cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24913146.v3
title: <b><i>ACTN3</i></b><b>, </b><b><i>ACE</i></b><b>, </b><b><i>BDKRB2</i></b><b>, and </b><b><i>AGT and Brazilian swimmers</i></b>

description: The influence of genetic polymorphisms on athletic performance has been widely explored. This study investigated the interactions between the polymorphisms <i>ACTN3 </i>(R577X), <i>ACE </i>(I/D), <i>BDKRB2 </i>(-9/+9), and <i>AGT </i>(M/T) and their association with endurance and strength phenotypes in Brazilian swimmers. 123 athletes (aged 20–30 years) and 718 controls participated in the study. The athletes were divided into elite and sub-elite (N=19 and 104, respectively) and strength and endurance experts (N=98 and 25, respectively). Hardy-Weinberg equilibrium was observed in all groups. Considering the <i>ACE</i> polymorphism, it was observed a higher frequency of the DD genotype than expected in the strength experts of the elite group, whereas the strength experts sub-elite athletes had a higher frequency of the ID genotype (χ<sup>2</sup>=8.17; p=0.01). Subjects with XX genotypes of <i>ACTN3</i> are more likely to belong to the athlete group when compared to the control group (OR=1.79, p=0.04). The DD homozygotes of the <i>ACE</i> are more likely to belong to the elite group with strength phenotypes than the group of sub-elite (OR=7.96, p=0.01) and elite strength experts compared to elite endurance (OR=18.0, p=0.03). However, no significant differences were observed in the allelic distribution of the polymorphisms evaluated when comparing Elite, sub-elite athletes and controls. In conclusion, <i>ACE</i> and <i>ACTN3</i> allele frequencies should be considered with regard to performance influencing factors in Brazilian swimmers.
DOI: 10.6084/m9.figshare.7251674.v1
title: 20news-18828.tar.gz

description: 20 Newsgroups; duplicates removed, only "From" and "Subject" headers (18828 documents)
DOI: 10.7294/26227082.v1
title: Arthropod diversity in shallow subterranean habitats of the Appalachian Mountains, morphospecies images, Part I

description: Subterranean arthropods are important components of soils and contribute essential food-web functions and other ecosystem services, however, their diversity and community composition has scarcely been assessed. Subterranean pitfall traps are a commonly used method for sampling soil habitats in Europe but have never been widely implemented in the Americas. We used subterranean pitfall traps to sample previously unsurveyed arthropod communities in southwestern Virginia, U.S. Traps were placed in shallow subterranean habitats (SSHs), underground habitats close to the surface where light does not penetrate, and more specifically at the interface between the soil and underlying milieu souterrain superficiel—a microhabitat consisting of the air-filled interstitial spaces between rocks (abbreviated MSS). In total, 2,260 arthropod specimens were collected constituting 345 morphospecies from 8 classes, 33 orders, and 94 families. A region of the mitochondrial cytochrome c oxidase subunit I (COI) gene was amplified and sequenced, and objective sequence clustering of 3% was used to establish molecular operational taxonomic units (mOTUs) to infer observed species richness. In all, 272 COI barcodes representing 256 mOTUs were documented for rare soil-dwelling arthropod taxa and are published to build a molecular library for future research in this system. This work is the first taxonomically extensive survey of North American soil-dwelling arthropods greater than 10 cm below the soil surface.
DOI: 10.17632/fkz6mbpr2z
title: Synthetic dynamic attributed networks with ground-truth information

description: 1. Synthetic network 1: graphs were built with 200 nodes and 20 snapshots. In the initialization, the nodes are divided into two groups, each one with 100 nodes. Then, at a randomly selected time period, 40 % of the nodes are chosen to migrate to a new community in Dataset 1, and 80 % in Dataset 2. The membership of each node is chosen according to a stochastic block model, where nodes within the same community are connected with probability 0.3, and the edges between communities are drawn with a probability of 0.1.
For the generation of the attributes, we propose 4 cases using a univariate or multivariate normal distribution with a standard deviation of 0.1. In Case 1, a three-variate normal distribution is used to generate three attributes, where only the first attribute contributes to identifying the groups. In Case 2, we propose to use only one meaningful attribute. In Case 3, there are three irrelevant random attributes. In Case 4, there is one irrelevant random attribute. This data correspond to Data17 (change of 40 %), with R0M0 (one relevant attribute), R0M1 (three attributtes with one relevant), R1M0 (one irrelevant attribute), R1M1 (three irrelevant attributes). Data18 (change of 80 %) as a similar interpretation for attributes.

