundace from all of a randomized clinical trial in functional constipation individuals.
DOI: 10.6084/m9.figshare.13580633.v1
title: Supplementary material from "Bird genetic databases need improved curation and error reporting to NCBI"

description: The dataset provides the curated GenBank records from cytochrome-b for all Aves species. The associated manuscript is published in IBIS, doi:
DOI: 10.6084/m9.figshare.27040849.v1
title: Dataset for "Evaluating Observer Reliability and Diagnostic Accuracy of CT-LEFAT Criteria for Post-Treatment Head and Neck Lymphedema: A Prospective Blinded Comparative Analysis of Oncologist Human Inter-Rater Performance"

description: Anonymized NIfTI dataset used in the development and analysis of the manuscript titled "Evaluating Observer Reliability and Diagnostic Accuracy of CT-LEFAT Criteria for Post-Treatment Head and Neck Lymphedema: A Prospective Blinded Comparative Analysis of Oncologist Human Inter-Rater Performance"This dataset contains anonymized NIfTI (.nii) images presented to reviewers during the CT-LEFAT qualitative review process.clinical_val_anon: This CSV file contains extracted anonymized/deidentified data from the patients included in the CT-LEFAT review. Data includes: anonymized subject ID, repeat, reader, timepoint, anatomical location (right/left anterior cervical space, right/left posterior cervical space, midline anterior neck, and submental midline space (N/A indicates no response was received)), lymphedema (HNL) diagnosis (1=positive, 0=negative), if lymphedema was present in the image (1=yes, 0=no), time difference between imaging and diagnosis in months(N/A used for no HNL diagnosis), and lymphedema diagnosis using MD Anderson Cancer Center lymphedema grading scale (as per Smith et al., doi:10.1097/MOO.0b013e3283393799, wherein 1=positive, 0=negative, N/A= unable to be assessed).inter-reader reliability: This CSV file contains statistical data for the inter-reader reliability Fleiss' kappa method. Data includes: ROI, timepoint, kappa value, interpretation of kappa value, mean kappa value.intra-rater: This CSV file contains statistical data for the intra-rater Fleiss' kappa method. Data includes: ROI, rater, exact kappa, average kappa by ROI, average kappa by reader, overall average kappa.
DOI: 10.17632/hd79hfj2vk
title: Web System Adoption:SME Malaysia

description: This dataset contains variables such as compatibility, relative advantage, security, and cost as independent variables, with top management support serving as a mediating variable. The dependent variable in this study is the web system adoptin by SMEs.
DOI: 10.6084/m9.figshare.11955498.v2
title: Where is my child? Spatiotemporal distribution of child trafficking in China and predicting the locations of missing children

description: The file (baby_find_home_data.txt) is data obtained from a Chinese public welfare website (https://baobeihuijia.com/) looking for missing children.<br>The file (code_v2.zip) is a code file implemented by an under review paper ("Where is my child? Spatiotemporal distribution of child trafficking in China and predicting the locations of missing children").<br>This project uses two C ++ open source libraries (Alglib and CJsonObject). This project complies with the GPL agreement.<br>Alglib (https://www.alglib.net/) is an open source numerical analysis and data processing library for personal and academic use, developed for the ALGLIB Project Company. Detailed implementation of random forest algorithm used in this project.<br>CJsonObject (https://github.com/Bwar/CJsonObject) is a C ++ version of the JSON open source library developed by Bwar based on cJSON. Used in this project for data reading and formatting output.
DOI: 10.6084/m9.figshare.5480908.v1
title: R code from Re-evaluating the link between brain size and behavioural ecology in primates

description: R code used to execute PGLS and model comparisons
DOI: 10.17632/7fvgj4jsp7
title: BUS-UCLM: Breast ultrasound lesion segmentation dataset

