cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/bk45c9yxb9.1
title: CRICVA Database

description: The CRICVA dataset contains eye tracking data for a subset of 232 digitized Pap smear images of the CRIC Center (https://sites.google.com/view/centercric). The CRIC images were acquired with a Carl Zeiss microscope equipped with a Zeiss AxionCam MRc camera at 40x magnification. The images have 0.255 μm/pixel and resolution of 1392 x 1040 pixels (8-bit). The specimens were prepared via conventional Pap smears and contain cervical cells as well as other artifacts often collected as part of the exams. All cervical cell images were collected from SUS (Brazilian Universal Health System). ---- Detailed Information 1. Pap smear images The CRIC images were presented to cytopathologists at a resolution of 1280x1024 pixels, keeping the original aspect ratio by adding white pixel lines at the image bottom. Notice that the added white pixel lines have been removed from the attached images. 2. Eye tracking device We used an EyeLink 1000 system designed by SR Research Ltd., Mississauga, Canada, with a sampling rate of 1000 Hz on the right eye recording to register the visual attention of cytopathologists. 3. Subjects CRICVA has eye fixation maps from three cytopathologists, all of them with normal or corrected-to-normal vision via lens glasses. The cytopathologists have experience with cervical care and reading conventional Pap smear slides on clinical laboratory. 4. Methodology The attention data was collected by a task-driven experiment in which the expert interpreted each cervical cell image and marked the abnormal cells with mouse clicks. The participants had free time to analyze the images. ---- Database Organization CRICVA comprises samples for normal (Negative) and abnormal (ASC-US: Atypical Squamous Cells of Undermined Significance; ASC-H: Atypical Squamous Cells of High Significance; LSIL: Low-grade Squamous Intraepithelial Lesion; HSIL: High-grade Squamous Intraepithelial Lesion; and CA: Carcinoma) cases. Our dataset contains eight folders each being relative to a trial. In each folder, there are the cervical cell images (sub-folder: images), the eye fixations (sub-folder: fixation_locs), and the attention maps (sub-folder: fixation_maps). For each trial, we also provide a text file (labels_trial_xx.txt) with the following information: image_id, image_name, image_class For example, 1,011fda505d7e4af4b8cc57545343624d,ASC-US 2,02c7fb946ad5c5e5f9c1e1178c21fc92,ca More information can be found in the references (related links) below. ---- Special Thanks: Dr. José Soares de Andrade Júnior and Dr. Humberto de Andrade Carmona for authorizing the use of the eye tracking device at the Complex Systems Laboratory, Department of Physics, Universidade Federal do Ceará, Fortaleza, CE, Brazil. ---- Feedback on the dataset is welcome. The person to contact is Daniel Ferreira (daniels@ifce.edu.br)
DOI: 10.17632/gg9vykbvgn.1
title: book files "Metodologías de cálculo actuarial a través de excel"

description: book files "Metodologías de cálculo actuarial a través de excel"
DOI: 10.17632/nj4tdzrdt3.2
title: Load-driven interactions between energy efficiency and demand response on regional grid scales: Data inputs

description: This repository contains building and system-level load data used as input to the
modeling presented in the paper "Load-driven interactions between energy efficiency 
and demand response on regional grid scales" by Gerke et al., published in
Advances in Applied Energy in 2022.

There are three subdirectories containing data corresponding to one of the
grid regions analyzed in the paper, namely California (CAMX), Texas (ERCT), and 
New England (NENG). Each region's subdirectory contains data subdirectories
containing the building and system-level load shape data for each of several
energy efficiency (EE) scenarios considered in the paper.
DOI: 10.17632/yzjw2stk7f.1
title: Population dynamics of normal human blood inferred from spontaneous somatic mutations - substitution calls

