cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4524785
title: Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection

description: Data Package for "Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection" This package contains data generated as part of our experiments on adaptive fitness function selection as part of unit test generation for Java systems. This paper is currently under submission. A draft of the paper is included in the data package. This package contains experimental data (in folder "experiment_data"), including goal attainment, fault detection, time per generation, and choices made by the reinforcement learning algorithm. In the folder "test_suites", the suites generated by each technique are included for each project. If you have questions, please contact Gregory Gay at greg@greggay.com. NOTE: A small number of items are currently missing from this data package and will be added shortly. Please make sure you have the latest version of this package.
description: This research was supported by Vetenskapsrådet grant 2019-05275.
DOI: 10.5281/zenodo.3786191
title: Understanding the Nature of System-Related Issues in Machine Learning Frameworks: An Exploratory Study

description: 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.
DOI: 10.5281/zenodo.3755327
title: Understanding the Differences in the Unit Tests Produced by Humans and Coverage-Directed Automated Generation

description: Automated test generation - the use of tools to create all or part of test cases - has a critical role in controlling the cost of testing. A particular area of focus in automated test generation research is unit testing. Unit tests are intended to test the functionality of a small isolated unit of code - typically a class. In automated test generation research, it is not abnormal to compare the effectiveness of the test cases generated by automation to those written by humans. Indeed, a common premise of automation research - implicitly or explicitly - is that effective automation can replace human effort. The hypothesis postulated is that, if we make enough advances, a tool could replace the tremendous effort expended by a human tester to create those unit tests. This observation leads to two natural questions. Do the tests produced by humans and automation differ in the types of faults they detect? If so, in what ways are the tests produced and the faults detected different? Understanding when and how to deploy automation requires a clearer understanding of how the tests produced by humans and automation are different, and how those differences in turn affect the ability of those test cases to detect faults. Insight into the differences between human and automation-produced test cases could lead not only to improvements in the ability of automation to replace human effort, but improvements in our ability to use automation to augment human effort. The goal of this study is to explore and attempt to quantify those differences. In this study, we make use of the EvoSuite test generation framework for Java. We generate test suites targeting two configurations - a traditional single-criterion configuration targeting Branch Coverage over the source code and a more sophisticated multi-objective configuration targeting eight criteria. Controlling for coverage level, we compare the suites generated by EvoSuite to those written by humans for five mature, popular open-source systems in terms of both their syntactic structure and their ability to detect 45 different types of faults. Our goal is not to declare a "winner'", but to identify the areas where humans and automation differ in their capabilities, and - in turn - to make recommendations on how human and automation effort can be combined to overcome gaps in the coverage of the other. We aim to identify lessons that will improve human practices, lead to the creation of more effective automation, and present natural opportunities to both augment and replace human effort.
DOI: 10.5281/zenodo.3755326
title: Understanding the Differences in the Unit Tests Produced by Humans and Coverage-Directed Automated Generation

description: Automated test generation - the use of tools to create all or part of test cases - has a critical role in controlling the cost of testing. A particular area of focus in automated test generation research is unit testing. Unit tests are intended to test the functionality of a small isolated unit of code - typically a class. In automated test generation research, it is not abnormal to compare the effectiveness of the test cases generated by automation to those written by humans. Indeed, a common premise of automation research - implicitly or explicitly - is that effective automation can replace human effort. The hypothesis postulated is that, if we make enough advances, a tool could replace the tremendous effort expended by a human tester to create those unit tests. This observation leads to two natural questions. Do the tests produced by humans and automation differ in the types of faults they detect? If so, in what ways are the tests produced and the faults detected different? Understanding when and how to deploy automation requires a clearer understanding of how the tests produced by humans and automation are different, and how those differences in turn affect the ability of those test cases to detect faults. Insight into the differences between human and automation-produced test cases could lead not only to improvements in the ability of automation to replace human effort, but improvements in our ability to use automation to augment human effort. The goal of this study is to explore and attempt to quantify those differences. In this study, we make use of the EvoSuite test generation framework for Java. We generate test suites targeting two configurations - a traditional single-criterion configuration targeting Branch Coverage over the source code and a more sophisticated multi-objective configuration targeting eight criteria. Controlling for coverage level, we compare the suites generated by EvoSuite to those written by humans for five mature, popular open-source systems in terms of both their syntactic structure and their ability to detect 45 different types of faults. Our goal is not to declare a "winner'", but to identify the areas where humans and automation differ in their capabilities, and - in turn - to make recommendations on how human and automation effort can be combined to overcome gaps in the coverage of the other. We aim to identify lessons that will improve human practices, lead to the creation of more effective automation, and present natural opportunities to both augment and replace human effort.
DOI: 10.5281/zenodo.3786190
title: Understanding the Nature of System-Related Issues in Machine Learning Frameworks: An Exploratory Study

description: 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.
DOI: 10.5281/zenodo.3484641
title: Understanding The Impact of Solver Choice in Model-Based Test Generation