description: The proposed dataset is comprised of breast ultrasound images from 38 patients. It consists of 683 images, of which 174 are benign, 90 are malignant, and 419 are normal. Scans were obtained with a Siemens ACUSON S2000TM Ultrasound System between 2022 and 2023. The ground truth is presented in separate files as RGB segmentation masks where green denotes benign lesions, red denotes malignant lesions, and black denotes the background or normal breast tissue. This dataset constitutes a valuable resource for research in breast cancer diagnosis, lesion detection, medical imaging, and health care applications. It facilitates researchers and practitioners to develop and examine machine learning models for the identification of benign and malignant lesions across full real cases. The segmentation annotations made by expert radiologists enable precise model training and evaluation, making this dataset a benefit in the field of computer vision and public health.
DOI: 10.17632/chx9mdyydb
title: Social media data #bitcoin #Ethereum # facebook

description: Datasets include tweets of #bitcoin #Ethereum # facebook ranging between 2015 to 2018 and analyzed over the common time frame among these three hashtags.
DOI: 10.6084/m9.figshare.20506554.v1
title: Sensitivity and spectral control of network lasers - Data

description: Source datasets for the figures presented in the paper entitled: "Sensitivity and spectral control of network lasers".
DOI: 10.6084/m9.figshare.28320866.v1
title: Supplementary Material for: Analysis Method of Real-World Digital Biomarkers for Clinical Impact in Cancer Patients

description: Introduction: Wearable technologies can enhance measurements completed from home by participants in decentralized clinical trials. These measurements have shown promise in monitoring patient wellness outside the clinical setting. However, there are challenges in handling data and its interpretation when using consumer wearables, requiring input from statisticians and data scientists. This paper describes three methods to estimate daily steps to address gaps in data from the Apple Watch in cancer patients and uses one of these methods in an analysis of the association between daily step count estimates and clinical events for these patients.
Methods: A cohort of 50 cancer patients used the DigiBioMarC app integrated with an Apple Watch for 28 days. We identified different gap types in watch data based on their length and context to estimate daily steps. Cox proportional hazards regression models were used to determine the association between step count and time to death or time to first clinical event. Decision tree modeling and participant clustering were also employed to identify digital biomarkers of physical activity that were predictive of clinical event occurrence and hazard ratio to clinical events, respectively. 
Results: Among the three methods explored to address missing steps, the method that identified different step data gap types according to their duration and context provided the most reasonable estimate of daily steps. Ten hours of waking time was used to differentiate between sufficient and insufficient measurement days. Daily step count on sufficient days was the most promising predictor of time to first clinical event (p = 0.068). This finding was consistent in participant cluster and decision tree analyses where the participant cluster that emerged naturally from the different levels of daily steps was the group with the highest steps on sufficient days and lowest hazard probability of mortality and clinical events. Additionally, daily steps on sufficient days can also be used as a predictor of whether a participant will have clinical events with an accuracy of 83.3%.
Conclusion: We have developed an effective way to estimate daily steps from consumer wearable data containing unknown data gaps. Daily step counts on days with sufficient sampling is a strong predictor of the timing and occurrence of clinical events, with individuals exhibiting higher daily step counts having reduced hazard of death or clinical events.
DOI: 10.6084/m9.figshare.14417225.v4
title: Ti38V15Nb23Hf24

description: The training and validation dataset (in numpy format) for variational autoencoders based order parameter for Ti38V15Nb23Hf24 alloy. Each data sample is a system configuration with the sampled temperature provided in the corresponding label file. The configurations are generated with interaction parameters calculated from VASP v5.4.4 (input files included). <br>
DOI: 10.6084/m9.figshare.28430210.v2
title: The dataset for "Pseudogap and Fermi arc induced by Fermi surface nesting in a centrosymmetric skyrmion magnet"

description: This is the dataset for "Pseudogap and Fermi arc induced by Fermi surface nesting in a centrosymmetric skyrmion magnet".
DOI: 10.6084/m9.figshare.29092430.v1
title: <b>Shatavari </b><b><i>(Asparagus racemosu</i></b><b>s </b>Willd<b>) Root Extract Improves Post-Partum Lactation in a Prospective, Randomized, Double-Blind, Placebo-Controlled Study</b>

