cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ncy5btsk4m.1
title: Dataset for the publication entitled "Validation of a novel low-fidelity virtual reality simulator and artificial intelligence assessment approach for peg transfer laparoscopic training"

description: Complete dataset for the publication entitled "Validation of a novel low-fidelity virtual reality simulator and artificial intelligence assessment approach for peg transfer laparoscopic training":

"Datasets.zip": This file contains the annotated datasets that were used for the training and testing of the object detection model. The annotations are in YOLO format.
"Evaluation sheet.xlsx": This file contains the evaluation form that the experts had to fill in during the evaluation.
"Likert scale questions".xlsx: This file contains the questions of the five points Likert scale about the VR simulator.

Virtual reality (VR) simulator related files:
"Operating room.jpg": This is an edited and optimized 360° photo about an operating room. Created by the research team. 
"Dissector.fbx": This is the 3D model of a Maryland dissector, created by the research team. 
"Rubber.fbx": This is the 3D model of the object that was used during the peg transfer exercise. The model was created by the research team. 
"VR  simulator box.fbx": This is the 3D model of the trainer box that was used with the VR simulator.
"VR simulator controller modifier.mf": This is the 3D model of the object that allows the VR controller to be converted into a laparoscopic instrument.

Please, upon citation, refer to the original article as well.
DOI: 10.17632/n2v49z2vnp.1
title: Real life data for operating room scheduling problem

description: This data is captured from OR log in Gent UZ during May 2017. The data is in HTML Format. It starts with a visual description of the waiting list.  The rest of the data is encapsulated in the related object (e.g. patient, surgeon, ...).
DOI: 10.17632/36tfjc42hm.1
title: The scRNA-seq datas of human umbilical cord mesenchymal stromal cells (22+5 W and 28 W)

description: Cell Preparation: HUMSCs in passage 3 from three different gestational ages (22+5 W, 28 W) were used in this study. Cell survival rates were checked and single-cell suspensions with survival rates above 80% were washed and resuspended to a suitable concentration of 700-1200 cells/μl for 10x Genomics Chromium (10X Genome, Single Cell 3’ Library & Gel Bead Kit v.3). The system was operated on the machine. GEM Creation & Thermal Cycling: Gel Bead in Emulsion (GEMs) were created for single-cell separation according to the number of cells to be harvested. Once the GEMs were formed, they were collected for reverse transcription in a PCR machine for labeling.Post Cycling Cleanup & cDNA Amplification: The GEMs were treated with oil and the amplified cDNA was purified by magnetic beads. It then underwent cDNA amplification and quality inspection. Library Preparation & Quantification: The 3ʹ Gene Expression Library was constructed with the quality-qualified cDNA. After fragmentation, adapter ligation, sample index PCR, etc., the library was finally quantitatively examined.Sequencing: The final library pool was sequenced on the Illumina HiSeq3000 using 150-base-pair paired-end reads.
DOI: 10.17632/bv33n4vjxm.1
title: Dataset on Digital Financial Inclusion and Artificial Intelligence: A Structural Equation Modeling (SEM) Approach

description: This data is the Excel format of the survey dataset generated from the fieldwork on Digital financial inclusion and Artificial intelligence.  The responses from the respondents have been coded with a five-point Linkert scale. The data was used to measure the impact of artificial intelligence applications on digital financial inclusion.  The data set tests the hypothesis that artificial intelligence applications have significantly influenced digital financial inclusion among commercial banks particularly in South West Nigeria.  Thus, it measures whether there exists a significant relationship between the adoption and utilization of artificial intelligence applications and its significant role in the promotion of digital financial inclusion with much focus in remote areas. The data is saved in both Excel format.
DOI: 10.5281/zenodo.8122071
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Christian Julius Wilhelm Schiede, http://www.wikidata.org/entity/Q67359. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q67359
DOI: 10.5281/zenodo.8122011
title: Dataset of the paper IT Governance Mechanisms as a Source to Enhance IT Business Value: a case study in an Industry in the South of Minas Gerais (Brazil)

