cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5p5sv8hshj
title: Dataset for a Dynamic Multi-Period Vehicle Routing Problem

description: This dataset involves the normal distribution parameters, calculated using used to create simulation instances for the experimental study of the paper "Dynamic Multi-Period Vehicle Routing with Touting". These distributions are used to generate random demand on each planning day. The dataset also includes 3-months historical collections data for a waste collection company, which are used to compare the heuristic solutions with the ones obtained by an exact solver. There are two sets of customers, belonging to two drivers, who cover different geographical areas. The information for two drivers are stored in separate excel files and each file has 2 sheets, as explained below: In "Orders" sheet, The historical collections information is presented. The amount of demand is given in Column A, while the day the demand is generated is provided in Column C. Column B has an index value for each customer, indicating its location. The depot has the index of "1". In "Customer Information" sheet, The list of customers is presented in Column A. They are named starting from C1. Columns B and C present the mean and variance values of the customers' demand distributions. Column D provides the index value for each customer, indicating its location. Finally, Column E presents the tank capacity values for the customers. In "Distance-Time Matrices" sheet, The distance (in Column C) and travel time (in Column D) values for each pair of nodes are presented. Distances and travel times are given in kilometres and minutes, respectively. "from" and "to" entries are associated with the index values of the customers. For example, to find the distance and travel time from C1 to C2 for Driver 1, one needs to look at the values from index 167 to index 168, which corresponds to a 28.7 kilometres of distance and 21.45 minutes of travel time. Driver 1 and 2 have 142 and 125 unique customers, respectively. However, the historical data includes total of 273 and 260 customers for Driver 1 and Driver 2, respectively, which means that some customers have multiple orders within that 3-month period. Finally, the problem parameters, such as vehicle capacity and time limits are provided in the file "Problem Parameters.txt".
DOI: 10.17632/drznt4d6r8
title: Data: Return and volatility linkages between global bond funds and commodities: Evidence across time horizons and market regimes

description: ㅇㅁㅅㅁ ㅐㄹ 히ㅐㅠ미 ㅠㅐㅜㅇ 려ㅜㅇㄴ
DOI: 10.17632/zrckm6tdhn
title: Synthetic data for: Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

description: The folder contains all the synthetic dataset used for the experiments described in the paper. Each file .mat contains: - At: the true effective connectivity matrix; - data: a cell array where each entry contains the data for a MC run; data{i}.y is the BOLD signal times-series (without measurement noise), data{i}.x is the neuronal activity time-series, data{i}.u is the excitation time-series, data{i}.e_gauss is the measurement noise; - n: number of monitored brain regions - N: number of data samples - noise_std: standard deviation of data{i}.u
DOI: 10.17632/w2kzmvfhb2
title: University students' perceived self-efficacy and justice datasets in a PBL project allocation context at a Sino-British international university in China

description: This research tested three hypotheses: H1. Perceived self-efficacy predicts perceived procedural justice; H2. Perceived self-efficacy predicts perceived distributive justice; H3. Perceived self-efficacy predicts perceived learning equity. The SEM test results reject the null hypothesis at a 0.1% significance level. Perceived self-efficacy was positively associated with perceived procedural justice, perceived distributive justice, and perceived learning equity. The data was collected in a PBL course at a Sino-British international university in China. This data link includes three excel files. The excel file named "scale questions" lists the question number and question description of the integrated scale used to collect data. The excel file named "PBL project allocation dataset1" includes the self-reported scale responses from sample 1 (N=226). Students of sample 1 used manual project allocation for PBL. The excel file named "PBL project allocation dataset2" includes the self-reported scale responses from sample 2 (N=537). Students of sample 2 used automated project allocation for PBL. The two datasets can be used for exploratory factor analysis with all the questions being included. They can also be used for confirmatory factor analysis to test the conceptual model proposed in our research paper that links to this data. For more details about the conceptual model, please read the research paper.
DOI: 10.17632/xjt4hwd66c
title: A set of 48 Target-capture probes for Silene