description: <strong># Solver Experiment - Data Package</strong> This repository is intended to allow replication and extension of the study<br> conducted in the following paper: Ying Meng and Gregory Gay. Understanding The Impact of Solver Choice in Model-Based Test Generation. To appear, 2020 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). Available from https://greg4cr.github.io/pdf/20solvers.pdf. This package includes all data used or generated in this experiment.<br> * Models<br> * Mutants<br> * Test Suites<br> * Execution Traces<br> * Fault-detection Results If you have any questions on this data, please contact us at greg@greggay.com. <br>
DOI: 10.5281/zenodo.3484640
title: Understanding The Impact of Solver Choice in Model-Based Test Generation

description: <strong># Solver Experiment - Data Package</strong> This repository is intended to allow replication and extension of the study<br> conducted in the following paper: Ying Meng and Gregory Gay. Understanding The Impact of Solver Choice in Model-Based Test Generation. To appear, 2020 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). Available from https://greg4cr.github.io/pdf/20solvers.pdf. This package includes all data used or generated in this experiment.<br> * Models<br> * Mutants<br> * Test Suites<br> * Execution Traces<br> * Fault-detection Results If you have any questions on this data, please contact us at greg@greggay.com. <br>
DOI: 10.23708/kmj4cc
title: User centric metadata model

description: The user centric metadata model aims to foster multi-disciplinary research, describing dataset of different disciplines in the same manner. It is able to favour the user viewpoint using the observation paradigm, adopted by the Observation data model, for specifying the dataset content. The advantages of the proposed metadata model are that the model specifies the semantics of the involved entities and links the technical description, usually provided by existing metadata models, with the thematic description. In this way, a researcher, regardless of his/her expertise, can discover and, eventually, correctly reuse datasets of any discipline. Following the FAIR principles, the proposed metadata model reuses as much as possible existing models, ontologies and vocabularies.
DOI: 10.5281/zenodo.5482783
title: Quality and Trackability of Author Indicated Software Citations (NEST Case Study)

description: This dataset contains a randomly selected minimal sample of 471 NEST software citations, which were provided by authors and published on the NEST software page as a publication list. The software citations were analyzed on their quality and trackability. The data was collected in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.
DOI: 10.5281/zenodo.5482782
title: Quality and Trackability of Author Indicated Software Citations (NEST Case Study)

description: This dataset contains a randomly selected minimal sample of 471 NEST software citations, which were provided by authors and published on the NEST software page as a publication list. The software citations were analyzed on their quality and trackability. The data was collected in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.
DOI: 10.5281/zenodo.5482798
title: Gnathonemus echidnorhynchus SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5482797
title: Gnathonemus echidnorhynchus SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5482840
title: Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification.

description: We uploaded the database of 104 lesions included in the manuscript: Fusco R, Piccirillo A, Sansone M, Granata V, Rubulotta MR, Petrosino T, Barretta ML, Vallone P, Di Giacomo R, Esposito E, Di Bonito M, Petrillo A. Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification. Diagnostics (Basel). 2021 Apr 30;11(5):815. doi: 10.3390/diagnostics11050815. PMID: 33946333; PMCID: PMC8146084.
DOI: 10.5281/zenodo.5482839
title: Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification.

description: We uploaded the database of 104 lesions included in the manuscript: Fusco R, Piccirillo A, Sansone M, Granata V, Rubulotta MR, Petrosino T, Barretta ML, Vallone P, Di Giacomo R, Esposito E, Di Bonito M, Petrillo A. Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification. Diagnostics (Basel). 2021 Apr 30;11(5):815. doi: 10.3390/diagnostics11050815. PMID: 33946333; PMCID: PMC8146084.
DOI: 10.5281/zenodo.5482838
title: HTML Status Codes of Publications citing ICPSR

description: This dataset contains the HTML status codes of ICPSR citing literature, which were tested in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.
DOI: 10.5281/zenodo.5482837
title: HTML Status Codes of Publications citing ICPSR

description: This dataset contains the HTML status codes of ICPSR citing literature, which were tested in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.
DOI: 10.5281/zenodo.5482480
title: OpenGHGMap - Europe - CO2 Emissions in 108,000 European Cities

description: CO2 emissions for 116,000 administrative areas (108,000 cities/municipalities) in 34 European countries. Emissions are for the year 2018. The model homepage is https://openghgmap.net/
DOI: 10.5281/zenodo.5482479
title: OpenGHGMap - Europe - CO2 Emissions in 108,000 European Cities

description: CO2 emissions for 116,000 administrative areas (108,000 cities/municipalities) in 34 European countries. Emissions are for the year 2018. The model homepage is https://openghgmap.net/
DOI: 10.5281/zenodo.5482848
title: Citation Location, Elements and Purpose of ICPSR Research Data Citations

description: The dataset contains an analysis of a randomly chosen sample of 1,073 publications that cite ICPSR research datasets. The collected research data (re)use indications were analyzed according to their location in the full-text, their metadata elements, and their citation purpose. The data was collected and analyzed in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.
DOI: 10.5281/zenodo.5482847
title: Citation Location, Elements and Purpose of ICPSR Research Data Citations

