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880 results for “Exploratory Study”
Investigating instability architectural smells evolution: an exploratory case study
<p>This is the dataset used in our case study on architectural smells evolution. We tracked smells from 524 versions among 14 open source Java systems. </p> <p>More information can be found in our ICSME'19 paper titled: "Investigating instability architectural smells evolution: an exploratory case study".</p> <p>Additionally, you can find the tool on GitHub: <a href="https://github.com/darius-sas/astracker">https://github.com/darius-sas/astracker</a></p>
Backpain exercise therapy remodels human epigenetic profiles in buccal and human peripheral blood mononuclear cells: An exploratory study in young male participants
<pre><strong>###### Files description #####</strong><br> <strong>Notes</strong>. 1) "BT" refers to before therapy and "AT" to after therapy. 2) 0 refers to FALSE and 1 to TRUE for binary variables. The provided files have tab-separated columns except the .RDS which is and R output of the mixOmics DIABLO integration analysis. <strong># Questionnaire</strong> > participants_categories.tsv: per participant (rows), output of the clustering with the participant ("ID") category ("category") per class<br> ("class") > questionnaire_agility_metrics.tsv: questionnaire and agility metrics per participant (rows) for the participants ("ID") with at least one paired AT+BT data in one type of biological sample (indicated in the columns "swab", "PBMC", and "plasma") <strong># PTMs</strong> Samples´ names are encoded as PBMC_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch (we removed _batch column suffix for the <br>processed files). NA indicates an undetected intensity. > raw_PBMC_light_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_light_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavy_labelled_intensities.tsv: raw intensity of light/endogenous peptides (row) by precursor per sample (column) from PBMC > raw_swab_heavy_labelled_intensities.tsv: idem from buccal cells > raw_PBMC_heavynormalized_intensities.tsv: raw intensity of light peptides normalized by heavy peptides intensity (row) by precursor per <br>sample (column) > raw_swab_heavynormalized_labelled_intensities.tsv: idem from buccal cells > processed_cleaned_PBMC_log2intensities.tsv: processed (heavy normalized, imputed, batch-corrected) intensity of peptides aggregated by modification (PTM, row) by precursor per sample (column) after log2-transformation. The relative abundances are computed from this file. Rows without me/ac suffix represents the amount of unmodified peptide for the considered site. > processed_cleaned_swab_log2intensities.tsv: idem from buccal cells > rel_abundance_PTM_PBMC.tsv: relative abundance computed per precursor, e.g. for a given sample, the H3_K4+H3_K4me1+H3_K4me2+H3_K4me3 <br>relative abundance values must sum to 100, with the relative abundance of H3_K4 representing the absence of modified K4. > rel_abundance_PTM_swab.tsv: idem from buccal cells > tests_from_rel_abundance_PTM_swab_PBMC.tsv: per type of samples ("Sample.origin", i.e.swab of PBMC) and per PTM (rows, "PTM"), report <br>the output of classic (p-values, adjusted with Benjamini-Hochberg (BH), or Benjamini-Yekutieli procedure (BY), from raw and arcsin square <br>root transformed percentage) and PLS-DA tests (VIP - Variable Importance score - and its 95% confidence interval). The percentage of change<br>of each PTM after therapy relative tobefore therapy is reported in "perc_change.AT.over.BT" column. The "is_candidate" indicates if the PTM has been considered as a hit in the swab or PBMC. <strong># Plasma</strong> Samples´ names are encoded as PLASMA_AT_8_batch1, i.e. cells origin_time upon therapy_ID_batch. NA indicates an undetected intensity. > raw_plasma_maxquant_log2ibaq_intensities.tsv: raw data from protein group MaxQuant file. The iBAQ columns are used in later steps. > processed_cleaned_plasma_log2intensities.tsv: processed (imputed, batch-corrected) intensity of protein groups after log2-transformation. > tests_from_intens_plasma.tsv: per protein group ("Proteins.ID"), report the output of classic (p-values, adjusted Benjamini-Hochberg (BH),<br>or Benjamini-Yekutieli procedure (BY), from log2-transformed intensities) and PLS-DA tests (VIP and its 95% confidence interval). The log2 <br>fold change after therapy relative to before therapy is reported in "log2FC.AT.over.BT" column. The "is_candidate" indicates if the protein group has been considered as a hit. <strong># Integration</strong> > circos_input: output of DIABLO analysis with correlation threshold set to 0.7. Use the readRDS R function to open.</pre> <p> </p>