description: These are the substitution calls to accompany the article "Population dynamics of normal human blood inferred from spontaneous somatic mutations". This article has the following authors and affiliations: Henry Lee-Six1, Nina Friesgaard Øbro2, Mairi S. Shepherd2, Sebastian Grossmann1, Kevin Dawson1, Miriam Belmonte2, Robert J. Osborne1, Brian J. P. Huntly2, Inigo Martincorena1, Elizabeth Anderson1, Laura O’Neill1, Michael R. Stratton1, Elisa Laurenti2, Anthony R. Green2§, David G. Kent2§, Peter J. Campbell1§ § Joint corresponding authors 1Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK. 2Wellcome-MRC Cambridge Stem Cell Institute and Department of Haematology, University of Cambridge, Cambridge CB2 2XY, UK. Address for correspondence: Dr Peter J. Campbell, Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton CB10 1SA, United Kingdom. Telephone: +44 (0) 1223 834244. e-mail: pc8@sanger.ac.uk Dr David G. Kent, WT/MRC Cambridge Stem Cell Institute, University of Cambridge, Hills Rd, Cambridge CB2 0AH, United Kingdom. Tel: +44 (0) 1223 762130. e-mail: dgk23@cam.ac.uk Prof. Tony R. Green, Cambridge Institute of Medical Research, University of Cambridge, Hills Rd, Cambridge CB2 0XY, United Kingdom. Tel: +44 (0) 1223 336820. e-mail: arg1000@cam.ac.uk
DOI: 10.17632/749k3cwbnm.2
title: Dataset for Autism Diagnosis Based on DSM-5

description: Autism is a neurodevelopmental condition affecting individuals from childhood up to adulthood. Individuals living with Autism are said to be having challenges with cognitive, social, and communication skills. According to the DSM5 criteria, there are 3 levels of Autism: Level 1, Level 2, and Level 3. Knowing the severity level of an individual after diagnosis helps in providing the right therapy for the individual. Most research work has focused on the diagnosis of autism, as in discriminating autistic individuals from non-autistic individuals. Very few works have been done in classifying autism into its severity levels based on the DSM 5 criteria. In this study, we collected datasets from individuals based on the guidelines of the DSM5 criteria using the google forms questionnaire.
DOI: 10.17632/g6m6884znc.1
title: Safety and efficacy of eblasakimab, an IL-13R alpha 1 monoclonal antibody, in adults with moderate-to-severe atopic dermatitis: a phase 1b, multiple-ascending dose study. Supplementary information.

description: This dataset contains figures, tables, and documents referenced in: Safety and efficacy of eblasakimab, an IL-13R alpha 1 monoclonal antibody, in adults with moderate-to-severe atopic dermatitis: a phase 1b, multiple-ascending dose study. (Blauvelt et al.)
DOI: 10.17632/4mfgjpvvjm.1
title: A study of SMC TGFb signaling in hypercholesterolemic patients with aortic aneurysms Chen et al. RElated To fig 1.

description: Smooth muscle cell (SMC) TGFb signaling and expression of relevant genes in the media of normal human aortas and non-Marfan atherosclerotic ascending aorta aneurysms by bulk RNAseq and immunocytochemistry. Related To Fig 1. SMC aneurysms samples show a reduction in TGFb signaling and increased expression of inflammation-related genes compared to normal aortas SMCs.
DOI: 10.17632/ztyk4h3v6s.2
title: Composed Encrypted Malicious Traffic Dataset for machine learning based encrypted malicious traffic analysis.

description: This is a traffic dataset which contains balance size of encrypted malicious and legitimate traffic for encrypted malicious traffic detection. The dataset is a secondary csv feature data which is composed of five public traffic datasets. 
Our dataset is composed based on three criteria: The first criterion is to combine widely considered public datasets which contain both encrypted malicious and legitimate traffic in existing works, such as the Malwares Capture Facility Project dataset and the CICIDS-2017 dataset. The second criterion is to ensure the data balance, i.e., balance of malicious and legitimate network traffic and similar size of network traffic contributed by each individual dataset. Thus, approximate proportions of malicious and legitimate traffic from each selected public dataset are extracted by using random sampling. We also ensured that there will be no traffic size from one selected public dataset that is much larger than other selected public datasets. The third criterion is that our dataset includes both conventional devices' and IoT devices' encrypted malicious and legitimate traffic, as these devices are increasingly being deployed and are working in the same environments such as offices, homes, and other smart city settings.

