Find research datasets worth reusing

opencc-by-4.0May 2022View details →

UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)

Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.

opencc-by-4.0Mar 2022View details →

Dataset for the development of a "Flow cell for operando X-ray photon-in-photon-out studies on photo-electrochemical thin film devices"

All raw experimental data concerning this published ChemRxiv manuscript: https://doi.org/10.26434/chemrxiv.12382529.v1 which is now published slightly revised here: https://doi.org/10.12688/openreseurope.14433.1 The dataset comprises raw data, analysis and short description: - the CAD-files of the operando cell - the grazing-incidence X-ray diffraction (GIXRD) files of the as-synthesized thin film samples - the high-energy resolution fluoresence-detected (HERFD) X-ray absorption (XAS) files of thin films during photoelectrochemical experimentation - the photoelectrochemical data gathered during HERFD-XAS experiments - the non-operando photoelectrochemical data on the thin film samples - scanning electron microscopy (SEM) image of a representative thin film cross-section - the extended data contains additional information arranged in a word document that are directly linked to the main manuscript

opencc-by-4.0May 2022View details →

Point-of-care monitoring of head and neck cancer treatment response and recurrence development using nanopore-based ctDNA consensus sequencing

Circulating tumor DNA (ctDNA) in blood may become a generic biomarker for non-invasive cancer diagnosis and monitoring. However, detection of ctDNA is challenged by the presence of many circulating DNA molecules from healthy cells. We found that single ctDNA molecules can be sequenced with high accuracy by a three-step process consisting of capturing, copying and concatenation of the original double-stranded ctDNA molecules. This innovative approach - called CyclomicsSeq -  is unparalleled by any other method in terms of cost-efficiency and speed, allowing point-of-care cancer diagnostics. Within this CPOC, subsidized by the Oncode institute, we have applied our CyclomicsSeq ctDNA test in patients with advanced head and neck cancer squamous cell carcinoma (HNSCC). Head and neck cancer (HNSCC) accounts for 380,000 cancer-related deaths worldwide. For these patients, determining whether a patient responds to the primary chemoradiation treatment is challenging, and non-responders are sometimes identified when other treatment options are no longer possible. By measuring the ctDNA levels in the blood of these patients prior to and during treatment, we aim to identify non-responders at an earlier stage. This dataset contains base calls of TP53 of 47 nanopore sequencing runs. We included 10 patients and 7 controls. For the patients, we have samples of multiple time points (0 = prior to treatment, 1 = 1 week after treatment initiation, etc).

opencc-by-4.0May 2022View details →

Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform

Online Repository of the Study “I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform Introduction In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness.  Our empirical study, carried out in the context of a Mobility as a Service start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services.  To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy.  Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction. Dataset Overview The dataset is composed of the following different sorts of data:  <ul> <li> “Data_and_preprocessing” folder  <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training.  <ul> <li>For this look at the sub-folder “input_data_for_ML” and the following matrices: <ul> <li>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</li> <li>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “randomSamples.csv” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> The following sections describe more in detail what each of those folders and files contain. “Data_and_preprocessing” folder <ul> <li>User-reported issues subset.</li> </ul> In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file randomSamples.csv. The randomSamples.csv a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains: <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final" contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented Three-level taxonomy of e-bike user reported issues; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li>Bag of Words Term by Document matrix.</li> </ul> An important input for training the ML models is the Bag of Words representation generated after processing the  2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder input_data_for_ML where they are labeled for High- and Low-level categories.  In the tf-idf-matrix-of-comment_finals_with_oracle_info.csv and tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−𝑡ℎ term contained in the j−𝑡ℎ user issue by using the tf-idf score. “RQ1” folder <ul> <li>“Three-level taxonomy of e-bike user-reported issues.pdf” file</li> </ul> The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file Three-level taxonomy of e-bike user-reported issues.pdf  presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages. “RQ2” folder <ul> <li>“Trained Models” folder</li> </ul> We provide the trained machine and deep learning models in the sub-folder ML_DL_models. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance.   The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types). <ul> <li>“Model results” folder</li> </ul> In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company.