Dataset Multifaceted intervention for patients admitted to an emergency unit for suicide attempt: an exploratory study
<p>This dataset is related to "Multifaceted intervention for patients admitted to an emergency unit for suicide attempt: an exploratory study" (Brovelli S., Dorogi Y., Feiner A.-S., Golay P., Stiefel F., Bonsack C. & Michaud L.)</p>
An exploratory study of consumers' familiarity, attitude, and purchase intentions for lignin-based sunscreens and bio-based skincare products [dataset]
<p>Dataset for <em>An exploratory study of consumers’ familiarity, attitude, and purchase intentions for lignin-based sunscreens and bio-based skincare products.</em></p>
Ethical Perspectives in AI: A Two-folded Exploratory Study From Literature and Active Development Projects - Supplementary Material
<p>This is the Supplementary Material provided for the work accepted on HICSS 2021.</p> <p>Title of work: Ethical Perspectives in AI: A Two-folded Exploratory Study From Literature and Active Development Projects.</p> <p> </p> <p> </p> <p>This dataset depicts 589 GitHub README files explored in our article. We devised each repository into 4 categories. AI Applications (78), reference lists (486), Explainable AI tool (6), Ethical AI tool (15). Moreoveer, 4 repositories could not be found, and 181 of them had a Programming Language. In addition, we classified them according to our judgement of Interesting (82) or Great (33), regarding the objectives of our work. 75 repositories were not related to AI Ethics, and 7 of them mentioned COVID-19 in their repositories in some manner. Highlighted repositories in red pertain to categories 3 and 4, that is, tools for implementing AI Ethics, while those in yellow are marked as “Great”.</p> <p>Explainable AI tool and Ethical AI tool were added to 21 occurrences of tools for implementing AI ethics publicly available in repositories. Furthermore, it is seen that many papers open-sourced their codes on GitHub (as in https://github.com/lopusz/awesome-interpretable-machine-learning), meaning that the academic field has also made good progress in implementing ethics in AI, hence, the combined energy of both sources fosters an enhanced debate and stimulates progress towards AI ethics in practice.</p>
An Exploratory Study of Documentation Strategies for Product Features in Popular GitHub Projects [Replication Package]
<h2>Artefact Summary</h2> <p>This repository contains the replication package for the paper 'An Exploratory Study of Documentation Strategies for Product Features in Popular GitHub Projects,' presented at the <em><a href="https://cyprusconferences.org/icsme2022/" target="_blank" rel="noopener">38th IEEE International Conference on Software Maintenance and Evolution (ICSME'22)</a></em>.</p> <p>The purpose of the package is to facilitate the verification and reproduction of the study results.<br>It provides all computational notebooks used to collect and analyse data, as well as the slides of the conference presentation.</p> <h2>Paper Abstract</h2> <p>[Background] In large open-source software projects, development knowledge is often fragmented across multiple artefacts and contributors such that individual stakeholders are generally unaware of the full breadth of the product features. However, users want to know what the software is capable of, while contributors need to know where to fix, update, and add features. [Objective] This work aims at understanding how feature knowledge is documented in GitHub projects and how it is linked (if at all) to the source code. [Method] We conducted an in-depth qualitative exploratory content analysis of 25 popular GitHub repositories that provided the documentation artefacts recommended by GitHub’s Community Standards indicator. We extracted strategies used to document software features in textual artefacts and which strategies were used to link the feature documentation with source code. [Results] We observed feature documentation in all studied projects in artefacts such as READMEs, wikis, and website resource files. However, the features were often described in an unstructured way. Additionally, tracing techniques to connect feature documentation and source code were rarely used. [Conclusions] Our results suggest a lacking (or a low-prioritised) feature documentation in open-source projects, little use of normalised structures, and a rare explicit referencing to source code. As a result, product feature traceability is likely to be very limited, and maintainability to suffer over time.</p> <h2>References</h2> <p>The published paper is available on <a href="https://doi.org/10.1109/ICSME55016.2022.00043" target="_blank" rel="noopener">IEEE Xplore</a> and the preprint on <a href="https://doi.org/10.48550/arXiv.2208.01317" target="_blank" rel="noopener">arXiv</a>.</p>
