cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/7fmswnmyft.1
title: differentiated instruction for EBN

description: To apply the differentiated instruction theory to design the undergraduate EBN course and to evaluate effects of differentiated instruction on students’ learning outcomes,  classroom engagement, attitudes toward EBN, and learning satisfaction.
DOI: 10.17632/krjgrrdsyr.1
title: Pedoman Praktikum Penyehatan Udara

description: Politeknik Kesehatan Jakarta II Jurusan Kesehatan Lingkungan
DOI: 10.17632/9thyzzwd5d.2
title: Tree Segmentation and Change Detection of Large Urban Areas Based on Airborne LiDAR: Datasets and Supplementary Materials

description: Dataset of sample and reference territories for the article entitled "Tree segmentation and change detection of large urban areas based on airborne LiDAR".
DOI: 10.15468/dl.4fnujq
title: Occurrence Download

description: A dataset containing 342 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-7.84167 -32.66295,66.84167 -32.66295,66.84167 19.66295,-7.84167 19.66295,-7.84167 -32.66295))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Oldeania Stapleton"
 ]
}

 The dataset includes 342 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009371-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/9zxht4rchk.1
title: MacFawn et al 2024. Cancer Cell. The activity of tertiary lymphoid structures in high grade serous ovarian cancer is governed by site, stroma, and cellular interactions

description: Referenced data from manuscript and Key Resource Table.
DOI: 10.17632/v38wjmz6f6.1
title: A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments

description: The dataset of Wi-Fi signals was captured in three indoor environments. 30 subjects participated in the data collection process. In each of the environments, the subjects were instructed to perform a set of pre-explained experiments between two devices, each of them has Intel 5300 network interface card installed. On the transmitting device one transmitting antenna was used, while on the receiver, the transmitted signals were captured by three antennas. The designed experiments were described comprehensively to each of the participating subjects before the start of the experiments. For the first and second environments, a LOS architecture was used while a NLOS architecture was used in the third environment. The Intel 5300 network interface card was configured to operate within the 2.4 GHz frequency band, wireless channel number 3, channel bandwidth of 20 MHz, and 320 packets/second sampling rate. The data collection process took place in three environments. For each of the environments, 10 subjects performed 5 different experiments. Each subject performed 20 trials for each of the experiments. The exchanged Wi-Fi signals were recorded and the RSSI value and the CSI values were recorded.
DOI: 10.17632/zkpwhrrfng.1
title: A C++ code for simulations of RHEED intensity oscillations within the kinematical approximation

description: This paper proposes a new software implementation for the rapid development and deployment of computer simulations of reflection high-energy electron diffraction (RHEED) intensity oscillations during the growth of thin epitaxial films. The calculations are based on the kinematical approximation. In this model the RHEED intensity oscillations from growing layers is considered as an effect of changes of surface roughness.

The previous version of this program (ADVL) may be found at https://doi.org/10.1016/j.cpc.2005.04.005.
DOI: 10.17632/vhmvfbgvxj.1
title: Wild Animal Facing Extinction

description: Wild animal poaching is quite severe, especially in Africa, where South Africa occupies 82% of the world’s rhino community. Africa is poised to be leading in wildlife and poaching badly influences the continent tourism business, as it adds to the extinction of wild animals. Governments and numerous Non-Government Organizations (NGOs) are spending a lot of money and time protecting wild animals from poaching through various methods, such as upgrading fencing systems and employing video surveillance and monitoring systems.  

The deep-learning-based  Computer Vision (CV) solutions depend severely on the large volume of annotated image data. In computer vision-based conservation studies, having a wild animal dataset is an additional advantage for making informed dynamic decisions. Traditionally, most datasets are built through manual annotation, where labelImg is used to draw boundary boxes. However, manual image annotation is very time-consuming, particularly when images are countless and that ends up becoming an expensive task because such assignments will need to be outsourced.