description: Dataset of the study entitled "IT Governance Mechanisms as a Source to Enhance IT Business Value: a case study in an Industry in the South of Minas Gerais (Brazil)". The data are available in the original language it was collected (Brazilian-Portuguese).
DOI: 10.5281/zenodo.8122010
title: Dataset of the paper IT Governance Mechanisms as a Source to Enhance IT Business Value: a case study in an Industry in the South of Minas Gerais (Brazil)

description: Dataset of the study entitled "IT Governance Mechanisms as a Source to Enhance IT Business Value: a case study in an Industry in the South of Minas Gerais (Brazil)". The data are available in the original language it was collected (Brazilian-Portuguese).
DOI: 10.17632/zg8v4s2kpw.1
title: RealLife operating room scheduling dataset, 2021-June-November

description: This dataset pertains to the Operating Room (OR) log file spanning from June 2021 to November 2021, consisting of 20 instances with weekly planning. The instances are classified into 8 group settings, which are elaborated at very below. For detailed explanations of these settings, please refer to the paper currently undergoing revision in the Journal of OMEGA (A  study on policy decisions to embed flexibility for reactive recovery in the planning and scheduling in operating rooms).

Within each folder, such as G_1, you will find one Master Surgery schedule compiled from one-year information, along with 20 instances containing 20 solutions each. These solutions comprise 20 surgical case planning solutions and 20 surgical case (re)planning and scheduling solutions. The file indicating the Master Surgery schedule is named [group number] + MSS.xml, and it is important to note that modifying the settings will result in a different annual master surgery schedule.

The planning solutions are stored with names using the group and instance numbers as an index, i.e., [group number][instance number]+PLInfo.xml. We have employed the same naming convention for the surgical case (re)planning and scheduling solutions, i.e., [group number][instance number]+rePlnSch.xml. Lastly, the instance file is stored with the name [group number]_[instance number]+DataInfo.xml.

Each file contains the initial XML element "description," which demonstrates crucial information related to that specific file.






G_1	Open booking percentage= 0 	 	 Demand size for each group in different discipline= 0.7 . 0.7 	 Tactical blocks= 2 	 Operational blocks= 2 
G_2	Open booking percentage= 0 	 	 Demand size for each group in different discipline= 0.7 . 0.7 	 Tactical blocks= 2 	 Operational blocks= 4 
G_3	Open booking percentage= 0 	 	 Demand size for each group in different discipline= 1.1 . 1.1 	 Tactical blocks= 2 	 Operational blocks= 2 
G_4	Open booking percentage= 0 	 	 Demand size for each group in different discipline= 1.1 . 1.1 	 Tactical blocks= 2 	 Operational blocks= 4 
G_5	Open booking percentage= 0.25 	         Demand size for each group in different discipline= 0.7 . 0.7 	 Tactical blocks= 2 	 Operational blocks= 2 
G_6	Open booking percentage= 0.25 	         Demand size for each group in different discipline= 0.7 . 0.7 	 Tactical blocks= 2 	 Operational blocks= 4 
G_7	Open booking percentage= 0.25 	         Demand size for each group in different discipline= 1.1 . 1.1 	 Tactical blocks= 2 	 Operational blocks= 2 
G_8	Open booking percentage= 0.25 	         Demand size for each group in different discipline= 1.1 . 1.1 	 Tactical blocks= 2 	 Operational blocks= 4
DOI: 10.34740/kaggle/dsv/6091256
title: (Database) Cracks images in mortar coating
DOI: 10.6084/m9.figshare.23638854
title: Malaria case study data.docx

description: Data for a single malaria patient with covid case study
DOI: 10.17632/c8d342266x.1
title: RealLife operating room scheduling dataset, 2021-Jan-May

description: This dataset pertains to the Operating Room (OR) log file spanning from January 2021 to May 2021, consisting of 20 instances with weekly planning. The instances are classified into 8 group settings, which are elaborated at very below. For detailed explanations of these settings, please refer to the paper currently undergoing revision in the Journal of OMEGA (A  study on policy decisions to embed flexibility for reactive recovery in the planning and scheduling in operating rooms).