description: Background: Inadequate milk production during the immediate postpartum period is a common problem. Shatavari (Asparagus racemosus), a revered herb in Ayurveda, is traditionally used as a galactagogue. Objective: To assess the effectiveness of Shatavari root extract on lactation in post-partum women. Methods: This prospective, randomized, double-blind, placebo-controlled study, enrolled 120 (Intent-to-treat) post-partum women (≥ 37 weeks’ gestation, aged 20–40 years) with uncomplicated vaginal or cesarean deliveries who intended to breastfeed. Participants received either Shatavari root extract (SHT; 300 mg) or placebo (PL) capsules twice daily for 72 hours postpartum. The primary outcomes were time to evident breast fullness after the last feeding and the total milk volume expressed at 72 hours. The secondary outcomes were maternal and investigator-rated satisfaction (e.g., noticeable breast fullness, maternal/infant wellbeing), assessed by a five-point Likert scale. The safety parameter was evaluated by reporting adverse events. Independent t-tests were used for primary outcomes, chi-square tests for subjective ratings, and analysis of covariance to assess the impact of covariates (age, body mass index, delivery type, and milk expression method). Results: Seven participants (3 SHT, 4 PL) did not complete the study, yielding a per-protocol dataset of 113 women (57 SHT and 56 PL). The baseline characteristics were comparable across groups. The time to evident breast fullness after the last feeding was significantly shorter in the SHT group (p = 0.002), and milk volume at 72 hours was significantly higher (p &lt; 0.001). The maternal satisfaction with lactation was higher in the SHT group, with 52.63% reporting satisfaction versus 25.00% in the PL group (p = 0.008). The satisfaction regarding infant well-being and lactation was also higher among SHT participants. Investigators rated maternal and infant well-being more in the case of SHT group vs the placebo group (80.70% vs. 64.29%). No significant adverse events were reported, and treatment compliance was high. Conclusion: Shatavari root extract significantly enhanced lactation outcomes in postpartum women by increasing milk volume and maternal satisfaction, indicating its potential as a safe, effective galactagogue.
DOI: 10.6084/m9.figshare.28654973.v1
title: ArticleSet2

description: This file is formatted as an Excel table (.xlsx format). Data table of pre-classified articles from medical sciences based on research methodology. In the data table, four columns are included:Title of Articles: The title of the collected and classified articleChatGPT: The predicted research methodology using ChatGPT platformOriginal: Research methodology classification by expertsNotebooklm: The predicted research methodology using Notebooklm platformThe dataset was used to train and validate classifiers to predict the applied research methodology of the papers such as (1) quantitative or (2) qualitative.In the case of the AI platforms, "Mixed / Unclear" category is also used where the platform could not predict the applied research methodology.
DOI: 10.6084/m9.figshare.12312209.v1
title: Single Institution, Bilateral, and Multilateral Collabortation Patterns (Institution Level) Area

description: Institutional collaboration modes for COVID-19 research. This graph is not cumulative. Papers are grouped by the week commencing on the dates given; the final week is incomplete. Publications counting toward ``single institution'' have only a single institutional affiliation, bilateral publications are affiliated with two institutions, and multilateral publications are those that are affiliated with more than two institutions. There is no sensitivity in this plot to the country in which institutions are situated.
DOI: 10.6084/m9.figshare.27064558.v1
title: <b>Source data and code for research</b>

description: source data and code for research "Weakened effectiveness of short-term emission controls for major events in Chinese cities"
DOI: 10.15468/dl.q6dr8q
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Holtbyrnia innesi (Fowler, 1934)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((44.0031 -15.9691,43.978 -16.1504,44.9648 -16.1504,44.9648 -15.5455,44.7261 -15.708,44.2309 -15.7446,44.0031 -15.9691))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013421-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.12916037.v1
title: Datasets for Fukuma et al. Communications Biology

description: These files includes datasets used in Fukuma et al. "Voluntary control of semantic neural representations by imagery with conflicting visual stimulation." <i>Communications Biology</i>.
DOI: 10.6084/m9.figshare.28169462.v3
title: Examining the clinical utility of the Assessment of Client’s Enablement (ACE): Qualitative study (Supplementary files)