We investigate a framework for semi-auto annotation based on boundary box labels but not tool development. We contribute by manually annotating a small dataset and training a model and semiauto annotating a large scale dataset using the trained model and later correct the miss-classified objects. Furthermore, we contribute a framework that minimizes annotation time and can be used in any dataset construction. Our wild animal dataset is a contribution to conservationists and wild animal literature, as wild animal research faces limitations in computer vision. 

In initializing the process, the images are collected through a search of words from both Google and Baidu search engines manually for six classes; during the manual collection, we carefully do that through web-scraping, which helps in avoiding wrongly categorized images and avoid saving similar images. A small set of images is then randomly chosen for annotation and trained through YOLOv5 to produce a customized model whose weights will suggest new boundary boxes for the remaining large set of images that were not annotated when both weights and a large set of images are fed to labelGo, to complete the process automatically. Human gets involved by correcting the boundary boxes that have class errors in correcting the misclassification of objects, and after the corrected images are saved to the folder that has images that were labeled by labelGo, in complementing semi-auto annotation subsequently the approach minimizes the time-consuming during manual annotation. 

Wild animals 	Number of images
Rhino	         1434
Cheetah	         1183
Elephant          1218
Lion	                1305
Zebra	        1377
Giraffe	        1232

Set	                 Number of Images
Train	             6505
Test	                       621
Validation	       623
Total	             7749

Image = .jpg  & Annotation = .txt
DOI: 10.17632/6hzrf5ktd7.1
title: Data for: The turn towards ICTs for women’s safety in public transport: A review and research agenda

description: Dataset of research articles collected for a literature review on the use of ICTs for women's safety in public transport.
DOI: 10.17632/p74pfhz3yx.1
title: Amharic Social Media Dataset for Hate Speech Detection and Classification in Amharic Text  with Deep Learning

description: This dataset is prepared for hate speech detection and classification into four categories of speech. Namely, Normal speech, Racial Hate speech, Religious Hate speech, Gender Hate speech and Disability Hate speech. This dataset is collected from three social media sites: Facebook, Twitter, and YouTube. The collection is done automatically and the data is annotated by human annotators. The dataset is collected only for Amharic Language. 
To make a clear annotation process we have developed and prepared an annotation guideline. We have made the annotation process a twofold round. The first round annotation is done by 100 annotators who have different demographic and sociocultural backgrounds. Before the annotation process is started, besides giving the developed guideline, a brief introduction is given to the annotators which includes:
● What hate speech is
● Social media and hate speech
● Impact of hate speech
● Types of hate speech
● How to control hate speech and also
● How to use the annotation website system to annotate the hate speech dataset
To start the annotation, process the annotators have to sign up and login to our custom built annotators tool (https://annotate.shegerapps.com) called “Amharic Hate Speech Annotation Tool”. As the schema shows in Figure 5.2 the annotation tool which is the website has a database with ten tables in it, eight of the tables hold an annotated or labeled dataset. The rest two tables are to hold users (annotators, curators, and admin) for authentication purposes, and finally, the tenth table holds the raw data. Raw data table is a container where the to be annotated dataset is dumped then the annotators fetch the data from this raw data table, when data is annotated it is inserted into the respective eight tables. On this annotation part, we have annotated texts in eight categories but for this research, we need only the four categories. We included the other four hate speech categories for future studies so any interested researcher or ourselves can continue researching without the need for a new annotation. This annotation tool database is MySQL, the backend is developed using PHP and the frontend is done using HTML, JavaScript, and jQuery.
Some of the advantages of this annotation tool are to create an efficient team-based annotation experience, it maintains control for data preparation, it is used to manage annotators’ tasks and their progress, and also it makes exporting the annotated dataset easier.
After finalizing the annotation, the dataset is given to the respective model as input in CSV format. During training time this data is split into three with an 80:10:10 ratio for training, validation, and testing purposes
DOI: 10.17632/s5sfnk28gw.1
title: Primary and Tabular Data Set for the article Entitled "Social Media and Cyber Crime: A Socio-Demographic Study of Awareness Level among Indian Youth"