Based on the criteria, 5 public datasets are selected. After data pre-processing, details of each selected public dataset and the final composed dataset are shown in “Dataset Statistic Analysis Document”. The document summarized the malicious and legitimate traffic size we selected from each selected public dataset, proportions of selected traffic size from each selected public dataset with respect to the total traffic size of the composed dataset (% w.r.t the composed dataset), proportions of selected encrypted traffic size from each selected public dataset (% of selected public dataset), and total traffic size of the composed dataset. From the table, we are able to observe that each public dataset equally contributes to approximately 20% of the composed dataset, except for CICDS-2012 (due to its limited number of encrypted malicious traffic). This achieves a balance across individual datasets and reduces bias towards traffic belonging to any dataset during learning. We can also observe that the size of malicious and legitimate traffic are almost the same, thus achieving class balance.
The datasets now made available were prepared aiming at encrypted malicious traffic detection.
Since the dataset is used for machine learning model training, a sample of train and test sets are also provided. The train and test datasets are separated based on 1:4 and stratification is applied during data split. Such datasets can be used directly for machine or deep learning model training based on selected features.
DOI: 10.17632/dfz8ss6m7h.1
title:  Agricultural carbon emissions 

description:  We believe that low-carbon agricultural development is an effective way to achieve high-quality agricultural development in the context of rural revitalization.It has been a great significance to effectively formulate high-quality of the agricultural development strategies by scientific measurement of agricultural carbon emission and the study of its formation machanism.
DOI: 10.17632/wsxphrmydn.1
title: Design files for A low-cost experimental setup to perform impact tests on a dummy head

description: Design files for A low-cost experimental setup to perform impact tests on a dummy head
DOI: 10.17632/y9g8dn7kww.1
title: Effect of Dialogue Journal Writing on EFL Students' Speaking Skill

description: This study investigated the effect of dialogue journal writing on English-as-a-Foreign-Language (EFL) students' speaking skills.
DOI: 10.17632/62f7rfczfw.1
title: Data for: Geospatial and techno-economic analysis of wind and solar resources in India

description: These data were created as part of the publication: Deshmukh R, Wu G C, Callaway D, Phadke A. (2018) "Geospatial and techno-economic analysis of wind and solar resources in India". The data set includes 3 shape files for wind, solar photovoltaic, and concentrated solar power technologies. The shape files consist of potential project opportunity areas (POAs) for each of the technologies. For each POA, which is 5 x 5 km2 or smaller in size, we calculated various attributes including mean renewable resource quality, levelized cost of generation based on that resource quality, levelized cost for transmission connection to the nearest substation, levelized cost for road connection, distances to nearest substation, road, load center, and water body, water stress (important consideration for solar resources), and electricity generation and installed capacity potential for that land parcel. The description of the attributes in the shape files is included in a pdf file. The abstract for the paper, which summarizes our findings is as follows: Using geospatial and economic analysis, we identify abundant renewable resources in India - 850-3,400 GW for onshore wind, 1,300-5,200 GW for utility-scale solar photovoltaic (PV), 160-620 GW for concentrated solar power (CSP, with 6h-storage). However, these resources are concentrated in the western and southern regions. Deriving capital costs from India's 2017-18 auction prices, we estimate the 5th and 95th percentiles of levelized costs of energy generation ranging from USD 47-52 per MWh for solar PV and USD 42-62 per MWh for wind. Karnataka, Maharashtra, Tamil Nadu, and Telangana are the best states for access to high-voltage substations, but transmission investments in Gujarat, Rajasthan, Andhra Pradesh, and Madhya Pradesh are needed to harness signicant renewable resources. More than 80% of wind resources lie on agricultural lands where dual land use strategies could encourage wind development and avoid loss of agriculturally productive land. Approximately 90% of CSP resources and 80% of solar PV resources are in areas experiencing high water stress, which can severely restrict deployment unless water requirements are minimized. Finally, we find co-location potential of at least 110 GW of wind and 360 GW of solar PV, which together could meet 35% of electricity demand in 2030.
DOI: 10.17632/b5gdgtgttb.1
title: Environmental, Social, and Corporate Governance (ESG)

description: ESG investing for layman means investing in those Organizations in which all the stakeholders understand the impact of their business activities on the Society and Environment at large and make systematic business effort to mitigate the negative impact of their business activity as much as possible and while also disclosing these efforts to the public so that investor who want to channelize their investments into sustainable goals can use this information.
DOI: 10.17632/22d2kcr2yp.1
title: Multi-Diseases Dataset Composed of Medical Sensors and Text inputs: Simulated Dataset Accompanied with Codes

