cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3115120.v1
title: Data from CCAB computerized design fluency test

description: Tests of design fluency (DF) assess a participant’s ability to generate geometric patterns and are thought to measure executive functions involving the non-dominant frontal lobe. Here, we describe the properties of a rapidly administered computerized design-fluency (C-DF) test that measures response times and is automatically scored. In Experiment 1, we found that the number of unique patterns produced over 90 s by 180 control participants (ages 18 to 82 years) correlated with age, education, and daily computer-use. Each line in the continuous 4-line patterns required approximately 1.0 s to draw. The rate of pattern production and the incidence of repeated patterns both increased over the 90 s test. Unique pattern z-scores (corrected for age and computer-use) correlated with the results of other neuropsychological tests performed on the same day. Experiment 2 analyzed C-DF test-retest reliability in 55 participants in three test sessions at weekly intervals and found high z-score intraclass correlation coefficients (ICC = 0.79). Z-scores in the first session did not differ significantly from those of Experiment 1, but performance improved significantly over repeated tests. Experiment 3 investigated the performance of Experiment 2 participants when instructed to simulate malingering. Z-scores were significantly reduced and pattern repetitions increased, but there was considerable overlap with the performance of the control population. Experiment 4 examined performance in veteran patients tested more than one year after traumatic brain injury (TBI). Patients with mild TBI performed within the normal range, but patients with severe TBI showed reduced z-scores. The C-DF test reliably measures visuospatial pattern generation ability and reveals performance deficits in patients with severe TBI.
DOI: 10.6084/m9.figshare.3523325.v1
title: Appendix D. A table showing repeated-measures ANOVA and single season ANOVAs for the effects of the intergroup competition on productivity of native perennials and exotic annuals.

description: A table showing repeated-measures ANOVA and single season ANOVAs for the effects of the intergroup competition on productivity of native perennials and exotic annuals.
DOI: 10.6084/m9.figshare.3555738.v1
title: Appendix E. Ellagitannin characterization.

description: Ellagitannin characterization.
DOI: 10.6084/m9.figshare.23608209.v1
title: BDNF PTSD Data

description: Deidentified data of the PTSD BDNF genetic treatment study.
DOI: 10.6084/m9.figshare.1335752.v1
title: data of toric IOL

description: Comparison of the visual performance of toric intraocular lenses with non-toric intraocular lenses of the same material.
DOI: 10.6084/m9.figshare.16691350.v1
title: Scenario 2. PD

description: HV industrial environment
DOI: 10.6084/m9.figshare.20032103.v3
title: Chicken_FAANG

description: 我们通过整合来自 23 个鸡组织的 7 个表观遗传标记、染色质构象和转录组的 377 个全基因组测序数据集，系统地鉴定和表征了鸡基因组中的调节元件。并使用这些数据来解释鸡的复杂性状和驯化。
DOI: 10.6084/m9.figshare.27093856.v1
title: Three seabuckthorn genome data

description: Here contains the genome data of male <i>Hippophae gyantsensis</i>, female <i>Hippophae salicifolia</i> and male <i>Hippophae salicifolia</i>. Male <i>Hippophae salicifolia</i> included two sets of haplotype data, hap1 and hap2.
DOI: 10.6084/m9.figshare.12806642.v1
title: Supplementary Material for: Allostatic Load and Its Impact on Health: A Systematic Review

description: <b><i>Introduction:</i></b> Allostatic load refers to the cumulative burden of chronic stress and life events. It involves the interaction of different physiological systems at varying degrees of activity. When environmental challenges exceed the individual ability to cope, then allostatic overload ensues. Allostatic load is identified by the use of biomarkers and clinical criteria. <b><i>Objective:</i></b> To summarize the current knowledge on allostatic load and overload and its clinical implications based on a systematic review of the literature. <b><i>Methods:</i></b> PubMed, PsycINFO, Web of Science, and the Cochrane Library were searched from inception to December 2019. A manual search of the literature was also performed, and reference lists of the retrieved articles were examined.<b><i></i></b>We considered only studies in which allostatic load or overload were adequately described and assessed in either clinical or non-clinical adult populations. <b><i>Results:</i></b> A total of 267 original investigations were included. They encompassed general population studies, as well as clinical studies on consequences of allostatic load/overload on both physical and mental health across a variety of settings. <b><i>Conclusions:</i></b> The findings indicate that allostatic load and overload are associated with poorer health outcomes. Assessment of allostatic load provides support to the understanding of psychosocial determinants of health and lifestyle medicine. An integrated approach that includes both biological markers and clinimetric criteria is recommended.
DOI: 10.17632/2rj8ss5mpn
title: Impact of Climate Change on Green Roof Energy Performance in Oceanic Climates: A Simulation Dataset (2022-2050)