description: Primary and Tabular Data Set for the article Entitled "Social Media and Cyber Crime: A Socio-Demographic Study of Awareness Level among Indian Youth"
DOI: 10.17632/zf8wmrcc5v.1
title: Socio-demographic determinants of good food safety practices for young educated food handlers in Turkey

description: Dear Editors, Reviewers and Readers, 

In the attached SPSS data file;
The frequency test was used to find correct (3), incorrect (1), and not sure (2) percentages of the statements and demonstrated in Table 2. 
"Practice_score_good" dataset was formed using Count Values within Cases in Data menu of SPSS by counting all score “3”. “Practice_score_good_rate” was obtained by dividing "Practice_score_good" by 19 (statement number). Then, “Practice_score_good_poor” was created using “Practice_score_good_rate”. Accordingly, if the rate in “Practice_score_good_rate” was 60% and more, “1” was assigned to “Practice_score_good_poor” column, otherwise “0” was written. 
Cross tabulation was performed to count Score “1” (it means good food safety practice level) of “Practice_score_good_poor” in the socio-demographic groups. The score “1” percentages were presented in Table 3. 
"Practice_score_good_poor" dataset was used for analysis of variance (t test and ANOVA) for comparison according to socio-demographic groups (statistical lettering in the first column of Table 3). 
Binary logistic regression analysis was performed to find the relationship between "Practice_good_poor_coded" and the socio-demographic groups such as "gender", "age", "monthly income", “marital status”, "grade level", and "internship status" (categorical variables) datasets with selected "first" as a reference category. The results were summarized in Table 3. 
Using "Practice_score_good"; mean, standard deviations were calculated and presented as “Mean score ± SD” (not presented in Tables, only placed in-text). 

Kind Regards.
DOI: 10.17632/475kmt7ysv.1
title: Data for: Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data

description: A national product of Surface Water Dynamics in France (SWDF) on a monthly temporal scale and 10-m spatial scale is generated using an automatic rule-based superpixel (RBSP) approach. The datasets include the monthly, seasonal, annual (permanent and maximum extents) and annual frequency maps of surface water bodies in France using Sentinel-2 imagery. Also, the datasets provide our validation data (200 sampling points and two local lakes). The detailed description and methodology can be found in the linked manuscript and its supplemental materials. If you have any additional questions, please contact xiuchengyang@163.com.
DOI: 10.17632/pdnjz7zz5x.1
title: Data for: Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process

description: This data was generated using an advanced mathematical simulation of a 100,000 litre penicillin fermentation system referenced as IndPenSim. All details describing the simulation are available on the following website: www.industrialpenicillinsimulation.com. IndPenSim is the first simulation to include a realistic simulated Raman spectroscopy device for the purpose of developing, evaluating and implementation of advanced and innovative control solutions applicable to biotechnology facilities. This data set generated by IndPenSim represents the biggest data set available for advanced data analytics and contains 100 batches with all available process and Raman spectroscopy measurements (~2.5 GB). This data is highly suitable for the development of big data analytics, machine learning (ML) or artificial intelligence (AI) algorithms applicable to the biopharmaceutical industry. The 100 batches are controlled using different control strategies and different batch lengths representing a typical Biopharmaceutical manufacturing facility: Batches 1-30: Controlled by recipe driven approach Batches 31-60: Controlled by operators Batches 61:90: Controlled by an Advanced Process Control (APC) solution using the Raman spectroscopy Batches 91:100: Contain faults resulting in process deviations. Please reference: Goldrick S., Stefan, A., Lovett D., Montague G., Lennox B. (2015) The development of an industrial-scale fed-batch fermentation simulation Journal of Biotechnology, 193:70-82. and Goldrick S., Duran-Villalobos C., K. Jankauskas, Lovett D., Farid S. S, Lennox B., (2019) Modern day control challenges for industrial-scale fermentation processes. Computers and Chemical Engineering. Additionally help publicise this work on the following platforms: Twitter: @Stephen_Goldric Github: StephenGoldie LinkenIn: Stephen Goldrick - Post Doc @UCL Biochemical Socitey
DOI: 10.17632/fmbtg28frm.1
title: Sediment-water oxygen and nutrient flux data Narragansett Bay