description: A set of 48 target capture probes for Silene and related genera. The probes have been annotated using BLASTx for gene identification and identified proteins have been aligned using GeneWise to locate exon-intron regions.
DOI: 10.17632/32kgvhsyzh
title: A bi-objective optimization approach for carbon capture and storage supply chain network combining with pricing policies: Economic and social aspects

description: This data belongs to the research paper entitled "A bi-objective optimization approach for carbon capture and storage supply chain network combining with pricing policies: Economic and social aspects"
DOI: 10.17632/35gb4v72dr
title: BananaSet: A Dataset of Banana Varieties in Bangladesh

description: This dataset presents an assortment of high-resolution images that exhibit six well-known banana varieties procured from two distinct regions in Bangladesh. These bananas were thoughtfully selected from rural orchards and local markets, providing a diverse and comprehensive representation. The dataset serves as a visual reference, offering a thorough portrayal of the distinct characteristics of these banana types, which aids in their precise classification. It encompasses six distinct categories, namely, Shagor, Shabri, Champa, Anaji, Deshi, and Bichi, with a total of 1166 original images and 6000 augmented JPG images. These images were diligently captured during the period from August 01 to August 15, 2023. The dataset includes two variations: one with raw images and the other with augmented images. Each variation is further categorized into six separate folders, each dedicated to a specific banana variety. The images are of non-uniform dimensions and have a resolution of 4608 × 3456 pixels. Due to the high resolution, the initial file size amounted to 4.08 GB. Subsequently, data augmentation techniques were applied, as machine vision deep learning models require a substantial number of images for effective training. Augmentation involves transformations like scaling, shifting, shearing, zooming, and random rotation. Specific augmentation parameters included rotations within a range of 1° to 40°, width and height shifts, zoom range, and shear ranges set at 0.2. As a result, an additional 1000 augmented images were generated from the original images in each category, resulting in a dataset comprising a total of 6000 augmented images (1000 per category) with a data size of 4.73 GB.
DOI: 10.17632/xjmtb22pff
title: Video Dataset for Safe and Unsafe Behaviours

description: This dataset is presented in the form of high-resolution video clips of safe and unsafe behaviours from a closed production area, to be used in occupational accident prevention studies. The dataset was collected from the security cameras of a production facility operating in an organised industrial zone in Eskişehir, Turkey, after obtaining the necessary permissions from company officials and employees. A total of 8 classes of behaviours, 4 classes of safe and 4 classes of unsafe behaviours, were identified for the dataset and 691 video clips containing these behaviours were obtained. 

The videos in the workplaces for this dataset were obtained between 5 November 2022 and 13 December 2022 from "Kafaoğlu Metal Plastik Makine San. ve Tic. A.Ş." in Eskisehir, Turkey between 5 November 2022 and 13 December 2022.
DOI: 10.15468/dl.9gxwkv
title: Occurrence Download

description: A dataset containing 1510 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Nassella neesiana (Trin. & Rupr.) Barkworth",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 1510 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012905-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/fw456dctnm
title: Tamil Palmleaf Dataset

description: Data for Pulication in Data in Brief -The name of the samples are ’NALADIYAR’ (29), ‘THOLKAPPIYAM’ (227), and ‘THIRIKADUGAM’ (15), Samples contain information on mortal discipline and health, authoritative text on Tamil grammar, and medicine.
DOI: 10.17632/pfdbfpfygh
title: openpits asbestos

description: Database includes images of open-pit places taken in the Bazhenovskoye field, Russia. All images are taken in the different weather and day conditions. All data are labeled for instance segmentation (as well as object detection) problems and have labeling in the COCO format. The archive contains both: all data in the images folder and annotation in the annotations folder. The labeling was performed manually in the CVAT software. The image size is 2592 × 2048.
DOI: 10.17632/r57drh58pm
title: Gypsum from soils of chesas, NE Spain