description: This dataset comprises two distinct components. The first section encompasses climate data adapted for climate change scenarios derived from the HadCM3 GCM A2 ensemble dataset. It includes essential variables such as Dry Bulb Temperature (DBT), Dew Point Temperature (DPT), Relative Humidity (RH), Atmospheric Pressure, Extraterrestrial Horizontal Radiation (ExtHoRad), Extraterrestrial Direct Radiation (ExtDiRad), Horizontal Infrared Radiation (HoInfRad), Global Horizontal Radiation (GlHoRad), Direct Normal Radiation (DirNoRad), Diffuse Horizontal Radiation (DifHoRad), Global Horizontal Illuminance (GlHoIll), Direct Normal Illuminance (DirNoIll), Diffuse Horizontal Illuminance (DifHoIll), Zenithal Luminance (ZenLum), Wind Direction (WindDir), Wind Speed (WindSp), Total Sky Cover (TotSkyCo), Opaque Sky Cover (OpSkyCo), Visibility (Visibili), Ceiling Height (Ceiling), Precipitable Water Overhead (PWO), Precipitable Water Column (PWC), Precipitation (Prec), Aerosol Optical Depth (AerOptD), Snow Depth (SnowD), and the Date of the Last Snowfall (LastSnow). These datasets are presented in Excel, including raw data, line charts, and boxplots. The second part of the dataset presents the results of simulations assessing the thermal performance of green roofs under two scenarios: the present scenario in 2022 and a future climate change projection for 2050. These simulations were conducted using the Design Builder software. This dataset is also provided in Excel format, featuring raw data, line charts, and boxplots. It compares conventional bare roofs and green roofs in distinct climate areas, specifically Auckland, Christchurch, and Wellington. This dataset's primary focus is on the year 2050, providing valuable insights into how climate change is expected to impact the energy performance of green roofs within oceanic climates.
DOI: 10.6084/m9.figshare.12928145.v2
title: Complementary search database for Invacost

description: This database is complementary to the original version of Invacost<sup>[1]</sup>. An updated version of this database is now incorporated within this core database.<b>CONTENT</b>: This page contains (a) the original version of the 'Complementary Search’ database' ('InvaCost_SC database') and (b) full definition and details about the descriptive columns used in this database (‘Descriptors’ file). <b>METHODOLOGY</b>: The construction of this complementary database results from a collective work made by members of the <i>InvaCost</i> consortium<sup>[2]</sup> following the InvaCost workshop<sup>[3,4].</sup> We have screened a total of 304 new references (collected but not processed in the building process of original InvaCost) from which we extracted 2,374 cost entries. Most of the screened references belong to plants; however, they also contain costs for other invasive organisms (animals and pathogens). All cost entries have been standardized for both costs and text, however not all have been yet double-checked (i.e. process to ensure quality of the integrated information, usually made by one or two independent researchers) as the other parts of the Invacost database (i.e. original InvaCost_1.0 and Non-English).<b>UPDATES, RECOMMENDATIONS & CAUTION: </b>Please consider information already described elsewhere<sup>[5]</sup> for the original InvaCost database. <b> </b><b>RELATED LINKS AND PUBLICATIONS</b>: [1]Database: https://figshare.com/articles/dataset/InvaCost_References_and_description_of_economic_cost_estimates_associated_with_biological_invasions_worldwide_/12668570 [2] Researchgate page: https://www.researchgate.net/project/InvaCost-assessing-the-economic-costs-of-biological-invasions[3] InvaCost workshop: https://www.biodiversitydynamics.fr/invacost-workshop/ [4] Diagne C, Catford JA, Essl F, Nuñez MA, Courchamp F (2020) What are the economic costs of biological invasions? A complex topic requiring international and interdisciplinary expertise. NeoBiota 63: 25–37.https://doi.org/10.3897/neobiota.63.55260[5] Diagne, C., Leroy, B., Gozlan, R.E. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7, 277 (2020). https://doi.org/10.1038/s41597-020-00586-z
DOI: 10.6084/m9.figshare.21291825.v4
title: BloodNet-Benchmark