2. Synthetic network 2: we generated 10 timestamps with communities that grow and shrink. We designed 3 datasets with 3 groups of 80, 90 and 100 nodes. The network structure is generated by a degree-corrected stochastic block model. Three types of attributed networks were proposed, with different original link probabilities, to assess strongly assortative structures, weakly assortative structures, and disassortative structures. This data correspond to Data107PV0.21, Data108PV0.1, and Data109PV0.19.

3. Synthetic network 3: we design four datasets with different evolution of links over time,  consisting of attributed networks of 100 nodes and 60 time points were generated. Intra- and inter-cluster edges are selected from a truncated Gaussian distribution in the range [0; 1]. Changes in the structure of the networks are generated between time t = 20 to t = 21 and t = 40 to t = 41. Attributes were generated similarly to Synthetic network 1. This data correspond to Data19, Data20, Data21, and Data22, with similar interpretation for attributes as in Synthetic network 1.

4. Synthetic network 4: We use the synthetic benchmark DANCer to create 12 attributed graphs with undirected edges that can change over time, where nodes are grouped into densely connected sets, relatively homogeneous according to the attributes. The number of nodes start at 1000 nodes, the number of communities at 10, and the number of edges at 5000, which increase over time, ending with a maximum value of 4814 nodes, 15 communities and 21908 edges among the built networks. This data correspond to Data122 to Data125, Data128 to Data131, and Data135 to Data138.

For all datasets, 10 seeds where used. All data files were generated using Matlab.
DOI: 10.17632/xsphgmmh7b
title: 6000+ Indian Food Recipes Dataset

description: When I browsed for a Food Recipes (Especially Indian Food) Dataset, I could not find one (that I could use) online. So, I decided to create one. The dataset has following fields (self-explanatory) - ['RecipeName', 'TranslatedRecipeName', 'Ingredients', 'TranslatedIngredients', 'Prep', 'Cook', 'Total', 'Servings', 'Cuisine', 'Course', 'Diet', 'Instructions', 'TranslatedInstructions']. The datset contains a csv and a xls file. Sometimes, the content in Hindi is not visible in the csv format. You might be wondering what the columns with the prefix 'Translated' are. So, a lot of entries in the dataset were in Hindi language. To take care of such entries and translating them to English for consistency, I went ahead and used 'googletrans'. It is a python library that implements Google Translate API underneath. The code for the crawler, cleaning and transformation is on my Github repository (@kanishk307). The dataset has been created using Archana's Kitchen Website. It is a great website and hosts a ton of useful content. You should definitely consider viewing it if you are interested. The dataset can be used to answer a lot of questions related to Food Recipes. You can see the explore the serving sizes, time required to prepare a dish, most common ingredients, different cuisines, diets, courses and what not. I hope this dataset helps the Analytics community.
DOI: 10.6084/m9.figshare.16653046.v5
title: spatial_timeseries_hydrology

description: See README
DOI: 10.6084/m9.figshare.21487101.v1
title: DEM

description: dem data
DOI: 10.6084/m9.figshare.21593310.v3
title: StMary-PPGRR.zip