description: This dataset package presents a simulated dataset for triaging and prioritizing patients to multi emergency levels. Four types of data are presented which are: (ECG, blood pressure, and SpO2 signals) and the fourth is text-inputs. For the ECG, blood pressure, and SpO2 signals, the online library Physionet [1], which is considered the most reliable and relevant library in healthcare services and bioinformatics sciences, was used. The library contains collections of databases, and signals related to ECG, blood pressure, and SpO2 sensor. However, Simulated data accompanied by codes are presented. The contributions of our presented dataset are (1) The presented dataset is considered as vital features extracted from the signal records. The dataset includes medical vital features: (QRS width, ST Elevation, Peaks number and Cycle interval from ECG signal, SpO2 level from SpO2 signal and High Blood (systolic) Pressure value and Low-Pressure (diastolic) value from Blood Pressure signal). Those features were extracted based on our machine learning algorithms. In addition to that, new medical features were added based on medical doctors' recommendations as text-inputs: Chest pain, Shortness of Breath, Palpitation, and whether the patient at rest or not. All these features considered significant symptoms for many diseases such as Heart attack or stroke, Sleep apnea, Heart failure, arrhythmia, and blood pressure chronic diseases. Therefore, (2) The formulated dataset is considered in the doctor diagnostic procedures for identifying the patients' emergency level. (3) in the online library [1], the ECG, blood pressure, and SpO2 were represented as signals. In contrast, we achieved the signal processing tasks and we re-present the dataset by numeric values for the vital features in Excel sheet representations. Moreover (4) based on our simulation outcomes, the dataset is re-organized and re-formatted in structure dataset format. The dataset is represented in terms of tables to illustrate the patient's profile and type of diseases. (5) The presented dataset is utilized in the evaluation of medical monitoring and healthcare provisioning systems [2], and finally (6) the Simulation codes for feature extractions functions are presented.
DOI: 10.17632/nkcdcmxjn6.1
title: Data for: Giving kids a head start: The impact and mechanisms of early commitment of financial aid on poor students in rural China

description: Abstract of associated article: We estimate the impact of two early commitment of financial aid (ECFA) programs—one at the start and one near the end of junior high school (seventh and ninth grades, respectively)—on the outcomes of poor, rural junior high students in China. Our results demonstrate that neither of the ECFA programs has a substantive effect. We find that the ninth-grade program had at most only a small (and likely negligible) effect on matriculation to high school. The seventh-grade program had no effect on either dropout rates during junior high school or on educational performance as measured by a standardized math test. The seventh-grade program did increase the plans of students to attend high school by 15%. In examining why ECFA was not able to motivate significant behavioral changes for ninth graders, we argue that the competitiveness of the education system successfully screened out poorer performing students and promoted better performing students. Thus by the ninth grade, the remaining students were already committed to going to high school regardless of ECFA support. In regards to the results of the seventh grade program, we show how seventh graders appear to be engaged in wishful thinking (they appear to change plans without reference to whether their plans are realistic).
DOI: 10.17632/6gj7yts74v.1
title: Green Human Resource Management Research Dataset (2008-2019)

description: The Green Human Resource Management research & publication dataset, which was indexed by Scopus from 2008 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.
DOI: 10.17632/6cv77bry6m.1
title: The genomic and immune landscapes of lethal metastatic breast cancer

description: Supplemental information for: The genomic and immune landscapes of lethal metastatic breast cancer
DOI: 10.17632/jctsfj2sfn.1
title: COVID19, Pneumonia and Normal Chest X-ray PA Dataset