description: <em><strong>BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference</strong></em> <br> The time since deposition (TSD) of a bloodstain, i.e., the time of a bloodstain formation is an essential piece of biological evidence in crime scene investigation. The practical usage of some existing microscopic methods (e.g., spectroscopy or RNA analysis technology) is limited, as their performance strongly relies on high-end instrumentation and/or rigorous laboratory conditions. This paper presents a practically applicable deep learning-based method (i.e., <strong>BloodNet</strong>) for efficient, accurate, and costless TSD inference from a macroscopic view, i.e., by using easily accessible bloodstain photos. To this end, we established a benchmark database containing around 50,000 photos of bloodstains with varying TSDs. Capitalizing on such a large-scale database, BloodNet adopted attention mechanisms to learn from relatively high-resolution input images the localized fine-grained feature representations that were highly discriminative between different TSD periods. Also, the visual analysis of the learned deep networks based on the Smooth Grad-CAM tool demonstrated that our BloodNet can stably capture the unique local patterns of bloodstains with specific TSDs, suggesting the efficacy of the utilized attention mechanism in learning fine-grained representations for TSD inference. As a paired study for BloodNet, we further conducted a microscopic analysis using Raman spectroscopic data and a machine learning method based on Bayesian optimization. Although the experimental results show that such a new microscopic-level approach outperformed the state-of-the-art by a large margin, its inference accuracy is significantly lower than BloodNet, which further justifies the efficacy of deep learning techniques in the challenging task of bloodstain TSD inference. Our code is publically accessible via <em>https://github.com/shenxiaochenn/BloodNet</em>. ----data.zip All the bloodstain images have been placed in the appropriate folders according to the corresponding categories.The name of the folder is the tag information corresponding to the image. ----bloodnet50_new.pth The weights corresponding to the classification model. ----bloodnet50_reg.pth The weights corresponding to the regression model. ----bloodnet(small).pth The weights corresponding to the small classification model. ----seresnet50-60a8950a85b2b.pkl The weights corresponding to the Imagenet pretrain model. ----bloodstain_information.csv The detailed information of each bloodstain. <br>
DOI: 10.6084/m9.figshare.21476958.v1
title: Dataset - survival analysis

description: Dataset used to produce analyses of pre-weaning calf survival in migratory caribou, as part of the results presented in the core of the paper:Vuillaume, B., J. H. Richard, S. Hamel, J. Taillon, M. Festa-Bianchet and S. D. Côté. 2023. Birth date determines early calf survival in migratory caribou.2 datasets: including stillborns (..._withSB.txt) and excluding stillborns (..._noSB.txt)<br>
DOI: 10.6084/m9.figshare.24718590.v1
title: combined_data.xlsx

description: this is a test.
DOI: 10.6084/m9.figshare.28927592.v1
title: Data Extraction Table for "<i>Acne-Associated Post-Inflammatory Hyperpigmentation: A Systematic Review of Treatments and Guidelines for Clinicians</i>"

description: This data extraction table contains detailed information extracted from studies included in the systematic review titled "<i>Acne-Associated Post-Inflammatory Hyperpigmentation: A Systematic Review of Treatments and Guidelines for Clinicians.</i>" The table includes key study characteristics, patient characteristics, treatment information, and outcomes collected from relevant studies assessing treatments for Acne-Associated Post-Inflammatory Hyperpigmentation (AAPIH). <br><br>A systematic review was conducted in line with PRISMA guidelines and registered with PROSPERO (CRD420251041782). MEDLINE and Embase databases were searched for studies published between January 1999 and December 2024. Studies assessing treatments for AAPIH were selected based on predefined eligibility criteria using the PICOS framework. Levels of evidence were assessed, and a risk of bias analysis was conducted. Due to heterogeneity in treatment outcomes, a descriptive analysis was performed.<br><br>This table serves as a supplementary resource for the systematic review, providing comprehensive insight into the methodologies, patient demographics, treatment protocols, and clinical outcomes observed across the included studies.
DOI: 10.17632/9zxj8b56y9
title: LBsegmentation

description: Lattice Boltzmann segmentation of Database X-ray coronary angiograms (DCA1)
DOI: 10.17632/vsxtpmdhps
title: MSborderpolje

description: Data for Historical soil erosion events in border polje revealed by geochemical fingerprint analysis of soil profiles
DOI: 10.6084/m9.figshare.13370684.v1
title: test4.zip

description: Fold 4 test set
DOI: 10.17632/v25np8ntfk
title: Supplementary table : Incidence of childhood-onset vitiligo and increased risk of atopic dermatitis, autoimmune diseases, and psoriasis: A nationwide population-based study.