description: StMary Introduction The StMary dataset is Photoplethysmography(PPG) and Respiratory Rate(RR) dataset acquired from surgical intensive care unit(SICU) of Catholic University Seoul St. Mary's Hospital, South Korea. Data Collection The StMary dataset was acquired from a SICU at the hospital using our in-house web application, PPG Signal Collector. Using laptop, we connected a network between the web application and a patient monitor. Through pulse oximeter which connected to the patient monitor, we collected photoplethysmogram of patients and healthy volunteers. Subject's demographic information must be entered before collecting. After that, PPG signal continuously recorded during the time limit we had set and Respiraotry Rate also recorded manually by medical staff pressing spacebar at the exhalation time. The time limit was set up to 2 minutes and 10 seconds. When the time ends, the data is recorded as history and has stored in the database. The collection procedure is as follows: Move into local monitoring page through the browser installed in the laptop. (Set up the measuring time additionally) Register subject’s demographic – gender, age, diagnosis – to start recording. If a window pop-up, click the start button to record PPG. Press spacebar when the subject exhales. When the time outs, the window closed, and the data stored automatically. Data Files The StMary dataset consists of 50 patients and 50 healthy volunteers both aged 18 and older. The dataset has a demographic information, PPG signal and exhalation timestamp for each de-identified subject. 2-minute PPG was collected for each subject twice, and it was sampled into 125Hz. A unique ID is generated for each collected 2-minute PPG. While this ID serves as an identifier for individual data, serving as a de-identifier for the subject at the same time. Patient.csv Diagnosis: Indicates diagnosis of the subject. For volunteers, mark '0'. For patients, name the diagnosis. Gender: Indicates gender of the subject. ("M"/"F") Age: Indicates age of the subject. startMeasureTime: Indicates time when the data starts to move into DB server. endMeasureTime: Measurement ending time. Pleth.csv Timestamp: Indicates time when the PPG at that time collected. Pleth: Indicates PPG which acquired from pulse oximeter.The StMary dataset is Photoplethysmography(PPG) and Respiratory Rate(RR) dataset acquired from surgical intensive care unit(SICU) of Catholic University Seoul St. Mary's Hospital, South Korea. respirationTimeline.csv Timestamp: Indicates time when subject exhales. <br> All protocol followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. All data were anonymized, deidentified and aggregated. This data was approved by the Institutional Review Board of the Ethics Committee of Seoul St. Mary’s Hospital (IRB No. KC21ONSI0839) and the need for consent was waived by IRB.
DOI: 10.6084/m9.figshare.29105252.v1
title: <b>Table S</b><b>3</b><b>:</b> Descriptive statistics for discrete variables related to colostrum management and calf feeding practices, analyzed across different dairy production systems (LOW = 12.0 L/cow per day (7.4–14.5); INT = 18.0 L/cow per day (14.6–22.5); and UPP = 26.7 L/cow per day (22.6–32.0).

description: Descriptive statistics for discrete variables related to colostrum management and calf feeding practices.
DOI: 10.17632/t3hy6cf4n7
title: Data for: Observational assessment of food safety behaviours at farmers' markets in Ontario, Canada: a cross-sectional study

description: Food safety behaviour data observed at farmers' markets in Ontario, Canada, 2018
DOI: 10.6084/m9.figshare.16619635.v1
title: Brazilian Municipalities - shapefile

description: Brazil shapefile
DOI: 10.6084/m9.figshare.24297544.v4
title: Chromosome-scale genome assembly of sweet tea (<i>Lithocarpus polystachyus </i>Rehder)

description: The genome assembly and annotation data of <i>Lithocarpus polystachyus</i>.
DOI: 10.6084/m9.figshare.25466656.v1
title: Wang Jian-HJS-Research Data

description: This dataset includes pollen, δ<sup>13</sup>C<sub>TOC</sub>, δ<sup>18</sup>O<sub>carb</sub>-δ<sup>13</sup>C<sub>carb,</sub> and magnetic susceptibility from the HJS section in the Tongxin Basin, midwestern China.
DOI: 10.6084/m9.figshare.28600136.v1
title: Ziegenhain_data

description: Experimental results for correlation analysis of the Ziegenhain dataset in AcImpute.
DOI: 10.15468/dl.h4wq24
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chanos chanos (Forsskål, 1775)"
 ]
}

 The dataset includes 112 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013367-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17605/osf.io/fe5a2
title: Motivation for language learning and international migration

description: Today, insights into the motivation of L2 learners in universities and schools abound. However, findings can translate poorly to populations of adult migrants where language proficiency is inextricably bound up with life opportunities where the future is often uncertain , and where L2 learning is a long time high stakes process (Ortega, 2019). To address this knowledge gap, the purpose of the research was (i) to identify motivation constructs relevant to the situation of adult L2 learners in circumstances of precarity, and (ii) to develop and validate scales to measure motivation.
DOI: 10.6084/m9.figshare.1533034.v1
title: Supplemental material for Hogle et al. 2015 mBio

