cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ktkmtj3s4t
title: Reaction mechanism of benzylsuccinate synthase - results of modeling and kinetic experiments

description: The dataset collects results of the study on the reaction mechanism catalyzed by benzylsuccinate synthase (BSS) a fumarate-adding enzyme. Fumarate-adding enzymes (FAE) are a subset of the glycyl radical enzyme superfamily involved in anaerobic hydrocarbon degradation. Benzylsuccinate synthase catalyzes the enantiospecific formation of R-benzylsuccinate from toluene and fumarate, initiating anaerobic toluene degradation. In this deposition we collect crucial data from two papers: I) describing the initial step of the reaction i.e. hydrogen atom transfer from cysteine residue in the active site to glycyl radical which transfers radical close to the reagents and ii) description of the whole mechanism with QM:MM methods validated by prediction of kinetic isotope effect and enantioselectivity of the reaction. 

The first part of the data refers to the paper: M. Szaleniec et al., Modeling the Initiation Phase of the Catalytic Cycle in the Glycyl-Radical Enzyme Benzylsuccinate Synthase, The Journal of Physical Chemistry B 2024, 128, 24, 5823-5839 (https://doi.org/10.1021/acs.jpcb.4c01237). The data contains PDB structures of stationary states of the hydrogen atom transfer between Gly and Cys for the substrate and product bound enzyme as well as for the apoenzyme and AMBER parameters used to model BSS enzyme with radical Gly or Cys. 

The second part of the data refers to the preprint of the paper: M. Szaleniec et al., Unravelling the Enantioselective Mechanism of Benzylsuccinate Synthase: Insights into Anaerobic Hydrocarbon Degradation Through Multiscale Modelling and Kinetics, bioRxiv DOI: 10.1101/2024.10.11.617960. The data contains PDB structures of stationary states of the whole pathway (proR) and part of the proS pathway as well as results of kinetic experiments for both papers collected into an enzymeML spreadsheet. The enzymeML data contains results on KIE and HDX. 

Acknowledgements
The authors acknowledge the financial support provided by Deutsche Forschungsgemeinschaft/National Science Center Poland under Beethoven Life grant He2190/13-1 / 2018/31/F/NZ1/01856 as well as Polish high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2023/016888, PLG/2022/016024 and PLG/2021/015218.
DOI: 10.17632/k8xb6rf7y7
title: LINF_170009300

description: LSM domain containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.6084/m9.figshare.11879202.v3
title: Accession Numbers

description: This table contains all of the accession numbers for the viral genomes contained within this data set.
DOI: 10.17632/mc9jshwb38
title: Evaluation of 1D Convolutional Neural Network in Estimation of Mango Dry Matter Content

description: This data accompanies the research presented in the upcoming publication Walsh et al. 2024 "Evaluation of 1D Convolutional Neural Network in Estimation of Mango Dry Matter Content". It is intended to be used in conjunction with the code complementing the materials and methods of this study, available on GitHub at: https://github.com/jemjemwalsh/research-deep-learning-nirs/tree/main/publications/Eval-1D-CNN-Mango-DM

The original dataset before pretreatment and preparation for this study are available as Mango DMC and spectra Anderson et al. 2020 on Mendeley Data at: https://data.mendeley.com/datasets/46htwnp833/3
DOI: 10.6084/m9.figshare.18516476.v1
title: Wallace et al 2022 Data and R code - Urban Forest Restoration

description: Data for our paper "<strong>Restoration trajectories and ecological thresholds during planted urban forest successional development"</strong><strong></strong>
DOI: 10.17632/zcbh9r37rc
title: Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

description: This dataset compiles the results of a systematic literature review on user intent modeling in Natural Language Processing (NLP), with a focus on its application in conversational recommender systems. Over 13,000 papers from the past decade have been analyzed to provide a thorough understanding of the prevalent AI models used in this area. The dataset includes detailed examinations of various machine learning models such as SVM, LDA, Naive Bayes, BERT, Word2vec, and MLP, highlighting their advantages, limitations, and suitability for different scenarios in recommender systems.

