cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/h3fhg9zr47.1
title: Colorectal Cancer Immune Cell Markers Dataset v1 (CRC-ICM-v1)

description: CRC-ICM-v1 dataset contains clinical data, histopathological and immune cell markers images of 136 CRC patients.
This dataset is a collaboration of different research groups. For more information you can visit this website: https://gi-ribon.com

Colorectal cancer (CRC) is the second most common cause of cancer death in the world, and can be identified by the location of the primary tumor in the large intestine: right and left colon, and rectum. Based on the location, CRC shows differences in chromosomal and molecular characteristics, microbiomes, incidence, pathogenesis, and outcome. It has been shown that tumors on the left and right sides also have different immune landscapes, so the prognosis may be different based on primary tumor locations.
It is widely accepted that the immune components of the tumor microenvironment (TME) play a critical role in tumor development. Accordingly, analyses of the interactive relationships between tumor cells and the immune system components in the TME have received more attention, while routine grading system such as TNM classification does not have the power to predict the clinical outcome of the disease. Therefore, the identification of immune-related markers in TME might help to predict the prognosis and clinical outcome of the disease.
One of the critical regulatory molecules in the TME is immune checkpoints that as the gatekeepers of immune responses regulate the infiltrated immune cell functions. Inhibitory immune checkpoints such as PD-1, Tim3, and LAG3 as the main mechanism of immune suppression in TME overexpressed and result in further development of the tumor.
Therefore, this research aimed at providing a well-organized histopathological microscopy image dataset for best-scoring colorectal cancer. For this purpose, the images have been taken from colon tissues of patients with colorectal cancer stained with specific antibodies for CD3, CD8, CD45RO, PD-1, LAG-3, and Tim3 and separately determined in both invasive margin and center of tumor for each marker. Moreover, the present data set contains comprehensive information on the clinicopathological characteristics of the patients, focusing on the location of the tumor, separating the right and left and the rectum.
DOI: 10.17632/bsxgbt8wnp.2
title: Academic motivation and learning strategies: Dataset in a public university sample

description: The dataset presents scores obtained in each of the items that make up the factors of the instrument. The scores obtained in Motivation scale (Table 3) and Learning Strategies Scales (Table 4). To interpret the obtained points, the statistical mean is used as a comparison, as well as the empirical mean to make the respective inferences according to each factor. 
This data includes information from university students; this can provide a parameter to identify student perception about their own motivation orientation (intrinsic or extrinsic), academic stress, self-efficacy and learning strategies
DOI: 10.17632/h7szncxfdx.2
title: Effects of sodium-glucose cotransporter-2 inhibitors on inflammatory skin diseases in patients with type 2 diabetes

description: Online only Supplementary Information for research letter published in JAAD. "Effects of sodium-glucose cotransporter-2 inhibitors on inflammatory skin diseases in patients with type 2 diabetes."
DOI: 10.17632/g8mrry54j8.1
title: Data for: Online motion accuracy compensation of industrial servomechanisms using machine learning approaches

description: Here are shared the files related to the study presented in the paper:

Title: "Online motion accuracy compensation of industrial servomechanisms using machine learning approaches"
Authors: Pietro Bilancia, Alberto Locatelli, Alessio Tutarini, Mirko Mucciarini, Manuel Iori and Marcello Pellicciari

Two folders are shared:
1) Experimental Data --&gt; contains the results obtained from an extensive experimental campaign carried out on a test rig for industrial servomechanisms. 
- Speed --&gt; {100,200,...,18000] rpm (18 levels)
- Output torque --&gt; {0,100,...,1800} Nm (19 levels)
- Oil temperature --&gt; {25,30,35}°C (3 levels)
2) Machine learning models --&gt; ONNX files saved from Python after training and readily available to be imported and utilized within Programmable Logic Controllers to achieve motion prediction and compensation.
DOI: 10.17632/27y943wbkw.1
title: Station-based peat moisture in Sumatra peatland 

description: Peat moisture is a key hydrological variable of peatland that has a practical value for a wide range of uses. In tropical peatland, the observation-based monitoring remains research challenge due to its remote and harsh environment.  The monthly peat moisture from 21 observation-based monitoring stations is provided for period 2018-2019. The stations are in Sumatra peatland, Indonesia that were maintained by BRG. Information on peat moisture is of importance related to the assessment on the success of restoration projects.
DOI: 10.17632/55623jkwrp.3
title: Data Science, Analytics and Machine Learning with R

description: Datasets of the book.
DOI: 10.17632/t7bfhtzhxg.1
title: FISETIO: A FIne-grained, Structured and Enriched Tourism Dataset for Indoor and Outdoor attractions