Understanding Digits in Identifier Names: An Exploratory Study
<p>This is the dataset that accompanies the study: "<strong>Understanding Digits in Identifier Names: An Exploratory Study</strong>." This study has been accepted for publication at: The 1<sup>st</sup> International Workshop on Natural Language-based Software Engineering (NLBSE 2022).</p> <p> </p> <p><strong><em>Following is the abstract of the study:</em></strong></p> <p>Before any software maintenance can occur, developers must read the identifier names found in the code to be maintained. Thus, high-quality identifier names are essential for productive program comprehension and maintenance activities. With developers free to construct identifier names to their liking, it can be difficult to automatically reason about the quality and semantics behind an identifier name. Studying the structure of identifier names can help alleviate this problem. Existing research focuses on studying words within identifiers, but there are other symbols that appear in identifier names-- such as digits. This paper explores the presence and purpose of digits in identifier names through an empirical study of 800 open-source Java systems. We study how digits contribute to the semantics of identifier names and how identifier names that contain digits evolve over time through renaming. We envision our findings improving the efficiency of name appraisal and recommendation tools and techniques.</p> <p> </p>
Refactoring Debt: Myth or Reality? An Exploratory Study on the Relationship Between Technical Debt and Refactoring
<p>This is the dataset that accompanies the study: "<strong>Refactoring Debt: Myth or Reality? An Exploratory Study on the Relationship Between Technical Debt and Refactoring</strong>." This study has been accepted for publication at the 2022 Mining Software Repositories Conference.</p> <p><strong><em>Following is the abstract of the study:</em></strong></p> <p>To meet project timelines or budget constraints, developers intentionally deviate from writing optimal code to feasible code in what is known as incurring \textit{Technical Debt} (TD). Furthermore, as part of planning their correction, developers document these deficiencies as comments in the code (i.e., self-admitted technical debt or SATD). As a means of improving source code quality, developers often apply a series of refactoring operations to their codebase. In this study, we explore developers repaying this debt through refactoring operations by examining occurrences of SATD removal in the code of 76 open-source Java systems. Our findings show that TD payment usually occurs with refactoring activities and developers refactor their code to remove TD for specific reasons. We envision our findings supporting vendors in providing tools to better support developers in the automatic repayment of technical debt.</p> <p> </p>
Replication package for "An Exploratory Study on the Predominant Programming Paradigms in Python Code"
<p>This dataset includes scripts and data files used to generate all analysis and results from the paper. A README.md file is included for details on using the scripts - though all of the data the scripts generate should already be cached and none of the scripts actually need run.</p> <p>It also includes a spreadsheet containing the human judgements from Table 4 of the paper.</p> <p>Always current source for the scripts is available on GitHub: <a href="https://github.com/psybers/python-paradigms">https://github.com/psybers/python-paradigms</a></p>
Data and R script for 'Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study'
<p>One raw data file and one R script that reproduces all analyses and figures reported in the paper '<strong>Opportunistic food consumption in relation to childhood and adult food insecurity: An exploratory correlational study</strong>' by Nettle et al. </p>
Replication Package for "Type Inference in Kotlin: An Exploratory Study of Developer Usage Patterns"
<p>This dataset includes scripts and data files used to generate all analysis and results from the paper. A <strong>README.md</strong> file is included for details on using the scripts.</p> <p>The dataset is quite large. It is broken down into three archives. All scripts are in <strong>replication-pkg.zip</strong> and the other 2 files only contain data. So if you want to just inspect the analysis, you only need that single zip.</p> <p>If you grab the <strong>data-cache.zip</strong> file and extract it, it will need around 1GB of space. This is the processed dataset stored in Parquet files. Use this if you want to just recreate the tables/figures from the paper.</p> <p>If you want to make changes to the analyses, you will need the raw data in <strong>data.zip</strong>. This will need around 13GB of space once extracted. If you then generate the CSV files from those TXT files (which you will need to do for any custom analysis), you will need an additional 12GB of space.</p>