Additionally, the dataset encompasses a wide range of applications of user intent modeling across sectors such as e-commerce, healthcare, education, social media, and virtual assistants. It sheds light on how these models aid in delivering personalized recommendations, detecting fake reviews, providing health interventions, tailoring educational content, and enhancing user experience on social media.

A key component of the dataset is a decision model, derived from the literature review, designed to assist researchers and developers in selecting the most appropriate machine learning model for specific user intent modeling tasks in recommender systems. This model addresses the challenge posed by the variety of available models and the lack of a clear classification scheme.

Furthermore, the dataset includes the outcomes of two academic case studies conducted to assess the utility of the decision model. These case studies follow Yin's guidelines and provide practical insights into the application of the decision model in real-world scenarios.

Researchers, developers, and practitioners in the field of NLP, AI, and recommender systems will find this dataset invaluable for navigating the complex landscape of user intent modeling. It not only synthesizes scattered research but also provides a practical tool for model selection, thereby contributing significantly to the advancement of personalized user experiences in various domains.

Keywords: User Intent Modeling, NLP, Conversational Recommender Systems, Machine Learning, Systematic Literature Review, Decision Model
DOI: 10.6084/m9.figshare.13003355.v1
title: Set 2_R4_possorted_genome

description: Renal artery stenosis (RAS) engenders stenotic-kidney ischemia, dysfunction, and injury, but whether these are mediated by cellular senescence has not been elucidated. Using INK-ATTAC transgenic mice, high-resolution imaging, and unbiased scRNA-sequencing of murine kidneys, the authors identified cellular senescence as an important mechanism of progressive injury triggered in renal epithelial/stromal cells within post-stenotic kidneys. Both P16-specific and broad quercetin/dasatinib interventions to blunt senescence improved renal function and structure, underscoring its central role in the pathogenesis of the disease. Furthermore, this mechanism was conserved in human subjects with RAS. These observations reveal new mechanisms that contribute to the pathogenesis of chronic ischemic renal injury, and support development of senolytic therapy to reduce senescent cell burden and delay renal injury.
DOI: 10.17632/bymgs9z2s2
title: Accused Is the Favorite Child Of Law; A Critical Analysis Of High Acquittal Rate In Pakistan

description: The focus of this study is Pakistan's criminal justice system's low conviction rate. The low conviction rate in Pakistan's broken criminal justice system is directly related to the incompetence of the investigating officers and the flawed, antiquated, and ineffective techniques used to investigate the case. It is also indirectly related to the lethargic prosecution and the high evidentiary standards of proof demanded by the law. As the cornerstone of the criminal justice system, the role of investigation is emphasised, as are the inconsistencies of the investigation and the prosecution department's lack of authority in criminal cases. In addition, the "proof beyond a reasonable doubt" principle and how even slight doubts lead to the acquittal of the accused are two other factors that favour the accused in his acquittal. In order to gain a more comprehensive understanding of the research, we have compared the distinct categories of criminal cases between Pakistan and India in this article. We will also discuss the shortcomings of the current system and offer some recommendations.
DOI: 10.17632/nkdmg5dgpv
title: Data for: Flexible Multi-Manned Assembly Line Balancing Problem: Model, Heuristic Procedure, and Lower Bounds for Line Length Minimization

description: This directory contains: Input files for all precedence graphs included in the data sets of: Scholl, Armin (1993): Data of Assembly Line Balancing Problems. Schriften zur Quantitativen Betriebswirtschaftslehre 16/93, Th Darmstadt. Format of in2-files: line 1: number n of tasks lines 2-n+1: integer task times lines n+2,...: direct precedence relations in form "i,j" last line: end mark "-1,-1" (optional)
DOI: 10.6084/m9.figshare.22922426.v2
title: Complete morphology.xlsx of the Native chicken maintained at College of Poultry Production and Management, Hosur

description: the data collected while conducting the trial from o to 20 weeks of age. the data are presented in the form of excel format <br>
DOI: 10.6084/m9.figshare.5129422.v1
title: Supplementary Material for: Meconium Evacuation for Facilitating Feed Tolerance in Preterm Neonates: A Systematic Review and Meta-Analysis