description: This data in brief paper introduces our publicly available datasets in the area of tourism demand prediction for future experiments and comparisons. Most previous works in the area of tourism demand forecasting are based on coarse- grained analysis (level of countries or regions) and there are very few works and datasets available for fine-grained tourism analysis as well (level of attractions and points of interest). In this article, we present our fine-grained datasets for two types of attractions – (I) indoor attractions (27 Museums and Galleries in U.K.) and (II) outdoor attractions (76 U.S. National Parks) enriched with official number of visits, social media reviews and environmental data for each of them. In addition, the complete analysis of prediction results, methodology and exploited models, features’ performance analysis, anomalies, etc, are available in our original paper
DOI: 10.17632/3xm88g6m6d.2
title: A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors

description: *In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current worldwide use of smartphones makes particularly easy to get this kind of data from people in a non-intrusive and cheaper way, without the need of other wearables. In this paper, we introduce our orientation-independent, placement-independent and subject-independent human activity recognition dataset. The information in this dataset is the measurements from the accelerometer, gyroscope, magnetometer, and GPS of the smartphone. Additionally, each measure is associated with one of the four possible registered activities: inactive, active, walking and driving. This work also proposes an SVM model to perform some preliminary experiments on the dataset. Considering that this dataset was taken from smartphones in their actual use, unlike other datasets, the development of a good model on such data is an open problem and a challenge for researchers. By doing so, we would be able to close the gap between the model and a real-life application. Data collection was made through an Android app, for each smartphone of every individual from the 19 that got part in the study. The data collected, by focusing such gathering on a real-life environment, does not have a fixed orientation or placement of the smartphone, leaving each individual to use their device as they would for each of the specified actions. The activities performed were four: Inactive: not carrying the mobile phone. For example, the device is on the desk while the individual performs another kind of activities. Active: carrying the mobile phone, moving, but not going to a particular place. In other words, this means that, for example, making dinner, being in a concert, buying groceries or doing the dishes count as "active" activities. Walking: Moving to a specific place. In this case, running or jogging count as a "walking" activity. Driving: Moving in a means of transport powered by an engine. This would include cars, buses, motorbikes, trucks and any similar. The data collected comes from four different sensors: accelerometer, gyroscope, magnetometer and GPS. We saved the data of the accelerometer, the gyroscope and the magnetometer with their tri-axial values. In the case of GPS, we stored the device's increments in latitude, longitude and altitude, as well as the bearing, speed and accuracy of the collected measurements. Note that these measures do not have a fixed frequency, as it is not possible to set it on Android devices. In addition, some of the people who took part in the study did not have all these sensors available on their smartphone, so there are certain sessions that do not have a gyroscope, or neither a gyroscope nor a magnetometer together. Inside each .zip file, there is a README file with details for each part.
DOI: 10.17632/4pk5fwybzd.1
title: Changing nurse practitioner students’ perceptions regarding substance use disorder

description: Nurse practitioners, like other healthcare professionals, are serving an increased number of individuals and families affected by substance use disorder (SUD), in a variety of healthcare settings. There is limited information on the gains in knowledge, attitudes/beliefs, confidence, skills post education on SUD. The presented research data demonstrates significant positive changes (p
DOI: 10.17632/tm2bjzjp5g.1
title: Genetically defined nucleus incertus neurons differ in connectivity and function--Spikol et al.--part 1

description: Please see accompanying paper, "Genetically defined nucleus incertus neurons differ in connectivity and function" for details.
DOI: 10.17632/s6s48n8zkd.1
title: Presence of an undisclosed glucocorticoid in Skin-Cap products distributed in Asia

description: A specified assays for diflorasone-21-propionate by Liquid Chromatography with Tandem Mass Spectrometry (LC-MS-MS), detected 3.53μg/mg of diflorasone-21-propionate in the Skin-Cap creas.
DOI: 10.17632/rc2pphrcjk.1
title: Dataset-covid-academic-performance

description: This work tested the hypothesis that the academic performance of students in a distance learning course in Biological Sciences (PBS-CEDERJ) decreased during the COVID-19 pandemic in disciplines with and without practical activities. Performance was quantified via students' grades in PBS-CEDERJ disciplines in pre-pandemic years (2018 and 2019) and during the pandemic (2020 and 2021) years
DOI: 10.17632/yhg5wk39kf.2
title: Ransomware and user samples for training and validating ML models

description: Ransomware is considered as a significant threat for most enterprises since past few years. In scenarios wherein users can access all files on a shared server, one infected host is capable of locking the access to all shared files. In the article related to this repository, we detect ransomware infection based on file-sharing traffic analysis, even in the case of encrypted traffic. We compare three machine learning models and choose the best for validation. We train and test the detection model using more than 70 ransomware binaries from 26 different families and more than 2500 h of ‘not infected’ traffic from real users. The results reveal that the proposed tool can detect all ransomware binaries, including those not used in the training phase (zero-days). This paper provides a validation of the algorithm by studying the false positive rate and the amount of information from user files that the ransomware could encrypt before being detected.