description: Title: "Trace metal acquisition by heterotrophic bacterioplankton with contrasting trophic strategies" Dataset contains all supplemental files available at mBio website plus additional files including python, perl, and R scripts
DOI: 10.17632/4gkcyxjyss
title: SorghumWeedDataset_Classification

description: Purpose of dataset creation: ‘SorghumWeedDataset_Classification’ is created to address real-time weed challenges and encourage weed research using computer vision applications. About the dataset: ‘SorghumWeedDataset_Classification’ is a crop-weed research dataset with 4312 data samples, which can be used for image classification. Sorghum samplings (Class 0), Grasses (Class 1), and Broad-leaf weeds (Class 2) are the three research objects focused during this data acquisition process. This dataset contains 1404 samples of sorghum samplings, 1467 samples of grasses, and 1441 samples of broadleaved weeds. The TVT (Train: Validate: Test) ratio is set as 7:2:1 to split the data samples into training, validation, and testing. Data samples with class labels are provided herewith. Equipment used for data acquisition: To record a rich set of information on the research objects, a state-of-the-art instrument - Canon EOS 80D – a Digital Single Lens Reflex (DSLR) camera with a sensor type of 22.3mm x 14.9 mm CMOS is used. Data type, format, and size: Each data sample is an RGB image represented in JPEG format with 6000 × 4000 pixels making an average size of 13MB each. All data samples are re-sized to 224 × 224 pixels without information loss to reduce the computation complexity. Data acquisition: This dataset emphasizes the early stages of crop growth to meet the challenges faced during the ‘Critical period of weed competition’. Data samples are captured from agriculture fields that follow both uniform crop spacing and random crop spacing. Temporal coverage: Data is acquired during April and May 2023. To generalize the dataset, acquisition is done in various light and weather conditions with varying distances. Geographical coverage: Data is acquired from Sri Ramaswamy Memorial (SRM) Care Farm, Chengalpattu district, Tamil Nadu, India. To the best of our knowledge, ‘SorghumWeedDataset_Classification’ is the first open-access crop-weed research dataset from Indian fields for classification that deals with weed issues in uniform and random crop-spacing fields. Expected outcome: The expected outcome of this dataset will be an Artificial Intelligence (AI) model that predicts the correct class of a particular data sample. Detailed description: A detailed description of the dataset and data acquisition process is given in the data article entitled “ ‘SorghumWeedDataset_Classification’ And ‘SorghumWeedDataset_Segmentation’ Datasets For Classification, Detection, and Segmentation In Deep Learning “. (Submitted in the journal ‘Data in Brief’ on 25/09/2023 and awaiting publication) Citation: If you find this dataset helpful and use it in your work, kindly cite this dataset using “Michael, Justina; M, Thenmozhi (2023), “SorghumWeedDataset_Classification”, Mendeley Data, V1, doi: 10.17632/4gkcyxjyss.1” Further queries: If any queries/suggestions concerning this dataset, please e-mail us at thenmozm@srmist.edu.in [corresponding author]
DOI: 10.6084/m9.figshare.29052692.v1
title: <i>Pistia stratiotes</i>: Una alternativa para la fitorremediación y potencial candidato como ingrediente de forraje de dietas para conejo blanco