Within each folder, such as G_1, you will find one Master Surgery schedule compiled from one-year information, along with 20 instances containing 20 solutions each. These solutions comprise 20 surgical case planning solutions and 20 surgical case (re)planning and scheduling solutions. The file indicating the Master Surgery schedule is named [group number] + MSS.xml, and it is important to note that modifying the settings will result in a different annual master surgery schedule.

The planning solutions are stored with names using the group and instance numbers as an index, i.e., [group number][instance number]+PLInfo.xml. We have employed the same naming convention for the surgical case (re)planning and scheduling solutions, i.e., [group number][instance number]+rePlnSch.xml. Lastly, the instance file is stored with the name [group number]_[instance number]+DataInfo.xml.

Each file contains the initial XML element "description," which demonstrates crucial information related to that specific file.






G_1	Open booking percentage= 0 	 	 Demand size for each group in different discipline= 0.7 . 0.7 	 Tactical blocks= 2 	 Operational blocks= 2 
G_2	Open booking percentage= 0 	 	 Demand size for each group in different discipline= 0.7 . 0.7 	 Tactical blocks= 2 	 Operational blocks= 4 
G_3	Open booking percentage= 0 	 	 Demand size for each group in different discipline= 1.1 . 1.1 	 Tactical blocks= 2 	 Operational blocks= 2 
G_4	Open booking percentage= 0 	 	 Demand size for each group in different discipline= 1.1 . 1.1 	 Tactical blocks= 2 	 Operational blocks= 4 
G_5	Open booking percentage= 0.25 	         Demand size for each group in different discipline= 0.7 . 0.7 	 Tactical blocks= 2 	 Operational blocks= 2 
G_6	Open booking percentage= 0.25 	         Demand size for each group in different discipline= 0.7 . 0.7 	 Tactical blocks= 2 	 Operational blocks= 4 
G_7	Open booking percentage= 0.25 	         Demand size for each group in different discipline= 1.1 . 1.1 	 Tactical blocks= 2 	 Operational blocks= 2 
G_8	Open booking percentage= 0.25 	         Demand size for each group in different discipline= 1.1 . 1.1 	 Tactical blocks= 2 	 Operational blocks= 4
DOI: 10.17632/fw3bcrxknf.1
title: Dataset for "Evolving dunes under flow reversals: from an initial heap toward inverted dune"

description: In this dataset, you can find Matlab files used to read and process .dfm videos. Also, simulation files were provided for a subaqueous barchan dune from a conical pile as an initial condition.
DOI: 10.17632/fw3bcrxknf.2
title: Dataset for "Evolving dunes under flow reversals: from an initial heap toward an inverted dune"

description: In this dataset, you can find Matlab files used to read and process .dfm videos. Also, simulation files were provided for a subaqueous barchan dune from a conical pile as an initial condition.
DOI: 10.17632/ffwgjdfn86.1
title: Road Traversing Knowledge for Quality Classification

description: This dataset consists of 6.297 labeled images from Brazil's inner country roads captured by a low-cost camera (HP Webcam HD-4110) attached to a moving vehicle. The dataset presents three quality levels classes (good, regular, and bad) for each surface kind: asphalt, paved and unpaved. The only exception is the unpaved surface that does not contain the class "good" due to its poor nature. The number of images by surface-quality class is:

asphalt-good: 1978
asphalt-regular: 839
asphalt-bad: 464
paved-good: 1179
paved-regular: 324
paved-bad: 124   
unpaved_bad: 593
unpaved-regular: 796


All images were collected during the daytime in good weather conditions. The frames present lighting variations, shadows, and solar glare. Moreover,  it is also noticed intra-class variations, such as asphalt surfaces with different color intensities, e.g., older asphalts are usually lighter than the new ones; paved surfaces formed by different patterns, e.g., cobblestone and concrete; and unpaved surfaces, which are naturally uneven.