description: <b><i>Background:</i></b> A delayed passage of meconium is considered as a risk factor for feed intolerance in preterm neonates. <b><i>Objectives:</i></b> The aim of this study was to review the effects of different therapeutic agents for meconium evacuation on feed tolerance in preterm neonates. <b><i>Methods:</i></b> A systematic review of randomised controlled trials (RCTs) of different therapeutic agents for meconium evacuation in preterm neonates (gestation &lt;32 weeks and/or birth weight &lt;1,500 g) using the Cochrane systematic review methodology was undertaken. Databases including Google Scholar were searched in January 2016. The primary outcome was the time to reach full feeds (TFF; ≥120 ml/kg feeds with stoppage of parenteral nutrition &gt;24 h). Secondary outcomes included necrotising enterocolitis (NEC), weight at discharge and adverse effects. The results were summarised as per the GRADE guidelines. <b><i>Results:</i></b> Six RCTs (2 each of glycerine suppository and enema, 1 normal saline enema and 1 oral osmotic contrast agent; n = 442) with a low or unclear risk of bias were included. The pooled estimate (random effects model) showed no reduction in TFF [mean difference (MD) -0.03, 95% CI -2.47, 2.41, p = 0.98; level of evidence: low]. No differences in NEC [risk ratio (RR) 1.71, 95% CI 0.63, 4.65, p = 0.30; level of evidence: low] and weight at discharge (MD -0.08, 95% CI -0.30, 0.15, p = 0.50; level of evidence: low) were found. The trial assessing oral osmotic contrast agents reported a trend towards a higher incidence of NEC ≥ stage II. There were no other adverse effects. <b><i>Conclusion:</i></b> Limited low-quality evidence indicates that prophylactic glycerine suppository, small volume glycerine/normal saline enema or oral osmotic contrast agents to evacuate meconium did not reduce TFF in preterm neonates. Large, well-designed trials are essential to study this clinically significant issue.
DOI: 10.17632/8633hm46p5
title: Sex- and strain-dependent effects of ageing on sleep and activity patterns in Drosophila

description: Raw lifespan and Drosophila Activity Monitor (DAM5H) data to support the manuscript "Sex- and strain-dependent effects of ageing on sleep and activity patterns in Drosophila."
DOI: 10.17632/c4x958pzpm
title: Data Retention Period Disclosures in Privacy Policies

description: 115 privacy policies from the OPP-115 corpus have been re-annotated with the specific data retention periods disclosed, aligned with the GDPR requirements disclosed in Art. 13 (2)(b). Those retention periods have been categorized into the following 6 distinct cases:

C0: No data retention period is indicated in the privacy policy/segment.
C1: A specific data retention period is indicated (e.g., days, weeks, months...).
C2: Indicate that the data will be stored indefinitely.
C3: A criterion is determined during which a defined period during which the data will be stored can be understood (e.g., as long as the user has an active account).
C4: It is indicated that personal data will be stored for an unspecified period, for fraud prevention, legal or security reasons.
C5: It is indicated that personal data will be stored for an unspecified period, for purposes other than fraud prevention, legal, or security.
Note: If the privacy policy or segment accounts for more than one case, the case with the highest value was annotated (e.g., if case C2 and case C4 apply, C4 is annotated).

Then, the ground truth dataset served as validation for our proposed ChatGPT-based method, the results of which have also been included in this dataset.

Columns description:
- policy_id: ID of the policy in the OPP-115 dataset
- policy_name: Domain of the privacy policy
- policy_text: Privacy policy collected at the time of OPP-115 dataset creation
- info_type_value: Type of personal data to which data retention refers
- retention_period: Period of retention annotated by OPP-115 annotators
- actual_case: Our annotated case ranging from C0-C5
- GPT_case: ChatGPT classification of the case identified in the segment
- actual_Comply_GDPR: Boolean denoting True if they apparently comply with GDPR (cases C1-C5) or False if not (case C0)
- GPT_Comply_GDPR: Boolean denoting True if they apparently comply with GDPR (cases C1-C5) or False if not (case C0)
- paragraphs_retention_period: List containing the paragraphs annotated as Data Retention by OPP-115 annotators and our red text describing the relevant information used for our annotation decision
DOI: 10.17632/jbkpf5p97b
title: Automatic extraction of STL features

description: The dataset consists of the following attributes, each representing different aspects of 3D printed models:

1. File Name: The name of the STL file representing the 3D model.

2. Volume: The overall volume of the 3D model in cubic units, indicating the amount of space the object occupies.

3. dX, dY, dZ: The dimensions of the bounding box that encloses the entire 3D model, along the X, Y, and Z axes, respectively. These dimensions help understand the spatial extent of the model.