description: We show data of soils developed on the outcropping gypseous nucleus of the Barbastro-Balaguer anticline, NE Spain. Chesas is the local name for the lands of this outcrop that stands out in the landscape by its whitish tones. The data collected here come from soils of the municipalities —from West to East— of Peraltilla, Almunia de San Juan, San Esteban, Tamarite, Alcampell, Ivars de Noguera, Camarasa, Torá, and Iborra. The analytical methods plus details of the landscape and sampling sites can be found in: (i) the book entitled “Morfología y génesis de suelos sobre yesos” (http://hdl.handle.net/10261/84695), and (ii) the article entitled “Salada Farrachuela, a saline wetland in Tamarite de Litera, Spain” (DOI: 10.29077/bol.114.ce05.herrero).
Here we present a compressed zip file with 300 TIFF images corresponding to 150 thin sections of these soils scanned under both plane polarized and crossed polarizers light. Some of the sections, marked L, are of lichens and their gypseous substrate. Most of the sections —with the prevalent size of 13.5 × 5.7 cm— were manufactured by the first author in the Institut National Agronomique de Paris-Grignon under the technical supervision of Mr. Pierre Guilloré, in the context of a scholarship granted by the Government of France for working in the lab of Dr. Nicolas Fédoroff. The surviving thin sections are archived at the EEAD-CSIC in Zaragoza, Spain. Their scans are compiled in the file “Chesas_150thinsections.zip”.
Several dozen micrographs of the chesas thin sections appeared in the above mentioned book published in 1991 (http://hdl.handle.net/10261/84695). The file “Chesas_Fig.captions1991-01.xlsx” presents a tentative English translation of the 154 Figure captions for the micrographs and the other figures in the book.
The analytical results for the samples having gypsum determinations are listed in the files “ChesasGyps_Peraltilla-Tora-Iborra.xlsx” and “ChesasGyps_Tamarite.xlsx”

Documents:
Chesas_150thin_sections.zip
Chesas_Fig.captions1991-01.xlsx
ChesasGyps_Peraltilla-Tora-Iborra.xlsx
ChesasGyps_Tamarite.xlsx

Funding
We acknowledge the grant PID2021-127170OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and the grant TED2021-130303B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGeneration EU/PRTR”.
DOI: 10.17632/23zgg8rtb2
title: Petrophysical and petrothermal raw data of the sedimentary succession in the Oliana anticline (Southern Pyrenees)

description: This dataset includes raw petrophysical (mineral density, bulk density, connected porosity, permeability and P-wave velocity) and petrothermal (thermal conductivity and effusivity) data from detrital, carbonate and evaporite rocks of the folded sedimentary succession of the Oliana anticline in the Southern Pyrenees.
DOI: 10.17632/rtphmvz5fj
title: 8 Mediterranean Pelagic Commercial Fish Species for Distribution Modelling

description: The objective is to create a species distribution model to predict the species probability in each longitude, latitude pair. The fish species Engraulis encrasicolus (729 samples), Sardina pilchardus (596 samples), Sardinella aurita (720 samples), Scomber colias (676 samples), Scomber scombrus (807 samples), Spicara smaris (1225 samples), Thunnus thynnus (1237 samples) and Xiphias gladius (1230 samples) are included. The target for prediction is the overall probability of occurrence. There are 6830 predictors created from 15 environmental variables: temperature, salinity, dissolved oxygen, meridional current, zonal current, chlorophyll, euphotic depth, secchi disk depth, wave height, nitrate, phosphate, distance to coast, distance to major river, bathymetry, and substrate.
DOI: 10.17632/2ssdnr3nn5
title: Efficacy of the Metacognitive Training on symptom severity, neurocognition and social cognition in a sample of patients diagnosed with schizophrenia