The directory is organized by subdirectories named with the "surface_quality".
DOI: 10.17632/hws49dsfvc.1
title: Performance Data of a SWRO arising from Wave Powered Desalinisation

description: Data Context – The data repository contains the processed datasets generated during the experimental testing of the SWRO membrane using the experimental setup and variables described and presented in this paper. 
Data Creation – The data was created from a series of experiments spanning 2022. The controlled and measured variables include; Feed flow (L/min), Brine flow (L/min)	, Feed pressure (Bar), Brine Pressure (Bar), 	Permeate flow (L/min), Permeate salinity (ppm), Brine salinity (ppm), Temp (degree C), Perecentage recovery (%), Salt rejection (%).
Four Excel Workbooks are provided as per Table 6.
Table 6: Data repository file names and brief description. 
File Name:	Description of Data:
Fig 3 (a) [PNG]	Schematic of the experimental set up
Fig 3 (b) [JPEG]	Physical setup as used in the laboratory
Membrane_integrity [.xlsx]	Three spreadsheets with the SWRO membrane performance during steady operating conditions when new, after sinusoidal experiments and after rectified sinusoidal experiments. 
Rectified_sinusoidal [.xlsx]	One spreadsheet with the SWRO membrane performance for variable rectified sinusoidal periods ranging from 7.5- 30s at feed flows of 6 L/min – 10 L/min. 
Sinusoidal 
[.xlsx]	Three spreadsheets with the SWRO membrane performance at 8, 10 and 12 L/min variable feed flow, oscillating by 1-3 L/min and sinusoidal periods of 7.5s-30s. 
Steady_Flow
[.xlsx]	Three spreadsheets with the SWRO membrane performance during steady operating conditions. Spreadsheets provide all values, constant flow scenarios (feed flow is maintained constant and feed pressure is varied between each test) and constant pressure scenarios (feed pressure is maintained constant and feed flow is varied between experiments. . 

Data Processing – The results presented are processed results and comprise of a time average 5 minute period once stability has been achieved through the system.
DOI: 10.17632/j6657wk3tf.1
title: Data Set of Manual Metal Arc Welded heterogeneous thin steel plates AISI1018 AND AISI4340

description: Advancement in technology demands the joining of heterogeneous metals of low and high-carbon steel grades. In this investigation, AISI1018 low and AIS4340 medium carbon steels were welded to form a heterogeneous thin metal joint using the Manual Metal Arc Welding (MMAW) method. Experimental variation of welding current, electrode position, and weld orientation are selected as the MMAW factors. The trials are planned using the Full Factorial Design (FFD) and the trial results are analysed using Analysis of Variance (ANOVA). A computerised tensile testing machine (TM2101N) was used to test the tensile strength of the welded specimens that were prepared in accordance with the ASTM standard. The prediction model for tensile strength was generated based on regression analysis. The ANOVA and prediction model helped in studying the effect of the MMAW factors.
DOI: 10.17632/grwkzbfb9t.1
title: Accounting Firms Mission, Vision and Core Values Dataset

description: The population of this study includes accounting firms. The sample used in this study comprised 50 international accounting firms and 50 regional accounting firms. Multiple criteria were used to select accounting firms for this study. Firstly, accounting firms must have published their core values publicly.  The core values of the selected 100 accounting firms were extracted from their official websites.
DOI: 10.17632/tgfz6h2bxh.1
title: Data on soil physicochemical factors as well as the structure and function of soil microbial communities.

description: Effects of simulated root secretions addition on the structure and function of soil microbial communities in coffee continuous planting soil.
DOI: 10.5281/zenodo.8122093
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Velva Elaine Rudd, http://www.wikidata.org/entity/Q6160123. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q6160123
DOI: 10.5281/zenodo.8122092
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Velva Elaine Rudd, http://www.wikidata.org/entity/Q6160123. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q6160123
DOI: 10.5281/zenodo.8122065
title: Chloroplast and mitochondrial genomes of Ulva mutabilis

description: Annotated chloroplast and mitochondrial genomes of Ulva mutabilis (wild-type). Genbank format.
description: {"references": ["De Clerck et al. (2018) https://doi.org/10.1016/j.cub.2018.08.015"]}
DOI: 10.5281/zenodo.8122064
title: Chloroplast and mitochondrial genomes of Ulva mutabilis

description: Annotated chloroplast and mitochondrial genomes of Ulva mutabilis (wild-type). Genbank format.
description: {"references": ["De Clerck et al. (2018) https://doi.org/10.1016/j.cub.2018.08.015"]}
DOI: 10.17632/4bjhfpvrw4.1
title: Supplementary material: Doppler ultrasound, blood flow, portal vein, milk vein, body condition, dairy cows

description: This dataset contains supplemental material to the research article: "Colour Doppler study of blood flow in the portal vein in relation to blood flow in the milk vein, milk yield and body condition of dairy cows during dry period and lactation".