description: Aunque el acceso al agua suficiente, asequible y de calidad es un derecho humano, no es garantizado eficazmente en México. Esto hace urgente las estrategias de mitigación de contaminación de agua. Se sugiere incorporar la biorremediación como alternativa sustentable para el retiro de contaminantes orgánicos e inorgánicos de cuerpos de agua. Especies como Pistia stratiotes sobreviven en agua contaminada aprovechando los desechos como nutrientes y produciendo biomasa rica en proteína, sin embargo, se desconoce cómo algunos factores pueden afectar la fisiología vegetal y reducir el valor nutricional de este lirio. Por tanto, el objetivo de esta investigación fue evaluar la biomasa de P. stratiotes L. producida a través de la fitorremediación de agua como potencial ingrediente para forraje de engorda de conejo blanco neozelandés. Esto se llevó a cabo en dos grandes fases: 1) Investigación documental en la que se describe el marco teórico y, 2) Investigación original, que mostró que múltiples contaminantes reducen la cantidad de proteína en P. stratiotes y se acumulan en hojas y raíces de esta. Así, la planta podría provocar efectos mutagénicos o tóxicos en caso de ser ingerida por el ganado. Entonces, esta planta no es recomendable para fitorremediación y uso posterior como forraje en producción agropecuaria. Solo las aguas residuales incrementaron el contenido de proteína en P. stratiotes. Así, se estableció un experimento con aguas residuales provenientes del cultivo de mojarra para promover la acumulación de proteína en los tejidos de P. stratiotes L. a través de su cultivo en aguas residuales de un sistema productivo de mojarra. Se encontró que el agua residual de la acuacultura con mojarra promueve el crecimiento y reproducción del lirio al mismo tiempo que reduce su cantidad de sólidos totales. No obstante, debido a los riesgos colaterales del cultivo de la planta en aguas residuales, un último experimento buscó inducir el incremento de proteína relativa en la biomasa de P. stratiotes a través de su exposición a diferentes niveles luz en un medio de cultivo hidropónico inocuo. Se logró promover un 13.8% de proteína base seca, un valor que se encontró dentro del rango de otras especies que han sido incluidas como ingredientes de dietas de conejo. Por lo tanto, el aprovechamiento de P. stratiotes como ingrediente de dietas de conejo, aunque prometedor, requiere futuros esfuerzos experimentales para poner a prueba su contenido de otros nutrientes.
DOI: 10.6084/m9.figshare.12912257.v3
title: GazeBase Data Repository

description: GazeBase is a large-scale longitudinal dataset consisting of 12,334 monocular (left) eye-movement recordings captured from 322 college-aged subjects. Subjects completed a battery of seven tasks in two adjacent sessions during each round of recording, including a - 1) fixation task, 2) horizontal saccade task, 3) random oblique saccade task, 4) reading task, 5/6) free viewing of cinematic video task, and 7) gaze-driven gaming task. A total of nine rounds of recording were conducted over a 37 month period, with participants in each subsequent round recruited exclusively from prior rounds. All data was collected using an EyeLink 1000 eye tracker at a 1,000 Hz sampling rate, with a calibration and validation protocol performed before each task to ensure data quality. Due to its large number of participants and longitudinal nature, GazeBase is well suited for exploring research hypotheses in eye movement biometrics, along with other emerging applications applying machine learning techniques to eye movement signal analysis. Eye movement classification labels produced by the instrument's real-time parser are provided for a relevant subset of GazeBase, along with pupil area. Full details regarding the experimental protocol are available in the corresponding data descriptor manuscript.
DOI: 10.6084/m9.figshare.24874032.v1
title: Supplementary Material for: Polydrug use typologies of regular ecstasy users visiting electronic dance music events: A Latent Class Analysis

description: Background
Polydrug use patterns of young adults using ecstasy vary, as well as their willingness to change them. Polydrug use patterns are likely associated with different adverse health outcomes. It is unknown whether polydrug use patterns of young adults that use ecstasy are similar in different countries. 
Objective
This study aims to identify and compare polydrug use patterns and willingness to change it of young adults that use ecstasy in the United Kingdom (UK) and the Netherlands, two countries with a high prevalence of ecstasy use and a large electronic dance music (EDM) scene.
Method
The data from the online cross-sectional Electronic Music Scene Survey was used in a latent class analysis. The binary indicators used in the estimation were past year substance use of 21 different substances. The sample consisted of young adults ecstasy users that regularly visit EDM events (age 18-34).
Results
1077 respondents from the UK (age M=23.1 ) and 1178 from NL (age M=23.7)) that regularly visit EDM events were included in the analyses. In both countries, three polydrug use patterns of ecstasy users were identified based on BIC fit indices: a traditional polydrug use class (UK: 28%; NL: 40%), a stimulant and ketamine polydrug use class (UK: 48%; NL: 52%), and an extensive polydrug use class (UK: 24%; NL: 8%) characterized by substantial use of stimulants, depressant and psychedelic substances. Overall, young adults that used ecstasy in the UK consumed 3,4-Methylenedioxymethamphetamine (MDMA) more often as powder/crystalline and at higher dosages compared to young adults in NL who preferred MDMA tablets. Regardless of polydrug class or country, most respondents indicated that they had the intention reduce but not quit their use.
Conclusions
In both countries, structurally similar polydrug use patterns among young adults that use ecstasy were found, while the use frequencies of individual substances and preferred MDMA form varied between the countries.