description: Supplementary File 1 (Interview Guide) Supplementary File 2 (Four-step coding for each participant analyzed through SCAT) is available in JapaneseSupplementary File 3 (Storyline)
DOI: 10.6084/m9.figshare.28374311.v1
title: Original data-plos one2025

description: Accurate identification of vegetation in mining areas is crucial for conducting pre-mining ecological assessments and post-mining ecological monitoring. However, the vegetation in the mining area is always highly heterogeneous including both field crops and naturally scattered growing vegetation, which brings great challenges for fine vegetation mapping. Feature combinations are an important factor to influence the vegetation mapping. Thus, to effectively identify the vegetation, this study utilized an unmanned aerial vehicle (UAV) RGB image to extract vegetation indexes and textures, and then selected features based on standard deviation and difference coefficient. By integrating selected optimal features with RGB images, different combinations were constructed and classified using Support Vector Machine (SVM).
DOI: 10.17632/tvhygj8rgg
title: Household energy consumption enriched with weather data in northeast of Mexico

description: The dataset encompasses information obtained from energy consumption in a residence located in the northeast region of Mexico over a span of 14 months. Each data point was recorded at one-minute intervals and features energy-related metrics enriched with weather data provided by OpenWeather, under a free license. The variable descriptions are outlined as follows:
date
active_power
current
voltage
reactive_power
apparent_power
power_factor
main (categorical weather conditions)
description (detailed categorical weather conditions)
temp
feels_like
temp_min
temp_max
pressure
humidity
speed (wind speed)
deg (wind degree)
temp_t+1 (forecast of temperature for the next day)
feels_like_t+1 (forecast of feels like for the next day)
This dataset provides a comprehensive insight into the energy dynamics of the household, coupled with meteorological influences, offering a nuanced perspective on energy consumption patterns over time.
DOI: 10.6084/m9.figshare.22032170.v2
title: Attune 10x multiome dataset

description: 10x multiome dataset
DOI: 10.17632/jw3zwf8rdp
title: Sepehr_RumTel01

description: The expansion of social networks has accelerated the transmission of information and news at every communities. Over the past few years, the number of users, audiences and social networking publishers, are increased dramatically too. Among the massive amounts of information and news reported on these networks, we are faced with issues that have not been verified which is called “rumors”. Identifying rumors on social networks is carried out in the form of rumor detection approaches; the massive amount of these news and information force to use the machine learning techniques. The most important problem with auto-detection approaches is the lack of a database of rumors. For that matter, in this article, a collection of rumors published on the social network “telegrams” have been collected. These data are gathered from five Persian-language channels that have specially reviewed this issue. The collected data set contains 3283 messages with 2829 attachments, having a volume of over 1.6 gigabytes. This dataset can also be used for different purposes of natural language processing.
DOI: 10.6084/m9.figshare.12014937.v1
title: FileS1-Supp_Data

description: This file contain raw, figures data, and description for the paper
DOI: 10.6084/m9.figshare.22793993.v1
title: River Selection

description: Initial node and node characteristics of reach
DOI: 10.6084/m9.figshare.25406263.v1
title: Dateset and Codes for Modelling Infiltration based on Source-Responsive Method for Improving Simulation of Rapid Subsurface Stormflow

description: This dataset and codes used to generate figures and tables for the paper entitled "<i>Modelling Infiltration based on Source-Responsive Method for Improving Simulation of Rapid Subsurface Stormflow</i>".
DOI: 10.6084/m9.figshare.29092604.v1
title: EC 5612 Proposal

description: This slideshow gives a step by step guide of how to access the data described in the EC5612 research proposal by Sebastian Dooley
DOI: 10.6084/m9.figshare.13498254.v1
title: Atomic structures of intermediate states in CO2 hydrogenation to CH3OH on S-vacancies of MoS2

description: The initial and optimized atomic positions in the reaction intermediates of CO2 hydrogenation to methanol on S vacancies of MoS2.