This dataset directory contains the 'infected' and 'not infected' samples and the models used for each T configuration, each one in a separated folder.

The folders are named NxSy where x is the number of 1-second interval per sample and y the sliding step in seconds.

Each folder (for example N10S10/) contains:
	- tree.py -> Python script with the Tree model.
	- ensemble.json -> JSON file with the information about the Ensemble model.
	- NN_XhiddenLayer.json -> JSON file with the information about the NN model with X hidden layers (1, 2 or 3).
	- N10S10.csv -> All samples used for training each model in this folder. It is in csv format for using in bigML application.
	- zeroDays.csv -> All zero-day samples used for testing each model in this folder. It is in csv format for using in bigML application.
	- userSamples_test -> All samples used for validating each model in this folder. It is in csv format for using in bigML application.
        - userSamples_train -> User samples used for training the models.
        - ransomware_train -> Ransomware samples used for training the models
        - scaler.scaler -> Standard Scaler from python library used for scale the samples.
        - zeroDays_notFiltered -> Folder with the zeroDay samples.

In the case of N30S30 folder, there is an additional folder (SMBv2SMBv3NFS) with the samples extracted from the SMBv2, SMBv3 and NFS traffic traces. There are more binaries than the ones presented in the article, but it is because some of them are not "unseen" binaries (the families are present in the training set).

The files containing samples (NxSy.csv, zeroDays.csv and userSamples_test.csv) are structured as follows:
	- Each line is one sample.
	- Each sample has 3*T features and the label (1 if it is 'infected' sample and 0 if it is not).
	- The features are separated by ',' because it is a csv file.
	- The last column is the label of the sample.

Additionally we have placed two pcap files in root directory. There are the traces used for compare both versions of SMB.
DOI: 10.17632/t9ndx37v5h.1
title: Digital Knee X-ray Images

description: The dataset consists of 1650 digital X-ray images of knee joint which are collected from well reputed hospitals and diagnostic centres. The X-ray images are acquired using PROTEC PRS 500E X-ray machine. Original images are 8-bit grayscale image. Each radiographic knee X-ray image is manually annotated /labelled as per Kellgren and Lawrence grades by 2 medical experts. A novel approach has been developed to automatically extract the Cartilage region (region of interest) based on density of pixels.
DOI: 10.17632/yf439yp445.1
title: Sea stack plots: replacing bar charts with histograms
DOI: 10.17632/ssfmrw2fg9.2
title: Radiographic Scoliosis Angle Estimation: Spline-based measurement reveals superior reliability compared to traditional COBB method

description: Raw Angle and Vertebra Detection Data of the study
DOI: 10.17632/j9v9dwg5vj.1
title: Rethinking Assessment: From Measuring to Promoting Critical Competencies

description: The main theme-problem addressed in the technical report is related to university professors' social representations of assessment in higher education. The investigation seeks to understand how these professors interpret and apply the concept of assessment in their pedagogical practices. This issue is crucial because assessment is viewed not merely as a tool for measuring performance but as a dynamic process involving the construction of meanings and the development of competencies for both teachers and students.

The general objective of the study is to investigate how university professors perceive and structure their assessment practices. To achieve this, the research aims to: (1) identify the central and peripheral elements in the social representations of professors regarding assessment; (2) analyze variations in perceptions among professors from different areas of knowledge; and (3) discuss the implications of these perceptions for pedagogical practice and teacher training.

Regarding data replicability, the technique used was the Free Word Association Technique (TALP), which allows data collection by having participants list words associated with the central theme. This method is highly replicable and can be applied in various academic and institutional contexts, as long as the focus remains on the social representations of specific groups. The data analysis was facilitated by the IRaMuTeQ software, a robust and freely accessible tool, which enabled the prototypical analysis of social representations. Therefore, the study can be replicated in other higher education institutions, or even at other educational levels, provided that the central theme is adjusted to the audience and context in question.