description: Supplementary table 1. Demographic characteristics of the study population.
Supplementary table 2. Incidence of childhood-onset vitiligo in the Korean population aged 0-12 years.
Supplementary table 3. Stratified analysis by sex of the associations between childhood-onset vitiligo and allergic/autoimmune diseases
DOI: 10.6084/m9.figshare.12622829.v1
title: Source data.xlsx

description: <b>Soil microbiome assembly at a continental and global scale</b>
DOI: 10.6084/m9.figshare.21711608.v2
title: Dataset for diagnosing UAVs using NLP

description: The dataset contains the modules described in the paper.Flight logs in ArduCrash should be loaded by python pickle library.
DOI: 10.6084/m9.figshare.96583.v2
title: Languages of Open Access Items (2012)

description: The dataset shows the languages of open access items (2004-2012) indexed in the Bielefeld Academic Search Engine (BASE). The datasets are presented in csv and xslx. Based on the data a visualization was created and published in LIBREAS. Library Ideas (an Open Access e-only journal). For the citable URN of the visualization see the link.
DOI: 10.17632/5rhw2srzjj
title: Sentiment Analysis of Multilingual Dataset of Bahraini Dialects, Arabic, and English

description: This dataset was generated using two cascading stages of translation—a machine translation followed by a manual one. Machine translation was applied using Google translate to translate English Amazon product reviews to Standard Arabic. In contrast, the manual approach was applied to translate the resulting Arabic reviews to Bahraini ones by qualified native speakers utilizing constructed customized forms. The resulting parallel dataset of English, Standard Arabic, and Bahraini dialects is called English_Modern Standard Arabic_Bahraini Dialects product reviews for sentiment analysis “E_MSA_BDs-PR-SA”. The dataset is balanced, composed of 2,500 positive and 2,500 negative reviews.
DOI: 10.25415/ujhb.22047239.v2
title: Private equity impact investing dataset

description: Dataset showing fundraising in various selected private equity impact funds and the determinants thereof
DOI: 10.6084/m9.figshare.14695326.v2
title: Sleep Quality among Undergraduate Students of a Medical College in Nepal during COVID-19 Pandemic: An Online Survey

description: We used the standard and validated Pittsburgh Sleep Quality Index (PSQI), which was developed by researchers at the University of Pittsburgh in 1988 AD. The questionnaire included baseline variables like age, sex, academic year, and questions addressing participants’ sleep habits and quality i.e. PSQI. The PSQI assesses the sleep quality during the previous month and contains 19 self-rated questions that yield seven components: subjective sleep quality sleep, latency, sleep duration, sleep efficiency and sleep disturbance, and daytime dysfunction. Each component is to be assigned a scored that ranges from zero to three, yielding a PSQI score in a range that goes from 0 to 21. A total score of 0 to 4 is considered as normal sleep quality; whereas, scores greater than 4 are categorized as poor sleep quality.Data collected from students through the Google forms were extracted to Google sheets, cleaned in Excel, and then imported and analyzed using STATA 15. Simple descriptive analysis was performed to see the response for every PSQI variable. Then calculation performed following PSQI form administration instructions.
DOI: 10.6084/m9.figshare.24100695.v1
title: SHARD - SHelf mAnagement Row Dataset

description: SHARD (SHelf mAnagement Row Dataset) contains ~22K images of shelves collected in about 2000 different supermarkets in Italy. Moreover, an annotation file provides the key information regarding the shelf rows’ position,<br>with the following structure:image name, p1, p2, ..., pn (2)<br>where pi is the Y-coordinate shelf row expressed as the percentage of the picture’s height in pixels, starting from the top, and n, where n &gt;= 1, is the number of shelf rows present in the image.Please refer to the original publication for any detail. Also when using the data, please cite the original paper:https://doi.org/10.1016/j.eswa.2024.124635
DOI: 10.6084/m9.figshare.27201789.v2
title: Supplement S1_Post-fire forest data_Kalkanlı.xlsx