4. Number of Layers: The total number of layers that make up the 3D printed object. This reflects the complexity and granularity of the model.

5. Area of First Layer: The surface area of the first layer of the model. This is crucial for assessing the initial contact with the build platform, which affects adhesion.

6. Area of Last Layer: The surface area of the last layer of the model, which might differ from the first layer, providing insights into the model’s geometry and build process.

7. comFLx, comFLy, comFLz: The coordinates of the center of mass of the first layer along the X, Y, and Z axes. These values are important for understanding the balance and distribution of mass at the initial layer.

8. comLLx, comLLy, comLLz: The coordinates of the center of mass of the last layer along the X, Y, and Z axes, providing similar insights as the first layer but for the completion of the model.

9. Center of Mass Difference: A tuple representing the difference in the center of mass between the first and last layers, which helps in analyzing stability and structural integrity throughout the build.

10. Build Adhesion: A binary or categorical attribute indicating whether the initial layer adhered properly to the build platform (1 for successful adhesion, 0 for failure). This is a critical quality metric in 3D printing.

This dataset is designed to analyze the relationships between these attributes and to predict the likelihood of successful build adhesion in 3D printing processes.
DOI: 10.6084/m9.figshare.19793119.v1
title: Training dataset

description: This dataset consists of a set of manually annotated fluoroscopy images in which the contrast-filled bladder was imaged from various angles and at different stages of the filling/voiding cycle.
DOI: 10.6084/m9.figshare.28069163.v1
title: Twitter dataset

description: The <b>Truth Seeker Dataset</b> is designed to support research in the detection and classification of misinformation on social media platforms, particularly focusing on Twitter. This dataset is part of a broader initiative to enhance the understanding of how machine learning (ML) and natural language processing (NLP) can be leveraged to identify fake news and misleading content in real-time.Dataset CompositionThe Truth Seeker Dataset comprises a substantial collection of social media posts that have been meticulously labeled as either real or fake. It was constructed using advanced ML algorithms and NLP techniques to analyze the language patterns in social media communications. The dataset includes:<b>Raw Social Media Posts</b>: A diverse range of tweets that reflect various topics and sentiments.<b>Labeling</b>: Each post is annotated with binary labels indicating its authenticity (real or fake).<b>Feature Sets</b>: Two distinct subsets of the dataset have been created using different NLP vectorization methods—Word2Vec and TF-IDF. This allows researchers to explore how different feature representations impact model performance.<b>Research Applications</b>The primary aim of the Truth Seeker Dataset is to facilitate the development and validation of models that can accurately classify social media content. Key applications include:<b>Fake News Detection</b>: Utilizing various ML algorithms, including Random Forest and AdBoost, which have demonstrated high F1 scores in preliminary evaluations.<b>Model Comparison</b>: Researchers can compare the effectiveness of different ML approaches on the same dataset, enabling a clearer understanding of which methods yield the best results in detecting misinformation.<b>Algorithm Development</b>: The dataset serves as a benchmark for developing new algorithms aimed at improving accuracy in fake news detection.
DOI: 10.17632/4xdv6wjmfz
title: Data for: Tectonic influences on late Holocene relative sea levels from the central-eastern Adriatic coast of Croatia.