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Data from: Adaptive division of growth and development between hosts in helminths with two-host life cycles

Parasitic worms (helminths) with complex life cycles divide growth and development between successive hosts. Using data from 597 species of acanthocephalans, cestodes, and nematodes with two-host life cycles, we found that helminths with larger intermediate hosts were more likely to infect larger, endothermic definitive hosts, although some evolutionarily shifts in definitive host mass occurred without changes in intermediate host mass. Life-history theory predicts parasites to shift growth to hosts in which they can grow rapidly and/or safely. Accordingly, helminth species grew relatively less as larvae and more as adults if they infected smaller intermediate hosts and/or larger, endothermic definitive hosts. Growing larger than expected in one host, relative to host mass/endothermy, was not associated with growing less in the other host, implying a lack of cross-host tradeoffs. Rather, some helminth orders had both large larvae and large adults. Within these taxa, though, size at maturity in the definitive host was unaffected by changes to larval growth, as predicted by optimality models. Parasite life-history strategies were mostly (though not entirely) consistent with theoretical expectations, suggesting that helminths adaptively divide growth and development between the multiple hosts in their complex life cycles.

opencc-zeroJun 2022View details →

How to Develop Resilience False Information?

In this video, you will   <ul> <li> Learn about the problems related to false information;  </li> <li> Understand two types of false information: misinformation and disinformation;  </li> <li> Develop an awareness of information literacy;  </li> <li> Discover some strategies to overcome susceptibility to fake news. </li> </ul>

opencc-by-nc-4.0May 2022View details →

The PI-CAI Challenge: Public Training and Development Dataset

This dataset represents the <a href="https://pi-cai.grand-challenge.org/">PI-CAI</a>: Public Training and Development Dataset. It contains 1500 anonymized prostate biparametric MRI scans from 1476 patients, acquired between 2012-2021, at three centers (Radboud University Medical Center, University Medical Center Groningen, Ziekenhuis Groep Twente) based in The Netherlands. The PI-CAI challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at clinically significant prostate cancer (csPCa) detection/diagnosis in MRI, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting. The study hypothesizes that state-of-the-art AI algorithms, trained using thousands of patient exams, are non-inferior to radiologists reading bpMRI. Key aspects of the PI-CAI study design have been established in conjunction with an international scientific advisory board of 16 experts in prostate AI, radiology and urology —to unify and standardize present-day guidelines, and to ensure meaningful validation of prostate AI towards clinical translation (<a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a>).

opencc-by-4.0Jan 2022View details →

Supplementary information for 'Distinct gene expression dynamics in developing and regenerating crustacean limbs', by Sinigaglia et al.

Supplementary data and code for the manuscript 'Distinct gene expression dynamics in developing and regenerating crustacean limbs', by Sinigaglia et al.

Development of a high-density 665 K SNP array for rainbow trout genome-wide genotyping. Supplemental VCF file

Single nucleotide polymorphism (SNP) arrays, also named « SNP chips », enable very large numbers of individuals to be genotyped at a targeted set of thousands of genome-wide identified markers. We used preexisting variant datasets from USDA, a French commercial line and 30X-coverage whole genome sequencing of INRAE isogenic lines to develop an Affymetrix 665 K SNP array (HD chip) for rainbow trout. In total, we identified 32,372,492 SNPs that were polymorphic in the USDA or INRAE databases. A subset of identified SNPs were selected for inclusion on the chip, prioritizing SNPs whose flanking sequence uniquely aligned to the Swanson reference genome, with homogenous repartition over the genome and the highest Minimum Allele Frequency in both USDA and French databases. Of the 664,531 SNPs which passed the Affymetrix quality filters and were manufactured on the HD chip, 65.3% and 60.9% passed filtering metrics and were polymorphic in two other distinct French commercial populations in which, respectively, 288 and 175 sampled fish were genotyped. Only 576,118 SNPs mapped uniquely on both Swanson and Arlee reference genomes, and 12,071 SNPs did not map at all on the Arlee reference genome. Among those 576,118 SNPs, 38,948 SNPs were kept from the  commercially available medium-density 57K SNP chip. We demonstrate the utility of the HD chip by describing the high rates of  linkage disequilibrium at 2 kb to 10 kb in the rainbow trout genome in comparison to the linkage disequilibrium observed at 50 kb to  100 kb which are usual distances between markers of the medium-density chip.   File submitted correspond to the supplementary data 1 of the publication (under submission) : INRAE_USDA_MAF1.vcf.gz

Gastruloids as in vitro models of embryonic blood development with spatial and temporal resolution

Rawdata (images and flow cytometry) for the manuscript "Gastruloids as in vitro models of embryonic blood development with spatial and temporal resolution".