description: The dataset contains data from the project "Efficacy of the Metacognitive Training on symptom severity, neurocognition and social cognition in a sample of patients diagnosed with schizophrenia: a single blind, randomised, controlled trial". The database indicates the missing data. Data imputation was completed using the Predictive Mean Matching (PMM) method.
Data were collected in two different psychiatric wards. Participants were randomized into intervention and control groups. Participants in the intervention group took part in a standard Metacognitive Training (MCT) along with psychiatric treatment as usual (TAU,), while patients in the control group only received TAU. Data were recorded at three time points: before MCT (T0), after MCT (T1) and after a six-month follow-up period (T2). The assessment time points are indicated in the column headings. IQ values shown were used to screen for inclusion criteria (IQ > 70). The database contains data from the following measures in that order:  Positive and Negative Syndrome Scale for Schizophrenia (PANSS) , Theory of Mind Picture Stories Task (ToM PST), Baron-Cohen Reading the Mind in the Eyes Test (RMET), Wisconsin Card Sorting Test (WCST), and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS).    
Code dictionary of the dataset: 
Intervention_Control: 1= randomised to intervention group; 2= randomised to control group
Gender: 1= male; 2= female
Marital status: 1= single; 2= married; 3= divorced; 4= widowed; 5= in a relationship
Level of education: 1= less than primary education; 2= primary education; 3= vocational education; 4= high school diploma; 5= higher education
Occupational status: 1= supported job for psychiatric patients; 2= normal employment part-time; 3= normal employment full-time; 4= unemployed
Antipsychotic treatment: 1= yes, 2= no
DOI: 10.17632/gpdsntb3y6
title: Drone images of sandy beaches and anthropogenic litter along the Saudi Arabian Red Sea
DOI: 10.15468/dl.prtfff
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argentina Linnaeus, 1758",
 "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 ((9.2698 2.0276,9.1702 1.8496,17.9648 1.8496,17.9648 10.8496,8.9648 10.8496,8.9648 2.8113,8.9984 2.8234,9.2654 3.2175,9.4253 3.0783,9.2698 2.0276))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012906-250525065834625/datasets/export for details.
DOI: 10.17632/35wbdcy3bc
title: Replication Files for "International joint ventures and internal technology transfer versus external technology spillovers: Evidence from China"

description: Data and scripts necessary for replication of the results discussed in Jiang et al., "International joint ventures and internal technology transfer versus external technology spillovers: Evidence from China," Journal of International Economics.
DOI: 10.17632/45f952y38r
title: KU-HAR: An Open Dataset for Human Activity Recognition

description: (Always use the latest version of the dataset. ) Human Activity Recognition (HAR) refers to the capacity of machines to perceive human actions. This dataset contains information on 18 different activities collected from 90 participants (75 male and 15 female) using smartphone sensors (Accelerometer and Gyroscope). It has 1945 raw activity samples collected directly from the participants, and 20750 subsamples extracted from them. The activities are: Stand➞ Standing still (1 min) Sit➞ Sitting still (1 min) Talk-sit➞ Talking with hand movements while sitting (1 min) Talk-stand➞ Talking with hand movements while standing or walking(1 min) Stand-sit➞ Repeatedly standing up and sitting down (5 times) Lay➞ Laying still (1 min) Lay-stand➞ Repeatedly standing up and laying down (5 times) Pick➞ Picking up an object from the floor (10 times) Jump➞ Jumping repeatedly (10 times) Push-up➞ Performing full push-ups (5 times) Sit-up➞ Performing sit-ups (5 times) Walk➞ Walking 20 meters (≈12 s) Walk-backward➞ Walking backward for 20 meters (≈20 s) Walk-circle➞ Walking along a circular path (≈ 20 s) Run➞ Running 20 meters (≈7 s) Stair-up➞ Ascending on a set of stairs (≈1 min) Stair-down➞ Descending from a set of stairs (≈50 s) Table-tennis➞ Playing table tennis (1 min) Contents of the attached .zip files are: 1.Raw_time_domian_data.zip➞ Originally collected 1945 time-domain samples in separate .csv files. The arrangement of information in each .csv file is: Column 1, 5➞ exact time (elapsed since the start) when the Accelerometer & Gyro output was recorded (in ms) Col. 2, 3, 4➞ Acceleration along X,Y,Z axes (in m/s^2) Col. 6, 7, 8➞ Rate of rotation around X,Y,Z axes (in rad/s) 2.Trimmed_interpolated_raw_data.zip➞ Unnecessary parts of the samples were trimmed (only from the beginning and the end). The samples were interpolated to keep a constant sampling rate of 100 Hz. The arrangement of information is the same as above. 3.Time_domain_subsamples.zip➞ 20750 subsamples extracted from the 1945 collected samples provided in a single .csv file. Each of them contains 3 seconds of non-overlapping data of the corresponding activity. Arrangement of information: Col. 1–300, 301–600, 601–900➞ Acc.meter X, Y, Z axes readings Col. 901–1200, 1201–1500, 1501–1800➞ Gyro X, Y, Z axes readings Col. 1801➞ Class ID (0 to 17, in the order mentioned above) Col. 1802➞ length of the each channel data in the subsample Col. 1803➞ serial no. of the subsample Gravity acceleration was omitted from the Acc.meter data, and no filter was applied to remove noise. The dataset is free to download, modify, and use. More information is provided in the data paper which is currently under review: N. Sikder, A.-A. Nahid, KU-HAR: An open dataset for heterogeneous human activity recognition, Pattern Recognit. Lett. (submitted). A preprint will be available soon. Backup: drive.google.com/drive/folders/1yrG8pwq3XMlyEGYMnM-8xnrd6js0oXA7
DOI: 10.17632/6xhdszzpmv
title: Code and Data for "Share Repurchases Under Economic Policy Uncertainty: Evidence from China"