description: The dataset contains an analysis of a randomly chosen sample of 1,073 publications that cite ICPSR research datasets. The collected research data (re)use indications were analyzed according to their location in the full-text, their metadata elements, and their citation purpose. The data was collected and analyzed in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.
DOI: 10.18112/openneuro.ds001420.v1.1.0
title: [11C]DASB PET Cimbi database example
DOI: 10.18112/openneuro.ds001421.v1.1.0
title: [11C]SB207145 PET Cimbi database example
DOI: 10.5281/zenodo.5482866
title: PGxMine

description: This describes the output files for the PGxMine project. The code for this viewer is available in the PGxMine Github repo if you want to run it independently. Each file is a tab-delimited file with a header, no comments and no quoting. You likely want <strong>pgxmine_collated.tsv</strong> if you just want the list of chemical/variant assocations. If you want the supporting sentences, look at <strong>pgxmine_sentences.tsv</strong>. You can use the <em>matching_id</em> column to connect the two files. If you want to dig further and are okay with a higher false positive rate, look at <strong>pgxmine_unfiltered.tsv</strong>. <strong>pgxmine_collated.tsv:</strong> This contains the chemical/variant associations with citation counts supporting them. It contains the normalized chemical, variant and where appropriate gene names with identifiers for PharmGKB, dbSNP and Entrez. <strong>pgxmine_sentences.tsv:</strong> This contains the supporting sentences for the chemical/variant associations in the collated file. Each row is a single supporting sentence for one association. This file contains information on the source publication (e.g. journal, publication date, etc), the actual sentence and the chemical/variant association extracted. <strong>pgxmine_unfiltered.tsv:</strong> This is the combined raw output of the createKB.py script across all of PubMed, Pubmed Central Open Access and PubMed Central Author Manuscript Collection. It contains every predicted relation with a prediction score above 0.5. So this may contain many false positives. Each row contain information on the publication (e.g. journal, publication date, etc) along with the sentence and the specific chemical/variant association.
DOI: 10.5281/zenodo.5176109
title: FAAH inhibition in migraine pain

description: This dataset comprises the findings obtained in the study aimed at investigating the relevance of fatty-acid amide hydrolase (FAAH) inhibition in the nitroglycerin (NTG) animal model of migraine, by testing the effects of the globally active FAAH inhibitor URB597. The attention was focused on mapping neuronal c-Fos protein expression, measurement of anandamide (AEA) and palmitoylethanolamide (PEA) levels in brain areas and in trigeminal ganglia, evaluation of pain-related trigeminal behavior and quantification of molecular mediators in rats that received URB597 (2 mg/kg i.p.) either before or after NTG administration (10 mg/kg, i.p.). Evaluation of c-Fos expression by immunohistochemistry: cell counts of individual brain nuclei were made from every sixth section throughout their rostrocaudal extent for each rat. The nuclei evaluated were identified with the rat brain atlas Paxinos and Watson 4th edition. Trigeminal nucleus caudalis (TNC): from bregma −14.08 mm (interaural −5.08 mm) until −17 mm (interaural −8 mm); nucleus tractus solitarii (NTS): from bregma −13.24 mm (interaural −4.24 mm) until −13.68 mm (interaural −4.68 mm); locus ceruleus (LC): from bregma −9.80 mm (interaural −0.80 mm) until −10.04 mm (interaural −1.04 mm); parabrachial nucleus (PAB): from bregma −8.80 mm (interaural 0.20 mm) until −9.80 mm (interaural −0.50 mm); periaqueductal gray (PAG): from bregma −7.80 mm (interaural 1.20 mm) until −8.80 mm (interaural 0.20 mm). Measurement of AEA and PEA levels: medulla, cervical spinal cord and trigeminal ganglia were homogenized in cold methanol (2 ml) containing AEA-d4 and PEA-d4 as internal standards. After extraction the lipids were measured using a Xevo TQ UPLC-MS/MS system equipped with a reversed-phase BEH C18 column (2.1 × 50 mm, 1.7 μm particle size) (Waters, Milford, USA). Pain-related behavior in the orofacial formalin test: the face rubbing was measured counting the seconds the animal spent grooming the injected area (upper lip, lateral to the nose) with the ipsilateral forepaw or hindpaw 0–6 min (Phase I) or 12–45 min (Phase II) after formalin injection (50 µl, s.c.). The observation time was divided into 15 blocks of 3 min each. mRNA expression levels: calcitonin gene-related peptide (CGRP), neuronal nitric oxide synthase (nNOS), interleukin-1β (IL-1β) and tumor necrosis factor-alpha (TNF-alpha) mRNA were evaluated in in medulla, cervical spinal cord and trigeminal ganglia; whereas, in dura mater were evaluated IL-1β and inducible NOS (iNOS). mRNA levels were measured by rt-PCR. All samples were assayed in triplicate and gene expression levels were calculated according to 2−∆∆Ct = 2− (∆Ct gene − ∆Ct housekeeping gene) formula by using Ct (cycle threshold) values.
description: Funding: Italian Ministry of Health to IRCCS Mondino Foundation, Pavia, Italy (RF2013-02355704)