Dataset contents explained:

1. Portal Vein Blood FLow (.avi) - video of color Doppler examination of blood flow in the portal vein of dairy cows

2. Table 3 (.pdf) - Descriptive statistics of milk vein blood flow volume (MV_BFVol), body condition score (BCS), backfat thickness (BFT) and heart girth (HG) circumference of the dairy cows included in the study, stratified by production stage (late lactation, dry period, early lactation).

3. Fig. 6 (.tiff) - Cross Sectional Area (CSA) of the portal vein: Changes throughout the study period: starting from 7 days before the dry period (“DP”), throughout the DP, 7 and 3 days prior to calving, at calving (“C”) and from the 3rd to the 90th day in the new lactation (“DIM”: days in milk). 

4. Fig. 7 (.tiff) - Diameter of the Portal Vein: Changes throughout the study period: starting from 7 days before the dry period (“DP”), throughout the DP, 7 and 3 days prior to calving, at calving (“C”) and from the 3rd to the 90th day in the new lactation (“DIM”: days in milk). 

5. Fig. 8 (.tiff) - Distance of the Portal Vein from skin surface: Changes throughout the study period: starting from 7 days before the dry period (“DP”), throughout the DP, 7 and 3 days prior to calving, at calving (“C”) and from the 3rd to the 90th day in the new lactation (“DIM”: days in milk). 

6. BFT example (.bmp) - B-mode ultrasound measurement of backfat thickness of a dairy cow (according to Schroeder and Staufenbiel, 2006)
DOI: 10.17632/4bjhfpvrw4.2
title: Supplementary material: Doppler ultrasound, blood flow, portal vein, milk vein, body condition, dairy cows

description: This dataset contains supplemental material to the research article: "Colour Doppler study of blood flow in the portal vein in relation to blood flow in the milk vein, milk yield and body condition of dairy cows during dry period and lactation".

Dataset contents explained:

1. Portal Vein Blood FLow (.avi) - video of color Doppler examination of blood flow in the portal vein of dairy cows

2. Table 3 (.pdf) - Descriptive statistics of milk vein blood flow volume (MV_BFVol), body condition score (BCS), backfat thickness (BFT) and heart girth (HG) circumference of the dairy cows included in the study, stratified by production stage (late lactation, dry period, early lactation).

3. Tables 4, 5, 6 (.pdf): Ingredients, chemical composition and nutritive value of the total mixed rations provided for the: High Lactation, Low Lactation, and Close-Up (dry period) groups.

4. Fig. 6 (.tiff) - Cross Sectional Area (CSA) of the portal vein: Changes throughout the study period: starting from 7 days before the dry period (“DP”), throughout the DP, 7 and 3 days prior to calving, at calving (“C”) and from the 3rd to the 90th day in the new lactation (“DIM”: days in milk). 

5. Fig. 7 (.tiff) - Diameter of the Portal Vein: Changes throughout the study period: starting from 7 days before the dry period (“DP”), throughout the DP, 7 and 3 days prior to calving, at calving (“C”) and from the 3rd to the 90th day in the new lactation (“DIM”: days in milk). 

6. Fig. 8 (.tiff) - Distance of the Portal Vein from skin surface: Changes throughout the study period: starting from 7 days before the dry period (“DP”), throughout the DP, 7 and 3 days prior to calving, at calving (“C”) and from the 3rd to the 90th day in the new lactation (“DIM”: days in milk). 

7. BFT example (.bmp) - B-mode ultrasound measurement of backfat thickness of a dairy cow (according to Schroeder and Staufenbiel, 2006)