cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/kmhgrg4rpz.1
title: The Effect of Climate Change Threat on Public Attitudes towards Ethnic and Religious Minorities and Climate Refugees

description: This entry includes the text of three surveys distributed via Qualtrics company to ethnic white British citizens in 2020 (Study 1, Survey T1 and T2) and 2021 (Study 2 - different sample from Study 1) and associated datasets (Study 1 and Study 2). Hence, Study 1 consists of two survey waves whereby the second wave was a recontact of participants in wave 1. Study 2 includes a completely different sample of ethnic white British citizens. The surveys used an experimental design (Study 1 (T2) and Study 2) to investigate the effect of climate change threat on attitudes towards ethnic/religious minorities and climate refugees. We found no significant direct effect. We found significant interaction effect of climate change threat and national climate efficacy (the belief that the UK will successfully manage negative climate change impacts) on the attitude of ethnic white British participants towards the least liked ethnic and religious minorities in the country (both studies) and climate refugees (Study 1). The moderating role of collective climate efficacy beliefs suggests that processes of group-based control may be central for explaining ethnocentric responses to climate change threat.  

The data was cleaned of variables pertaining to a different study on the effect of climate change threat on environmental extremism, which is still under analysis.  More information on this research can be found in these pre-registrations: https://aspredicted.org/S8X_13Z and https://aspredicted.org/ZXZ_SXQ.
DOI: 10.17632/n5nyf9mgbb.1
title: Methotrexate for PG: Supplemental Table

description: Supplemental Table for the article, "Methotrexate for pyoderma gangrenosum: a retrospective case series of 33 patients" by Josiah A. Williams, MD; Matthew L. Hrin, MD; Nathan L. Bowers, MD, PhD; Joseph L. Jorizzo; MD, Steven R. Feldman, MD, PhD; William W. Huang, MD, MPH
DOI: 10.17632/wynp3yvycc.1
title: WhatsApp Business APK Forensic Investigation Data from Samsung Galaxy A54 5G

description: Folder Descriptions: 1. Magnet_AXIOM-Acquire_Mobile_Agent --&gt; The Screenshots of Magnet AXIOM Acquire Mobile Agent running on the mobile phone. 2. Magnet_AXIOM-Process --&gt; The Screenshots of Magnet AXIOM Process acquiring Evidence from the mobile phone. 3. Magnet_AXIOM-Examine --&gt; The Screenshots of Magnet AXIOM Examine displaying and visualizing the Evidence acquired from the mobile phone. 4. WhatsApp_Business_Data --&gt; The Data related to "WhatsApp Business APK" acquired from the mobile phone. 5. WhatsApp_Business_APK --&gt; The Android Application Package of WhatsApp Business that has been extracted from the mobile phone. 6. WhatsApp_Business_APK_Analysis --&gt; The Screenshots of WhatsApp Business APK after Debugging on Android SDK.
DOI: 10.5281/zenodo.10101767
title: Converting NCBI Data to the AnnData Format

description: Input dataset for the tutorial "Converting NCBI data to the AnnData Format" hosted on the Galaxy Training Network
DOI: 10.5281/zenodo.10101768
title: Converting NCBI Data to the AnnData Format

description: Input dataset for the tutorial "Converting NCBI data to the AnnData Format" hosted on the Galaxy Training Network
DOI: 10.17632/ft9bhdgybs.1
title: AN EMG DATASET FOR ARABIC SIGN LANGUAGE ALPHABET