description: These data on oxygen and nutrient fluxes across the sediment-water interface were collected in Narragansett Bay, Rhode Island, USA, 1973-1974, at six stations, using in situ bottom chambers. The purpose was to assess the importance of these fluxes in the nutrient cycles of this estuary. Results showed that the benthic communities play an important role and are tightly coupled with the pelagic communities. Hale, S.S. 1975. The role of benthic communities in the nitrogen and phosphorus cycles of an estuary. In: F.G. Howell, J.B. Gentry, and M.H. Smith (eds.) Mineral Cycling in Southeastern Ecosystems. U. S. Energy Research and Development Administration, ERDA Symposium Series 1975:291–308. Hale, S.S. 1974. The role of benthic communities in the nutrient cycles of Narragansett Bay. MS thesis. Graduate School of Oceanography, University of Rhode Island, Narragansett, Rhode Island. 123 p.
DOI: 10.17632/c5nv9fzn5m.1
title: Function Analysis

description: We discuss various approaches to this subject, including covering lemmas, interpolation theorems, the Fourier transform method, the g-function argument, the Mellin transform technique, and Lie group and representation theoretic arguments. We obtain, in particular, some results for maximal functions associated to distributions on R^n and a generalization of those on compact semisimple Lie groups.
DOI: 10.17632/fsfpdy9t5r.1
title: PYATB: An efficient Python package for electronic structure calculations using ab initio tight-binding model

description: We present PYATB, a Python package designed for computing band structures and related properties of materials using the ab initio tight-binding Hamiltonian. The Hamiltonian is directly obtained after conducting self-consistent calculations with first-principles packages using numerical atomic orbital bases, such as ABACUS. The package comprises three modules: Bands, Geometric, and Optical. In the Bands module, one can calculate essential properties of band structures, including the partial density of states, fat bands, Fermi surfaces, and Weyl/Dirac points. The band unfolding method is utilized to obtain the energy band spectra of a supercell by projecting the electronic structure of the supercell onto the Brillouin zone of the primitive cell. With the Geometric module, one can compute the Berry phase and Berry curvature-related quantities, such as electric polarization, Wilson loops, Chern numbers, and anomalous Hall conductivities. The Optical module offers a range of optical property calculations, including optical conductivity and nonlinear optical responses, such as shift current and Berry curvature dipole.
DOI: 10.17632/7sgmg89zb6.1
title: Title and Abstract Screening Data for Systematic Review

description: This is a dataset accompanying the study "A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening".
DOI: 10.17632/m3n5sjxtjy.2
title: Dataset for Detection and Segmentation of the Radiographic Features of Pulmonary Edema