Supplemental material for: Software System Testing assisted by Large Language Models: An Exploratory Study
<p>This is the supplemental material of the paper titled as “Software System Testing Assisted by Large Language Models: An Exploratory Study” presented at the 36th International Conference on Testing Software and Systems.</p> <p>It contains the raw execution data generated by both models, GPT-4o and GPT-4omini, during the exploratory study. The supplementary material includes the following files:</p> <ul> <li><em>GPT-4ominiRQ1-2ExecutionData.zip</em>: contains the JSON outputs from the OpenAI API for the GPT-4o mini model. Each output is labeled according to the research question number and the corresponding timestamp (for RQ1) or the requested test case (for RQ2), all provided in plain text format.</li> <li><em>GPT-4oRQ1-2ExecutionData.zip</em>: contains the JSON outputs from the OpenAI API for the GPT-4o model. Like the previous file, each output is named in plain text format based on the research question number and timestamp (for RQ1) or the requested test case (for RQ2).</li> </ul> <p>To cite this work: </p> <p>C. Augusto, J. Morán, A. Bertolino, C. de la Riva and J. Tuya, “S<em>oftware System Testing assisted by Large Language Models: An Exploratory Study</em>”, in <em>Testing Software and Systems</em> (pp. 239–255). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-80889-0_17</p>
An Exploratory Study on the Effect of Virtual Environments on Cognitive Performances and Psychophysiological Responses
<p>This is the dataset of the following study: </p> <p>Frigione, I., Massetti, G., Girondini, M., Etzi, R., Scurati, G. W., Ferrise, F., Chirico, A., Gaggioli, A., & Gallace, A. (2022). An Exploratory Study on the Effect of Virtual Environments on Cognitive Performances and Psychophysiological Responses. <em>Cyberpsychology, behavior and social networking</em>, <em>25</em>(10), 666–671. https://doi.org/10.1089/cyber.2021.0162</p> <p>Abstract: Research shows that reduced exposure to natural contexts is associated with an increase in psychophysical disorders. Recent evidence suggests that even a brief experience in natural scenarios can positively affect people's health and well-being. However, natural contexts are not always easily accessible. This study investigates the effects of natural and indoor virtual environments (VREs) on psychophysiological and cognitive responses. Following a within-subject design, 34 healthy participants were exposed to two VREs (i.e., a forest and a living room) in a counterbalanced order through a head-mounted display (Oculus Rift). Participants were asked to explore the scenarios and execute a modified version of the Paced Auditory Serial Addition Test. Physiological parameters (heart rate, skin conductance level [SCL], and respiration rate) were recorded during the whole session. After the exposure to VREs, participants filled a set of visual analog scales to rate their subjective experience of presence, relaxation, and stress. Participants reported a higher perceived sense of relaxation in the virtual forest. Moreover, their SCLs were significantly higher in this environment, showing that the forest elicited higher physiological arousal than the living room. Furthermore, their SCLs were significantly higher during the attentional task in the virtual living room. The results suggest that a natural virtual environment can make people feel more relaxed and physiologically engaged than an indoor scenario. The latter instead can be linked to a performing venue, as reported for real contexts. However, these changes were not related to modulations of attentional performance.</p>
Rename Chains: An Exploratory Study on the Occurrence and Characteristics of Identifiers Undergoing Multiple Renamings