description: ABSTRACT Sign languages are natural, gestural languages that use visual channel to communicate. Deaf people develop them to overcome their inability to communicate orally. Sign language interpreters bridge the gap that deaf people face in society and provide them with an equal opportunity to thrive in all environments. However, Deaf people often struggle to communicate on a daily basis, especially in public service spaces such as hospitals, post offices, and municipal buildings. Therefore, the implementation of a tool for automatic recognition of sign language is essential to allow the autonomy of deaf people. Moreover, it is difficult to provide full-time interpreters to help deaf people in all public services and administrations. Although surface electromyography (sEMG) provides an important potential technology for the detection of hand gestures, the related research in automatic SL recognition remains limited. To date, most works have focused on the recognition of hand gestures from images, videos, or gloves. The works of BEN HAJ AMOR et al. on EMG signals have shown that these multichannel signals contain rich and detailed information that can be exploited, in particular for the recognition of handshape and for the control prosthesis. Consequently, these successes represent a great step towards the recognition of gestures in sign language. We build a large database of EMG data, recorded while signing the 28 characters of the Arabic sign language alphabet. This provides a valuable resource for research into how the muscles involved in signing produce the shapes needed to form the letters of the alphabet. Instructions: The data for this project is provided as zipped NumPy arrays with custom headers. In order to load these files, you will need to have the NumPy package installed. The respective loadz primitive allows for a straight forwardloading of the datasets. The data is organized as follows: The data for each label (handshape) is stored in a separate folder. Each folder contains .npz files. An npz file contains the data for one record (a matrix 8x400). For more details, please refer to the paper.
DOI: 10.17632/ft9bhdgybs.3
title: AN EMG DATASET FOR ARABIC SIGN LANGUAGE ALPHABET LETTERS AND NUMBERS

description: ABSTRACT Sign languages are natural, gestural languages that use visual channel to communicate. Deaf people develop them to overcome their inability to communicate orally. Sign language interpreters bridge the gap that deaf people face in society and provide them with an equal opportunity to thrive in all environments. However, Deaf people often struggle to communicate on a daily basis, especially in public service spaces such as hospitals, post offices, and municipal buildings. Therefore, the implementation of a tool for automatic recognition of sign language is essential to allow the autonomy of deaf people. Moreover, it is difficult to provide full-time interpreters to help deaf people in all public services and administrations. Although surface electromyography (sEMG) provides an important potential technology for the detection of hand gestures, the related research in automatic SL recognition remains limited. To date, most works have focused on the recognition of hand gestures from images, videos, or gloves. The works of BEN HAJ AMOR et al. on EMG signals have shown that these multichannel signals contain rich and detailed information that can be exploited, in particular for the recognition of handshape and for the control prosthesis. Consequently, these successes represent a great step towards the recognition of gestures in sign language. We build a large database of EMG data, recorded while signing the 28 characters of the Arabic sign language alphabet. This provides a valuable resource for research into how the muscles involved in signing produce the shapes needed to form the letters of the alphabet. Instructions: The data for this project is provided as zipped NumPy arrays with custom headers. In order to load these files, you will need to have the NumPy package installed. The respective loadz primitive allows for a straight forwardloading of the datasets. The data is organized as follows: The data for each label (handshape) is stored in a separate folder. Each folder contains .npz files. An npz file contains the data for one record (a matrix 8x400). For more details, please refer to the paper.
DOI: 10.17632/53yh35mrfd.1
title: Antimicrobial sol-gel data collection

description: Raw and processed data. Sol-gel UPEC 2023
DOI: 10.17632/thg4n4ddtj.1
title: EXPLORING THE IMPACT OF THE COVID-19 PANDEMIC ON ACADEMIC STRESS: A CROSS-SECTIONAL STUDY OF JUNIOR COLLEGE STUDENTS IN WEST BENGAL

description: Abstract: The objective of the current research is to investigate potential disparities in academic stress levels among junior college students during the COVID-19 era, specifically in relation to gender (male and female), habitat (urban and rural), and topic stream (science, commerce, and humanities). The study used a survey research methodology. This research is a cross-sectional investigation undertaken on junior college students affiliated with the West Bengal Council of Higher Secondary Education, namely from the south 24 Pargana area in West Bengal. A total of 600 students from a junior college were selected using a stratified random sample technique. The data gathering in this research included a standardised questionnaire. Data analysis included the use of statistical measures such as the mean, standard deviation, t-test, and F-test. The findings of this research indicate that male junior college students experience greater levels of academic stress compared to their female counterparts. The level of academic stress experienced by junior college students residing in urban areas is comparatively greater in comparison to their counterparts in rural areas. The level of academic stress experienced by junior college students is shown to be greater in the scientific subject as compared to the humanities and commerce disciplines.
DOI: 10.17632/57j5tjcx2w.1
title: Quantitative distribution profile of cadmium and lead in different organs of rats and mitigation of their accumulation through probiotic treatment