The analysis of the results was conducted through Prototypical Analysis, identifying the most frequently evoked words and terms related to the concept of assessment. The analysis was divided into quadrants, highlighting the Central Core, Peripheral Elements, and Contrast Zones of social representations. This approach offered an in-depth understanding of the more stable elements and those more susceptible to contextual variations, making it applicable in other settings where assessment practices are studied.
DOI: 10.17632/pd6gsm2m9r.1
title: Dataset for discriminant analysis

description: Data set of 50 projects to carry out discriminant analysis
DOI: 10.17632/6mpgk5z657.1
title: Forest succession alter the soil enzymes characteristics: Evidence from ecoenzymatic stoichiometry and temperature sensitivity

description: enzyme
DOI: 10.17632/5yjrm3vkj9.1
title: Carnivore-Livestock Conflict in the Mudumu Landscape, Namibia (2009-2021): A Comprehensive Data Set

description: This dataset documents carnivore-livestock conflicts in the Mudumu Landscape, Namibia, from 2009 to 2021. The data were meticulously gathered from event book records maintained by six communal conservancies - Balyerwa, Dzoti, Mashi, Mayuni, Sobbe, and Wuparo - within the framework of Community-Based Natural Resource Management (CBNRM). The dataset includes detailed records of incidents, specifying the date, location, species involved (both carnivores and livestock), and proximity to key environmental features such as parks, roads, and rivers. This dataset serves as a valuable resource for understanding the spatial and temporal patterns of human-wildlife conflict in this region.
DOI: 10.17632/59h6pwj543.1
title: Apparatus for measuring the properties of heat transfer by conduction in specials concrete

description: This work reports the design, development and operation of a hot plate device with guard called HD20 Hot Box to evaluate, under steady-state conditions, the transfer of heat by conduction through test tubes with prism-shaped faces. parallel, and indirectly determine the thermal conductivity of construction concretes with different aggregates. For its design, IRAM 11559, IRAM 11549 and IRAM 11601 standards valid in Argentina were applied. The hot box consists of two main units: the heating unit and the cooling unit. The thermal conductivity values obtained with this apparatus agree satisfactorily with the values published in the literature.
DOI: 10.17632/f4zvf29yd9.2
title: Prosper Software for Gas Lift System Design and Simulation

description: Artificial lift systems are widely used in the oil and gas industry to improve production rates in wells that cannot produce liquids to the surface under their own pressure. Among various artificial lift methods, the gas lift method is a popular technique used to enhance oil recovery. This involves injecting gas into the well to reduce the density of fluids generated by the reservoir, thereby reducing the weight of the fluid column and allowing the lower reservoir pressure to lift the fluids to the surface. However, building an effective gas lift system requires consideration of present and future operating conditions predicted by reservoir projections. To maximize oil exploration efficiency, it is crucial to evaluate each component of a well system to understand where pressure is lost, and nodal analysis can be used for this purpose. PROSPER software is a useful tool for performing nodal analysis and optimizing artificial lift systems to overcome frictional power losses and improve production rates.
DOI: 10.17632/6hm2xt5hd8.1
title: MAVES DB

description: Malayalam Audio-Visual Emotional Speech Data-Suitable for speech recognition, emotion recognition etc.
DOI: 10.17632/9n29p7rmn5.2
title: A synthetic population of Sweden: datasets of agents, households, and activity-travel patterns

description: The data contains a synthetic replica of over 10 million Swedish individuals (i.e., agents), their household characteristics, and activity-travel plans. The datasets are stored in a relational database format in Person, Household, and Activity-travel tables. The Person table contains the synthetic agents' socio-demographic attributes, such as age, gender, civil status, residential zone, personal income, car ownership,  employment, etc. The Household table stores agents' household attributes such as household type, size, number of children, and number of cars. The Activity-travel table contains the daily activity schedules of agents, i.e., where and when they do certain activities (work, home, school, and other) and how they travel between them (walk, bike, car, and public transport).
DOI: 10.17632/f3ftpd7xpg.1
title: MABSA: Malayalam Aspect Based Sentiment Analysis Dataset

description: MABSA is a curated aspect-based sentiment analysis dataset on the South Indian language Malayalam over the movie review domain. This dataset contains 4000 movie reviews in Malayalam and its sentiment labels for nine target aspects: direction, acting, songs, screenplay, story, casting, editing, cinematography, and BGM (background music). Each aspect in the review is mapped to one of the three sentiment classes, which are positive, negative, or neutral. These three sentiment classes are numerically represented as 1, 0, and 0.5, respectively. Movie reviews in Malayalam are collected through two methods, where the first method uses the Google survey tool to collect reviews from people, and the second method manually gathers movie reviews from various pages and groups on different social media platforms like Facebook, YouTube, and IMDb. The MABSA dataset consists of 4000 Malayalam movie reviews and a total of 5707 aspects manually annotated with its sentiment orientation.