description: This dataset allows you to replicate all the processes in the article "Share Repurchases Under Economic Policy Uncertainty: Evidence from China." Please download the attached zip file, which contains the code and data used to reproduce this article (including main data, program-level data, and monthly-level data). The code provides detailed documentation on the source of each table. Please note that different tables may require opening different datasets. If you have any questions, feel free to contact huangchenghao@hotmail.com.
DOI: 10.17632/bwh3zbpkpv
title: Dataset for Crop Pest and Disease Detection

description: The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The main goal of AI in agriculture is to improve crop yield, control crop pests/diseases, and reduce cost. The agricultural sector in developing countries faces severe in the form of disease and pest infestation, the knowledge gap between farmers and technology, and a lack of storage facilities, among others. To help address some of these challenges, this work presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images ( 6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test set consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research community.
DOI: 10.17632/nsc7hnsg4s
title: The Dataset for: Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning 

description: A dataset with 77 LFP/graphite cells (1.1 Ah nominal capacity and 3.3 V nominal voltage). The cells were cycled with an identical charge protocol but different multi-stage discharge protocols at a constant temperature of 30°C.
DOI: 10.17632/sphxfrywwh
title: Behavioral and emotional problems and parenting stress among Kampung Sungai Buah aborigine and non–aborigine children: a comparative cross-sectional study

description: Background and aims: There is a dearth of studies conducted on the Malaysian aborigine people who are known locally as the ‘Orang Asli’ (OA). The purpose of this study was to describe the behavioural and emotional problems in OA and non-aborigine (NA) children in Kampung Sungai Buah and its association with parenting stress in both communities. Materials and Methods: This was a cross-sectional comparative community study. A total of 27 OA and 39 NA parents answered the Strengths and Difficulties Questionnaire (SDQ) and Parenting Stress Index (PSI) regarding 64 OA children and 80 NA children. Results: OA children had higher total SDQ scores than NA children (p = 0.010). However, OA children had less conduct problems than NA children (p = 0.005). OA showed overall lower parental distress (p = 0.032) compared to NA. OA mothers had more parental distress than OA fathers (p = 0.02). There was negatively moderate correlation between total SDQ and total PSI scores within both OA (r=-0.27) and NA (r=-0.50) communities. Conclusion: With the OA community being at a significant disadvantage in terms of education, and income, it is worrying that the Orang Asli children experience more behavioral and emotional problems compared to a conventional community. Further studies exploring OA parenting styles, help-seeking behavior and community support might provide more insight on how the OA parents approach child mental health problems.
DOI: 10.17632/sctxx6w7bg
title: Original WB images

description: Uncropped images of original western blotting