Design Principles for the Development of Gd(III) Polarizing Agents for Magic Angle Spinning Dynamic Nuclear Polarization

This is the raw dataset for publication  Design Principles for the Development of Gd(III) Polarizing Agents for Magic Angle Spinning Dynamic Nuclear Polarization. with the DOI of 10.1021/acs.jpcc.2c01721. It contains all NMR, EPR raw data and the MATLAB codes that are used in this paper. For details, please refer to the readme file.

A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action

This data set pertains to the following research article: Purnell, P.J. (2022) A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13 – Climate Action. arXiv:2201.02006

opencc-by-4.0Jul 2022View details →

Dataset: Development and optimization of a bioelectrochemical system for ammonium recovery from wastewater as fertilizer

Dataset of Development and optimization of a bioelectrochemical system for ammonium recovery from wastewater as fertilizer <a href="https://doi.org/10.1016/j.clet.2021.100142">https://doi.org/10.1016/j.clet.2021.100142</a> <a href="https://www.sciencedirect.com/journal/cleaner-engineering-and-technology">Cleaner Engineering and Technology</a> <a href="https://www.sciencedirect.com/journal/cleaner-engineering-and-technology/vol/4/suppl/C">Volume 4</a>, October 2021, 100142

To What Extent Cognitive-Driven Development Improves Code Readability?

Cognitive-Driven Development (CDD) is a coding design technique that aims to reduce the cognitive effort that developers place in understanding a given code unit (e.g., a class). By following CDD de- sign practices, it is expected that the coding units to be smaller, and, thus, easier to maintain and evolve. However, it is so far unknown whether these smaller code units coded using CDD standards are, indeed, easier to understand. In this work we aim to assess to what extent CDD improves code readability. To achieve this goal, we conducted a two-phase study. We start by inviting professional software developers to vote (and justify their rationale) on the most readable pair of code snippets (from a set of 10 pairs); one of the pairs was coded using CDD practices. We received 133 answers. In the second phase, we applied the state-of-the art readability model on the 10-pairs of CDD-driven refactorings. We observed some conflicting results. On the one hand, developers perceived that seven (out of 10) CDD-driven refactorings were more readable than their counterparts; for two other CDD-driven refactorings, developers were undecided, while only in one of the CDD-driven refactorings, developers preferred the original code snippet. On the other hand, we noticed that only one CDD-driven refactorings have better performance readability, assessed by state-of-the-art readability models. Our results provide initial evidence that CDD could be an interesting approach for software design

opencc-by-4.0Jul 2022View details →

Supplementary Material for Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study

This repository delivers the supplementary material for the paper: Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study. In the following, we present the abstract of the study: Context: Agile Software Development (ASD) is a disruptive process compared to traditional software development. Therefore, traditional Requirements Engineering (RE) forms may not be the best way to do RE for ASD (RE-ASD). Objective: Working with ASD using traditional RE ways could limit ASD's potential. Thus, it is necessary to investigate what academia and industry have done in RE to take full advantage of all of the capabilities of ASD beyond traditional RE. Method: We conducted a Tertiary Study looking for solutions for RE-ASD using the Systematic Literature Review (SLR) protocol described by Kitchenham and Charters. We then categorized the solutions into families using Targeted Coding and Constant Comparison, tools from Socio-Technical Grounded Theory (STGT). Afterward, we classified the solutions as disruptive using our model based on the Hype Level Curve concept, assessing their hype (popularity) in the software engineering community using Google Trends and Google Colab tools. Results: After executing the SLR protocol, we accepted 37 studies and encountered 136 solutions used by academia and industry for RE-ASD. We categorized these solutions into 21 solution families, six of which we classified as disruptive. Design Thinking (DT) and Artificial Intelligence (AI) were the two families of solutions that stood out the most. We also identified the type of solution (e.g., process, method, technique, tool, model, framework) and domain (academia or industry). Furthermore, we cataloged the challenges presented by the solutions. Conclusion: We concluded that only a few solutions that have been used for RE-ASD have the power to successfully challenge the mainstream Agile Software Development process by using innovation (26 out of 106). There is a gap between academia and industry regarding these disruptive solutions, and some challenges still need to be addressed in using these solutions. The repository contains the following: <ul> <li>Dataset from the Tertiary Study: <ul> <li>Data of the retrieved studies. It presents the classifications of the documents as 'Accepted,' 'Rejected' (with the indication of the step of the protocol the authors rejected the study), or 'Duplicated.'</li> <li>Data of all solutions retrieved from the accepted studies</li> </ul> </li> <li>Socio-Technical Grounded Theory (STGT) tools <ul> <li>Result of the use of Targeted Coding and Constant Comparison</li> </ul> </li> <li>The Google Colab Notebook <ul> <li>Code in python</li> <li>Results</li> </ul> </li> </ul>