<p>This is the dataset that accompanies the study: "<strong>Rename Chains: An Exploratory Study on the Occurrence and Characteristics of Identifiers Undergoing Multiple Renamings</strong>." This study has been accepted for publication at the 2022 International Workshop on Refactoring.</p> <p><strong><em>Following is the abstract of the study:</em></strong></p> <p>Identifier names play a significant role in program comprehension activities, with high-quality names improving developer productivity and system quality. To correct poor-quality names, developers rename identifiers to reflect their intended purpose better. However, renames do not always result in high-quality, long-lasting names; in many cases, developers perform multiple rename operations on the same identifier throughout the system's lifetime. In this paper, we report on a large-scale empirical study that examines the occurrence of identifiers undergoing multiple renames (i.e., rename chains). Our findings show the presence of rename chains in almost every project, with methods typically having more rename chains than other identifier types. Furthermore, it is usually the same developer responsible for creating all renames within a chain, with most names maintaining the same grammatical structure. Understanding rename chains can help us provide stronger advice, and targeted research, on how to craft high-quality, long-lasting identifiers.</p>
Replication Package for "Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study"
<p>This dataset contains scripts and data used to generate relevant results for this paper. Detailed information are described in README.md. </p> <p>code</p> <p>This folder contains all the scripts used for the experiment. The upgrade.py and downgrade.py are used to perform upgrade and downgrade runs. The pairing.py is used to generate the DFVC pairs. The main.py is used to identify root causes of DFVC pairs.</p> <p>result</p> <p>This folder contains all the results of the experiments, including the runtime output (e.g., a_1.0.0.txt), the runtime environment (e.g., condalist_1.0.0.txt), and the project's runtime commands (e.g., pytorch-cifar.xlsx) of all tested 90 PyTorch and 50 TensorFlow projects.</p> <p><br> Distribution of dfvc pairs.xlsx</p> <p>This file includes 6,926 DFVC pairs and their root causes.</p> <p>Tested framework versions.xlsx</p> <p>This file includes the framework versions tested and the Python versions that the framework versions are compatible with.</p> <p>Tested projects.xlsx</p> <p>This file includes the tested 90 PyTorch projects and 50 TensorFlow projects. We provide the following main information: (a) project name, (b) stars, (c) link, (d) the starting version, (e) python version, (f) incompatible upgrade/downgrade version, and (g) compatible versions.</p>
Understanding the Nature of System-Related Issues in Machine Learning Frameworks: An Exploratory Study
<p>Modern systems are built using development frameworks. The infrastructure provided by these frameworks have a major impact on how the developed system executes, how configurations are managed, how it is tested, and how and where it is deployed. Machine learning (ML) systems have revolutionized multiple industries and come with different kinds of frameworks. Naturally, the issues that manifest in such systems may differ as well---as may the behavior of developers correcting those issues. We are interested in characterizing the types of system-related issues---issues impacting performance, memory and resource usage, and other quality attributes---that emerge in machine learning frameworks, and how they differ from those in traditional frameworks. To this end, we have conducted a large-scale exploratory study analyzing real-world system-related issues from 10 popular machine learning frameworks.<br> <br> Our findings offer a number of interesting observations, with implications for the development of machine learning systems, including differences in the frequency of occurrence of certain issue types, observations regarding the impact of debate and time on issue correction, and differences in the specialization of developers. We hope that this exploratory study will enable developers to improve their expectations, plan for risk, and allocate resources accordingly when making use of the tools provided by these frameworks to develop ML-based systems.</p>
Exploratory analysis using machine learning of predictive factors for falls in persons with type 2 diabetes: A Longitudinal Study
<p>The risk of falls in elderly individuals with diabetes was reported to be 1.5 - 3 times higher than in those without diabetes. However, it is not clear what risk factors are strongly related to falls in those with diabetes. In this study, we aimed to investigate the status of falls and to identify important risk factors for falls in persons with type 2 diabetes (T2D) including the non-elderly. Participants were 316 persons with T2D who were admitted to the University of Tsukuba Hospital for treatment of diabetes. They were assessed for medical history, laboratory data and physical capabilities during the hospitalization and were given a questionnaire on falls one year after discharge. Two different statistical models, logistic regression and random forest classifier, were used to investigate important predictors of falls. The response rate to the survey was 72%; of the 226 respondents, there were 