description: The dataset is organized into 3 folders (covid, pneumonia , normal) and metadata.csv which contain chest X-ray posteroanterior (PA) images. X-ray samples of COVID-19 were retrieved from different sources for the unavailability of a large specific dataset. First, 613 X-ray images of COVID-19 cases were collected from the following websites: GitHub [1,2], Radiopaedia [3], The Cancer Imaging Archive (TCIA) [4], and the Italian Society of Radiology (SIRM) [5]. Then, instead of data being independently augmented, a dataset containing 912 already augmented images was collected from Mendeley [6]. Finally, 1525 images of pneumonia cases and 1525 X-ray images of normal cases were collected from the Kaggle repository [7] and NIH dataset [8]. A total of 4575 samples were used in the experiment, where 1525 samples were used for each case. References: [1] http://arxiv.org/abs/2003.11597 [2] https://github.com/agchung [3] https://radiopaedia.org/ [4] https://www.cancerimagingarchive.net/ [5] https://www.sirm.org/en/category/articles/covid-19-database/ [6] https://data.mendeley.com/datasets/2fxz4px6d8/4 [7] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia [8] https://www.kaggle.com/nih-chest-xrays/data
DOI: 10.17632/nndf9yy426.2
title: Investor Sentiment Index Data

description: Updated investor sentiment index dataset up to December 2014 (including both Baker and Wurgler's sentiment index, and Huang, Jiang, Tu and Zhou (2015 RFS)'s investor sentiment index)
DOI: 10.17632/4r3cvfk6g4.1
title: Iris Dataset

description: This contains 150 points in 4 dimensional data for 3 classes.
DOI: 10.17632/c7dwrsd6z7.1
title: EGG

description: Certainly! Here's a data description you can use for your project:

---

*Data Description: Classification of Good and Bad Eggs (Ovum)*

This dataset consists of over 500 images capturing both good and bad eggs (ovum) under controlled conditions. The images were acquired using a Realme Narzo 20 Pro mobile camera, ensuring consistency in quality and resolution. Each image is taken against a black background to enhance contrast and improve the visibility of egg features, with natural daylight providing consistent and neutral lighting conditions.

*1. Data Acquisition:*
- *Device Used:* Realme Narzo 20 Pro mobile camera.
- *Resolution:* High-resolution images, typically around 48 MP, ensuring that fine details of the eggs are visible for precise classification.
- *Background:* Black background was chosen to create a sharp contrast between the eggs and the background, minimizing distractions and helping in accurate segmentation during analysis.
- *Lighting Condition:* Images were captured under daylight conditions, which provides natural and balanced lighting, reducing the chances of shadows or artificial lighting effects that could interfere with the classification process.

*2. Dataset Composition:*
- *Total Images:* The dataset comprises over 500 images, with a balanced number of good and bad eggs.
- *Classes:*
 - *Good Eggs:* These are ova that meet the criteria for being viable, healthy, and ready for further biological processes. Characteristics such as shape, color, texture, and overall integrity are typically used for classification.
 - *Bad Eggs:* These ova are deemed non-viable due to factors such as deformities, discoloration, irregular shapes, or damage. These characteristics make them unsuitable for further biological processes.

*3. Image Characteristics:*
- *Image Dimensions:* Images maintain high resolution, ensuring that subtle differences between good and bad eggs can be observed. 
- *Focus and Clarity:* The use of a mobile camera with advanced AI enhancements ensures that images are in focus, capturing the eggs' surface texture and features clearly.
- *File Format:* The images are stored in standard formats like JPEG or PNG, which are compatible with most image processing tools and machine learning frameworks.

*4. Purpose and Application:*
This dataset is intended for use in the classification and analysis of egg viability. It can be applied in machine learning algorithms aimed at distinguishing between good and bad eggs based on visual features. The high-quality images allow for detailed feature extraction, aiding in the development of robust models for automated classification.

This description should comprehensively cover the key aspects of your dataset while staying within the 3000-character limit. Let me know if you need any modifications or additional details!
DOI: 10.17632/fjggkmwg89.1
title: Development of Multilengual Dataset

description: Customers give reviews on mobile operators as their experiences depend on factors such as network coverage, call quality, data speed, customer service and pricing. Different regions and countries also have different mobile operators and the quality of service can vary between urban and rural areas. In Bangladesh, there are different mobile operators and they have Facebook pages where their users comment their feedbacks. Customers give reviews on mobile operators as their experiences depend on factors such as network coverage, call quality, data speed, customer service and pricing. Different regions and countries also have different mobile operators and the quality of service can vary between urban and rural areas. In Bangladesh, there are different mobile operators and they have Facebook pages where their users comment their feedbacks.