description: Atomic absorption spectroscopy data of Wistar rat tissues and feces samples
DOI: 10.17632/ty68672dnz.1
title: Sexually Transmitted Diseases in Females for Data Security and Privacy in 3D Modeling for Healthcare

description: Overview: This dataset comprises information collected from 450 females and is utilized for research in the field of Data Security and Privacy in 3D Modeling for Healthcare. The dataset is focused on the prevalence and analysis of sexually transmitted diseases (STDs) in females. It provides valuable insights into the demographics, behaviors, and health status of the study participants. Data Fields: The dataset includes the following key fields for each of the 450 females: S.no: A unique identifier assigned to each participant. Age: The age of the female participant, ranging from young adults to middle-aged individuals. Intimate Partners: The number of intimate partners the female has had, indicating their level of sexual activity. Protection Usage: A binary variable (0: Never, 1: Sometimes, 2: Always) representing the usage of protection during sexual activity. Symptoms: A binary variable (0: No symptoms, 1: Symptoms) indicating the presence or absence of symptoms related to STDs. Location: The location of the participant, categorized into general city/district areas, which can provide geographical context. Education: A binary variable (0: Low education, 1: High education) representing the education level of the participant. STD Testing History: A binary variable (0: No, 1: Yes) indicating whether the participant has a history of undergoing anonymous STD testing. STD Status: A binary variable (0: Uninfected, 1: Infected) reflecting the STD status of the female participants. Usage: This dataset serves as a valuable resource for researchers in the fields of healthcare, data security, and 3D modeling. Researchers can leverage this dataset to explore the relationship between demographic factors, behaviors, and STD prevalence among females. It is particularly relevant for studies that aim to enhance data security and privacy while utilizing 3D modeling techniques for healthcare applications. Data Privacy and Ethics: The collection of this dataset adheres to ethical and privacy considerations, with a focus on ensuring the anonymity and confidentiality of the study participants. Personal identifiers have been removed to protect the privacy of the individuals. Citation: If you intend to use this dataset in your research, please consider citing the source and acknowledging the data collection process. Proper citation helps maintain transparency and credit the researchers and institutions involved in data collection.
DOI: 10.17632/r26sr3vjwb.1
title: An essential role for palmitoylation in NLRP3 inflammasome activation

description: The NLRP3 inflammasome is a critical mediator of innate immune response. How NLRP3 responds to stimuli and initiates the assembly of the NLRP3 inflammasome is not fully understood. Here, we found that cellular metabolite, palmitate, facilitates NLRP3 activation by enhancing its S-palmitoylation, in synergy with lipopolysaccharide stimulation. NLRP3 is post-translationally palmitoylated by ZDHHC5 at the leucine-rich repeats (LRR) domain, which promotes NLRP3 inflammasome assembly and activation. Silencing ZDHHC5 blocks NLRP3 oligomerization, NLRP3-NEK7 interaction, and formation of large intracellular ASC aggregates, leading to abrogation in caspase-1 activation and IL-1β release. ABHD17A depalmitoylates NLRP3, and one heritable disease-associated mutation in NLRP3 was found to be associated with defective ABHD17A binding and hyper-palmitoylation. Furthermore, Zdhhc5-/- mice showed defect in NLRP3 inflammasome activation in vivo. Taken together, our data reveal an endogenous mechanism of inflammasome assembly and activation and suggest NLRP3 palmitoylation as a potential target for the treatment of NLRP3 inflammasome-driven diseases.
DOI: 10.17632/r26sr3vjwb.2
title: ZDHHC5-mediated NLRP3 palmitoylation promotes NLRP3-NEK7 interaction and inflammasome activation