129 males and 97 females (median age 62 years). The fall rate during the first year after discharge was 19% and increased with age; fall rates were 17% for those <60 years, 20% for those aged 60 – 69 years and 24% for those ≥70 years. Logistic regression revealed that knee extension strength (β= -0.698, P = 0.002), fasting C-peptide (F-CPR) level (β= 0.492, P = 0.009) and dorsiflexion strength (β= -0.432, P = 0.047) were independent predictors of falls. The random forest classifier placed knee extension strength (covariate importance = 0.304), grip strength (0.234), F-CPR level (0.232) and dorsiflexion strength (0.230) in the top 4 important variables for falls. The rate of falls in persons with T2D was high even in middle age. Lower extremity muscle weakness as well as elevated F-CPR levels and reduced grip strength were shown to be important risk factors for falls in T2D.</p>
Classifying the Correctness of Generated White-Box Tests: An Exploratory Study
<p>White-box test generator tools rely only on the code under test to select test inputs, and capture the implementation's output as assertions. If there is a fault in the implementation, it could get encoded in the generated tests. Tool evaluations usually measure fault-detection capability using the number of such fault-encoding tests. However, these faults are only detected, if the developer can recognize that the encoded behavior is faulty. We designed an exploratory study to investigate how developers perform in classifying generated white-box test as faulty or correct. We carried out the study in a laboratory setting with 54 graduate students. The tests were generated for two open-source projects with the help of the IntelliTest tool. The performance of the participants were analyzed using binary classification metrics and by coding their observed activities. The results showed that participants incorrectly classified a large number of both fault-encoding and correct tests (with median misclassification rate 33% and 25% respectively). Thus the real fault-detection capability of test generators could be much lower than typically reported, and we suggest to take this human factor into account when evaluating generated white-box tests.<br> <br> This material contains the dataset and the recorded videos of the study.</p>
Does external pneumatic compression treatment between bouts of overreaching resistance training sessions exert differential effects on molecular signaling and performance-related variables compared to passive recovery? An exploratory study
<p>Supplemental Information: Data set respective to manuscript published in PLOS ONE titled "<strong>Does external pneumatic compression treatment between bouts of overreaching resistance training sessions exert differential effects on molecular signaling and performance-related variables compared to passive recovery? An exploratory study" </strong></p>
Early, typical, and late talkers: an exploratory study on predictors of language development in the first two years of life
<p>Background: The consensus in scientific literature is that each child undergoes a unique linguistic development path, albeit with shared developmental stages. Some children excel or lag behind their peers in language skills. Consequently, a key challenge in language acquisition research is pinpointing factors influencing individual differences in language development.</p> <p>Methods: We observed children longitudinally from 3 to 24 months of life to explore early predictors of vocabulary size. Based on the productive vocabulary size of children at 24 months, 30 children met our sample selection criteria: 10 late talkers and 10 early talkers, and we compared them with 10 typical talkers. We evaluated interactive behaviors at 3, 6, 9 and 12 months, considering vocal production, gaze at mother's face, and gestural production during mother-child interactions, and we considered mothers' report of children's actions and gestures and receptive-vocabulary size at 15 and 18 months.</p> <p>Results: Results indicated early precursors of language outcome at 24 months identifiable as early as 3 months in vocal productions, 6 months for gaze at mother's face and 12 months for gestural productions.</p> <p>Conclusions: Our research highlights both theoretical and practical implications. Theoretically, identifying the early indicators of belonging to the group of late or early talkers underscores the significant role of this developmental period for future studies. On a practical note, our findings emphasize the crucial need for early investigations to identify predictors of vocabulary development before the typical age at which lexical delay is identified.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.