description: Objectives: This comprehensive dataset is well suited for training, evaluating, and using machine learning models to detect, segment, and analyze radiological features associated with pulmonary edema in chest X-ray images. Description: This dataset consists of a collection of chest X-rays extracted from the MIMIC database, carefully collected at the Beth Israel Deaconess Medical Center. In total, it comprises 1000 chest X-rays obtained from 741 patients with features suggestive of edema. These X-rays were carefully selected for manual annotation. The annotations are rich and detailed, covering specific radiological features commonly associated with pulmonary edema, including cephalization, Kerley lines, peribronchial cuffing, pleural effusions, bat wings, and infiltrates. Furthermore, each chest radiograph is thoughtfully assigned a severity category, categorizing it as "no edema", "vascular congestion", "interstitial edema", or "alveolar edema". Annotation Method: The annotation process was meticulously performed by a highly qualified clinician with over 10 years of radiology experience, utilizing both frontal and lateral views for each chest X-ray study. Cephalization and Kerley lines were delineated using polylines, while other features were delineated using binary masks. This methodological approach was carefully chosen to provide a comprehensive data set that would ensure accuracy in subsequent analyses and label assignments. Features and Severity Labels: The dataset includes a wide variety of radiological features, with a total of 4293 annotations. These include: 1. Cephalization: 1656 annotations 2. Kerley lines: 609 annotations 3. Peribronchial cuffings: 30 annotations 4. Pleural Effusions: 317 annotations 5. Bat wings: 1604 annotations 6. Infiltrate: 77 annotations At the same time, the dataset features a wealth of severity labels, totaling 741 cases, thoughtfully categorized as: 1. No edema: 21 cases 2. Vascular congestion: 74 cases 3. Interstitial edema: 51 cases 4. Alveolar edema: 595 cases Notably, all features are represented as bounding boxes, meticulously defined by their respective upper-left (x1; y1) and lower-right (x2; y2) corners. In addition, selected features are provided with masks encoded in base 64 format. To facilitate seamless decoding, we provide a conversion script called " mask_converter.py" that allows the transformation of encoded masks into a versatile numpy array format. This feature improves the usability of the dataset for precise analysis and deep learning applications. Datasets: 1. Source dataset: Clinician-labeled chest X-rays, consolidated into a single spreadsheet, featuring only frontal-view images. 2. Processed dataset: Emphasizes lung area analysis, excluding extraneous regions unused in clinician decision-making. 3. COCO dataset: Prepared in COCO format, ideal for training and testing, with subsets for each evaluated feature.
DOI: 10.17632/s6tt6j22p9.2
title: Household Water consumption dataset

description: Data is collected from a house.There are two tanks. Sump is underground, from which the water is pumped to the overhead tanks Tank1 and Tank2. An Ultrasonic distance sensor is mounted near the top of each tank that monitors and logs the distance of the water surface from the sensor at fixed intervals. There are 13 sets of data for Tank 1 and 16 sets of data for Tank 2. The description reg. these datasets are given in Tank 1-Data Description.xlsx and Tank 2-Data Description.xlsx, respectively. The data logged is the vertical distance of the water surface in the tank from the sensor's location. The minimum reading of sensor is 19 cm which indicates full tank and the maximum reading is 88cm which indicates an empty tank. So to calculate the percentage of tank that has been filled as of now, we use the formula: percent_full=100-(100*(data-19)/(88-19))) The data logged is the one adjusted with calibration equation. The equation found is as follows: Sensor calibration results: f(x) = p1*x + p2 Coefficients (with 95% confidence bounds): p1 = 1.033 (1.024, 1.042) p2 = 1.187 (0.7033, 1.67) Goodness of fit: SSE: 7.109 R-square: 0.9995 Adjusted R-square: 0.9995 RMSE: 0.4951 Note Reg. consumption: The experimental setup was installed in a house with a Solar water heater (capacity 300 Liters) that gets filled every midnight at 12.00 onwards from Tank 1. Hence, the level changes logged for tank 1 during the hours 00.00 to 03.00 am are high. During the rest of the day, the consumption is that of a regular household. The household has four adult occupants, with three occupants leaving the house by 10.00 am every weekday and returning by 4.00 pm. Tank 1 is used for supplying all household needs. Tank 2 is used only for gardening purposes.
DOI: 10.15468/dl.h655vj
title: Occurrence Download

description: A dataset containing 180 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Oligostachyum Z.P.Wang & G.H.Ye"
 ]
}

 The dataset includes 180 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009372-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/rbf7rvxrm2.1
title: Impact of Information Technology and Industrial Development on Corporate ESG Practices: Evidence from a Pilot Program in China

description: Utilizing an exogenous policy shock, the Pilot Program of the Integration of Informatization and Industrialization (PPII) in China, this paper employs a stacked difference-in-differences method. This method elucidates that IT and industrial development significantly bolsters ESG practices in manufacturing firms. Corporate ESG practices metrics are obtained from the CSI ESG rating system. PPII data are manually compiled from the list of pilot projects published by China’s MIIT. Data pertaining to firm characteristics, corporate governance, and regional-level variables are sourced from the CSMAR. After setting global paths as per the root setting instructions, proceed to run the main.do file following the sequence provided in the project directory. This process will guide the execution through each phase of the analysis.