description: The NLRP3 inflammasome is a critical mediator of the innate immune response. How NLRP3 responds to stimuli and initiates the assembly of the NLRP3 inflammasome is not fully understood. Here, we found that a cellular metabolite, palmitate, facilitates NLRP3 activation by enhancing its S-palmitoylation, in synergy with lipopolysaccharide stimulation. NLRP3 is post-translationally palmitoylated by ZDHHC5 at the leucine-rich repeats (LRR) domain, which promotes NLRP3 inflammasome assembly and activation. Silencing ZDHHC5 blocks NLRP3 oligomerization, NLRP3-NEK7 interaction, and formation of large intracellular ASC aggregates, leading to abrogation of caspase-1 activation, IL-1β/18 release, and GSDMD cleavage, both in human cells and in mice. ABHD17A depalmitoylates NLRP3, and one human heritable disease-associated mutation in NLRP3 was found to be associated with defective ABHD17A binding and hyper-palmitoylation. Furthermore, Zdhhc5-/- mice showed defective NLRP3 inflammasome activation in vivo. Taken together, our data reveal an endogenous mechanism of inflammasome assembly and activation and suggest NLRP3 palmitoylation as a potential target for the treatment of NLRP3 inflammasome-driven diseases.
DOI: 10.17632/w5cnn9zd37.1
title: LAGA CT-guide lung biopsy

description: This database is about the LAGA-assisted CT-guided lung tumor biopsy.
DOI: 10.17632/mff5m4jjxb.1
title: Focus group transcript of a research study on the educational potential of a Portuguese Scouts organization

description: The Portuguese educational system aims at nurturing the holistic development of individuals and societal progress, surpassing the mere transmission of curriculum-based knowledge. While considerable advancements have been made, the need for ongoing innovation and equal educational opportunities remains evident, so as all children can have not only equal access to education, but also equal chances of success. This study investigates the largest Portuguese Scout movement, CNE, exploring its organizational and pedagogical approaches, potentially applicable in formal education settings. The research surveyed participants aged 18-22, having completed approximately 12 years in the Scout movement. Data collection via focus groups followed a semi-structured script, before a thorough content analysis was carried. Findings are organized as: Pedagogical organization in learning cycles; Patrol system; The four stages of the project method; Characterization of the Scout game and other learning process dimensions; Other competencies and values developed throughout the Scout learning course; Comparisons with formal education contexts. The discourse of participants strongly suggests the Scout movement's potential to inspire educational change, displaying consistent indicators of pedagogical innovation. The study advocates for further exploration of CNE's pedagogical functioning and its potential to serve as a model for formal education, as evidenced by the learners' reflections.
DOI: 10.17632/vsxdn9wfhz.1
title: Effect of Vasopressin Injection Technique in Laparoscopic Excision of Unilateral Ovarian Endometriomas on Ovarian Reserve

description: Study Objective: To assess the efficacy of vasopressin injection in reducing anti-Müllerian hormone(AMH) decreases following surgery for unilateral endometrioma. Design: In this randomized controlled study, patients were stratified according to AMH level, then randomly assigned to the vasopressin injection group or control group. AMH level was measured 12 months after laparoscopic surgery (primary endpoint). Analyses for the primary endpoint were performed using an independent t-test, analysis of covariance, and linear mixed model for random and fixed effects. Setting: Tertiary university-affiliated medical center. Patients: Seventy-five women. Interventions: Conservative laparoscopic surgery for unilateral ovarian endometrioma with or without vasopressin injection. Measurements and Main Results: In the intention-to-treat analysis, cyst stripping (14.38±13.68 min vs. 8.91±7.89 min, p=.054) and bleeding control (6.53±6.77 min vs. 2.15±2.53 min, p=.002) times were shorter in women who received vasopressin. However, the decrease of AMH level 12 months after surgery did not differ significantly between the the control and vasopressin groups (-1.17±0.97 ng/mL vs.-0.96±1.11 ng/mL, respectively; p=.426). AMH values did not differ significantly between the 2 groups over time (pgroup × time= .816) or at any time point (baseline, 3, 6, or 12 months). In subgroup analysis according to baseline AMH level (&lt;3 ng/mL and 3-7 ng/mL), hormone values between the control and vasopressin groups did not differ significantly over time (pgroup × time= .923 and .556, respectively) or at each time point. Similar results were obtained in the per-protocol analysis. In the pathological analysis, the thickness and proportions of the excised ovarian tissue showed no significant difference between the groups. Conclusion: Although vasopressin injection did not prevent a decrease in AMH levels after endometrioma surgery, it reduced cyst stripping and bleeding control times. Trial registration: NCT04372836 (https://clinicaltrials.gov/ct2/show/NCT04372836) Date of registration and enrollment of the first subject: October 29, 2018
DOI: 10.17632/6kdgmhgn7k.1
title: Interviews with GenAI Tools: Using Generative Artificial Intelligence to Enhance Experiential Learning for Authentic Assessment