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Permafrost-thaw lake development in Central Yakutia: sedimentary ancient DNA and element analyses from a Holocene sediment record

In Central Yakutia (Siberia) livelihoods of local communities depend on alaas (thermokarst depression) landscapes and the lakes within. Development and dynamics of these alaas lakes are closely connected to climate change, permafrost thawing, catchment conditions, and land use. To reconstruct lake development throughout the Holocene we analyze sedimentary ancient DNA (sedaDNA) and biogeochemistry from a sediment core from Lake Satagay, spanning the last c. 10,800 calibrated years before present (cal yrs BP). SedaDNA of diatoms and macrophytes and microfossil diatom analysis reveal lake formation earlier than 10,700 cal yrs BP. The sedaDNA approach detected 42 amplicon sequence variants (ASVs) of diatom taxa, one ASV of Eustigmatophyceae (Nannochloropsis), and 12 ASVs of macrophytes. We relate diatom and macrophyte community changes to climate-driven shifts in water level and mineral and organic input, which result in variable water conductivity, in-lake productivity, and sediment deposition. We detect a higher lake level and water conductivity in the Early Holocene (c. 10,700–7000 cal yrs BP) compared to other periods, supported by the dominance of Stephanodiscus sp. and Stuckenia pectinata. Further climate warming towards the Mid-Holocene (7000–4700 cal yrs BP) led to a shallowing of Lake Satagay, an increase of the submerged macrophyte Ceratophyllum, and a decline of planktonic diatoms. In the Late Holocene (c. 4700 cal yrs BP–present) stable shallow water conditions are confirmed by small fragilarioid and staurosiroid diatoms dominating the lake. Lake Satagay has not yet reached the final stage of alaas development, but satellite imagery shows an intensification of anthropogenic land use, which in combination with future warming will likely result in a rapid desiccation of the lake.

opencc-zeroJul 2022View details →

Supplementary figures supporting the corrigendum on the PARASO Geoscientific Model Development paper

Figures supporting the corrigendum to the PARASO GMD paper. In the original version of the code, the COSMO wind stress were not properly rotated from the COSMO grid to NEMO's. figure_windstress_fix.png provides an illustration of the bug, and the other figures are a selection of diagnostics performed with a fixed version of PARASO, sharing the same experimental design and numbering as the original paper. Original paper: Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, <a href="https://doi.org/10.5194/gmd-15-553-2022">10.5194/gmd-15-553-2022</a>, 2022.

Time to run: Late rather than early exercise training in mice promotes fat mass loss and reduces atherosclerosis development

This dataset contains forward and reverse reads of 16S sequencing of the V3-V4 region of caecal bacteria. As indicated in the metadata file, mice are divided into three groups - SED (sedentary mice, E-SED for early sedentary and L-SED for late sedentary), E-RUN (early runners) and L-RUN (late runners). For more information about the groups, sequencing etc., consult the manuscript.

JOSSE: A Software Development Effort Dataset Annotated with Expert Estimates

The JIRA Open-Source Software Effort (JOSSE) dataset consists of software development and maintenance tasks collected from the JIRA issue tracking system for Apache, JBoss, And Spring open-source projects. All the issues were annotated with actual effort and 19% of them were annotated with expert estimates. JOSSE is a task-based dataset with a textual attribute represented as a task description for each data point. This paper explains how the data were collected and details six data quality refinement procedures of the data points.

Supplementary Material for the paper "Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells"

Supplementary material for the paper "Daniel Oliveira; Wesley K. G. Assunção; Alessandro Garcia; Baldoino Fonseca; and Márcio Ribeiro. Developers' Perception Matters: Machine Learning to Detect Developer-sensitive Smells. In: Empirical Software Engineering. 2022. Springer."