description: Post-fire data of pine forest in Kalkanlı region, 2024, Cyprus
DOI: 10.6084/m9.figshare.29149169.v1
title: Mixed species decomposition data

description: We compiled effect values from 91 field studies on litter mixture decomposition. Using a meta-analysis, we investigated the temporal dynamics and identified the biotic and abiotic factors influencing mixed-species litter decomposition rates across different decomposition stages
DOI: 10.6084/m9.figshare.19099733.v2
title: Raw data of the manuscript "Characterization of misleading fluoride information on Instagram"

description: Raw data of the manuscript "Characterization of misleading fluoride information on Instagram"
DOI: 10.6084/m9.figshare.23370104.v3
title: <strong>The Attitudes and Practices of United Arab Emirates Consumers Towards Food Waste</strong> <strong>- A Nationwide Cross-Sectional Study</strong>

description: The dataset consists of responses from a total of 1052 participants who took part in a study related to food waste. The data include variables from various domains, namely knowledge, awareness, attitude, and practice. The knowledge subdomain comprises two items, while the awareness subdomain consists of a single item. To assess participants' knowledge, the mean score of the two knowledge items is calculated. Similarly, for participants' knowledge and awareness, the mean score of the three items in the knowledge and awareness subdomains is computed. Higher scores indicate better knowledge and awareness regarding food waste, which corresponds to desirable behavior. The attitude subdomain includes validated questions related to personal, emotional, behavioral, and financial aspects of attitudes toward food waste. Each subdomain contains different numbers of items, specifically five, three, two, and four items, respectively. To evaluate participants' attitudes, the mean score of the items within each subdomain is calculated. Additionally, the overall attitude is assessed by computing the mean score of all fourteen items across the four attitude subdomains. Higher scores indicate more favorable attitudes toward food waste, reflecting desirable behavior. The practice domain consists of fifteen items, and the mean score of these items is used to assess participants' practice of food waste. In this case, higher scores reflect the worse practice of food waste, indicating undesirable behavior. Overall, this dataset provides information on participants' knowledge, awareness, attitude, and practice regarding food waste.
DOI: 10.6084/m9.figshare.1287000.v2
title: manual_venoms.csv

description: Manually curated records of therapeutic uses of venoms used for the initial population of VenomKB
DOI: 10.17632/bxfdtv7yb8
title: Smart Agriculture: Automated Phytomonitoring Data for Machine Learning

description: The dataset is an array of data that includes:
1. Vegetation indices are indicators calculated as a result of operations with different spectral ranges (channels) and related to plant parameters in a given image pixel.
- Infrared indices: NDVI and IPVI;
- Normalized difference: RVI;
- Global Environmental Monitoring Index: GEMI.
2. Environmental indicators 
- Temperature;
- Pressure;
- Humidity;
- Illumination.
The values of the indicators were obtained by means of automated phytomonitoring on a "smart" vertical farm of the Institute of Scientific Communications (Volgograd, Russia), which is an experimental scientific platform. The dataset time frame covers the period from October 4, 2022 (12:00) to October 10, 2022 (12:35). Data collection was carried out using the patented sensor sensors “Photon”. The indicators in the dataset are given in Russian and English.

Описание на русском языке:
Датасет представляет собой массив данных, в который включены:
1. Вегетационные индексы – это показатели, рассчитываемые в результате операций с разными спектральными диапазонами (каналами) и имеющие отношение к параметрам растения в данном пикселе снимка.
- Инфракрасные индексы: NDVI и IPVI;
- Нормализованный разностный: RVI;
- Индекс глобального мониторинга окружающей среды: GEMI.
2. Показатели окружающей среды:
- Температура;
- Давление;
- Влажность;
- Освещенность.
Значения показателей получены посредством проведения автоматизированного фитомониторинга на «умной» вертикальной ферме Института научных коммуникаций (Волгоград, Россия), являющейся экспериментальной научной площадкой. Временные рамки датасета охватывают период с 4 октября 2022 г. (12:00) до 10 октября 2022 г. (12:35). Сбор данных осуществлен с помощью запатентованных сенсорных датчиков «Фотон». Показатели в датасете приведены на русском и английском языке.