description: These interviews investigate the integration of Generative Artificial Intelligence tools into Experiential Learning for Authentic Assessment as explained by ChatGPT 3.5, Google Bard, Microsoft New Bing, and Anthrop\c Claude. The set of interviews was conducted using “thing ethnography” interviewing.
DOI: 10.17632/cfc9wf4fmy.1
title: Data for: Schisandrin A alleviates Diabetes-Associated Cognitive Dysfunction: Involvement in Inflammation and Ferroptosis

description: Data for: Schisandrin A alleviates Diabetes-Associated Cognitive Dysfunction: Involvement in Inflammation and Ferroptosis
DOI: 10.17632/5fdjb83khv.1
title: Data - Pressure volume equation of state of Fe18Pt82

description: Data for the XRD data and Figures 3-5 of the manuscript.
DOI: 10.17632/938p5c7tp3.1
title: West African Football Players - CAE and NHE Intervention Study

description: Intervention data - 4 timepoints of CAE and NHE interventions (8 week intervention + 4 week rest + 10 week intervention).
DOI: 10.17632/y6gcdc85sf.1
title: Fully automatic REM sleep stage-specific intervention systems using single EEG in mice

description: Raw data for the paper. Codes for UTSN-L2 are available at https://github.com/tarotez/sleepstages. EEG data for teacher data and tests are in the following link (Google drive). https://drive.google.com/drive/folders/1Y5IYUKw0ehcYPmQFP5jNHZoe4hW_dfUZ?usp=drive_link
DOI: 10.17632/bvts42fpfb.1
title: VTAR11

description: Advances in Artificial Intelligence and Computer Vision have made automation systems popular in a variety of fields. One of these is Human Activity Recognition, which has a number of novel, practical and innovative applications. Because the HAR is a complex system, many issues and challenges need to be addressed. One of the most challenging research areas under HAR is identifying toddler activities from various vision input devices. In order to enhance the safety and wellness of toddlers, their behavior has to be studied separately. For this, a comprehensive understanding of existing toddler activity systems, available datasets to conduct experiments, challenges involved, and potential societal applications must be explored. Toddler activity recognition is a specialized subfield of computer vision and machine learning that focuses on automatically identifying and classifying activities performed by toddlers or young children. Vision-based toddler activity recognition plays a crucial role in monitoring developmental milestones, detecting anomalies, and supporting research in child development studies. Current human activity recognition primarily emphasizes adult activities. However, toddler activities, while sharing similarities with those of adults, exhibit significant variations in execution. Consequently, there exists ample opportunity to explore toddler activities as a distinct and separate research domain. This paper presents an image dataset designed for the purpose of recognizing toddler activities. The dataset consists of 2,389 raw images distributed across 11 distinct activity classes. Each activity class contains over 195 images, ensuring a well-balanced distribution within the classes. The considered toddler activity categories for experimentation encompass crying, eating, drinking, smiling, sleeping, yawning, W-sitting, playing on a swing, playing with stacking rings, toddlers on a staircase, and toddlers with a knife. The dataset was meticulously compiled, merging images obtained from Google with manually collected photographs. The dataset undergoes image preprocessing on the raw images as part of the model building process. This preprocessing involves auto orientation, contrast stretching, and resizing the images to 320x320 pixels while accommodating white edges. To enhance the diversity of the original images, image augmentation techniques were employed. These techniques included horizontal flipping, the application of grayscale to 10% of the images, brightness adjustments with variations ranging from -25% to +25%, up to 1-pixel blurring, and the addition of noise to up to 3% of the pixels. As a result, a comprehensive training dataset was created, consisting of 12,331 images. Subsequently, the resulting dataset was utilized for model construction. The authors leveraged the RoboFlow AutoML model, achieving a validation accuracy of 93.2%.