description: Relative sea-level data from salt-marsh reconstruction at Jadrtovac, Croatia. Raw foraminiferal count data from salt-marsh core.
DOI: 10.17632/yt8f2zzfpt
title: x ray dataset

description: x ray dataset
DOI: 10.17632/77xyv9njzr
title: Dental esthetics perception

description: Influence of dental education on the dental esthetics attitude and perception
DOI: 10.17632/jpg9bjzjwk
title: Process Innovation

description: This is the Stata code and public version of the data used in Bena, Ortiz-Molina, and Simintzi "Shielding Firm Value: Employment Protection and Process Innovation", forthcoming at the Journal of Financial Economics.
DOI: 10.17632/ssvkbjscxg
title: Data for: Hemorrhagic Presentations of COVID-19: Risk factors for mortality

description: COVID 19 and Cerebral hemorrhage
DOI: 10.17632/39xyyjdsf3
title: Data for: Differential associations of clinical features with cerebrospinal fluid biomarkers in dementia with Lewy bodies and Alzheimer’s disease

description: Excel sheet with demographic and clinical data from all patients.
DOI: 10.17632/gy3f7kdpdz
title: Data for: International spillovers of quantitative easing

description: Datasets and codes replicating the results in "International spillovers of quantitative easing".
DOI: 10.17632/3832tx2cb2
title: Cassava Leaf Disease Dataset

description: RGB image dataset of cassava healthy leaves and leaves infected by Cassava Bacterial Blight (CBB) and Cassava Mosaic Virus (CMV). The field images were collected manually from different commercial agricultural fields in western Tamil Nadu, India, using a handheld camera. The size of the images is 512 × 512 pixels.
DOI: 10.17632/vhxdgjfjnk
title: Dataset of Tweets and News Media on COVID-19 in Portuguese

description: Collection of 3,925,366 tweets and 18,413 online news around the online discussion about COVID-19 in Brazil. The data from Twitter were collected through Twitterscraper Python library and we considered a set of keywords in Portuguese regarding to COVID-19. In order to facilitate the identification of tweets that have hashtags, media and retweets for researchers or data enthusiasts, we created three specific datasets for each of these categories. The news on COVID-19 was collected from the UOL portal, the most popular Brazilian website. All the data were gathered from January to May, 2020. These dataset can attract the attention from communities such as data science, social science, natural language processing, tourism, infodemiology, and public health.
DOI: 10.6084/m9.figshare.28581110.v1
title: Data and code for the paper "<b>Long-term knee health in adults with a history of adolescent Osgood-Schlatter: A cross-sectional follow-up on a </b><b>national cohort</b><b> </b><b>of patients from secondary care in Denmark 1977-2020</b>"

description: Data and code for the paper "<b>Long-term knee health in adults with a history of adolescent Osgood-Schlatter: A cross-sectional follow-up on a </b><b>national cohort</b><b> </b><b>of patients from secondary care in Denmark 1977-2020</b>"
DOI: 10.6084/m9.figshare.16988539.v4
title: Data and R code from: Multiple anthropogenic pressures eliminate the effect of biodiversity on ecosystem functions

description: Biodiversity contributes to maintaining ecosystem functions when faced with anthropogenic pressures. However, ecosystems are now exposed to a rapidly growing number of such pressures, and it remains unknown whether biodiversity can still sustain ecosystem functioning under anthropogenic pressures. Here we investigated the effects of soil biodiversity on soil functions and properties when faced with an increasing number of simultaneous global change factors (GCFs). Higher soil biodiversity had a positive effect on soil functions and properties when zero and only a few (e.g., 1 – 4) GCFs were applied, but this positive effect was eliminated by the co-occurrence of<br>numerous GCFs. This was attributable to the reduction of soil fungal abundance and the relative abundance of a bacterial cluster. These results suggest that biodiversity can contribute to ecosystem functions only when a few GCFs are active, but that this will be jeopardized by the presence of multiple GCFs. Our study indicates that reducing the number of anthropogenic pressures should be a goal in ecosystem management, in addition to biodiversity conservation.
DOI: 10.17632/b83fh386dh
title: Supplementary Materials_baseline table

description: Supplementary Materials_baseline table
DOI: 10.17632/msg7bfg5t6
title: Ideotype optimization with Crop Modelling: CERES-Rice

description: This dataset supports the study on rice ideotype optimization using the CERES-Rice model integrated with AI-driven sensitivity analysis and genetic algorithm optimization. It includes the input files, environmental scenarios (weather and soil data), and the output results from 1,884 virtual rice cultivars. Each cultivar is evaluated across four contrasting environments to analyze performance based on grain yield and water use efficiency. The dataset is structured to allow reproduction of the full analysis pipeline, including outputs and the final ideotype comparison with field cultivars.