cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/kmitl.the.2003.477
title: ผลของการใช้ชุดฝึกการแก้ปัญหาโจทย์คำนวณที่เน้นเทคนิคแผนผังทางปัญญาที่มีต่อผลสัมฤทธิ์ทางการเรียนวิชาเคมี ของนักเรียนชั้นมัธยมศึกษาปีที่ 4 /
DOI: 10.18430/m34yk1
title: X-ray diffraction data for the crystal Structure of the BID Domain of Bep6 from Bartonella rochalimae (4YK1)
DOI: 10.18430/m34yk2
title: X-ray diffraction data for the crystal Structure of the BID Domain of Bep9 from Bartonella clarridgeiae (4YK2)
DOI: 10.18430/m34ywr
title: X-ray diffraction data for the structure of a putative phosphomethylpyrimidine kinase from Acinetobacter baumannii in non-covalent complex with pyridoxal phosphate (4YWR)
DOI: 10.18430/m35i2b
title: X-ray diffraction data for the crystal structure of a peptide deformylase from Burkholderia ambifaria with actinonin (5I2B)
DOI: 10.18430/m35i4m
title: X-ray diffraction data for the crystal structure of Amidase, hydantoinase/carbamoylase family from Burkholderia vietnamiensis (5I4M)
DOI: 10.18430/m35if9
title: X-ray diffraction data for the crystal Structure of Cobyrinic Acid a,c-diamide synthase from Mycobacterium smegmatis with bound ATP analog and Magnesium (5IF9)
DOI: 10.18430/m35ifi
title: X-ray diffraction data for the cRYSTAL STRUCTURE OF ACETYL-COA SYNTHETASE IN COMPLEX WITH ADENOSINE-5'-PROPYLPHOSPHATE FROM CRYPTOCOCCUS NEOFORMANS H99 (5IFI)
DOI: 10.18430/m35ify
title: X-ray diffraction data for the crystal structure of Glucose-1-phosphate thymidylyltransferase from Burkholderia vietnamiensis in complex with 2 -Deoxyuridine-5'-monophosphate and 2'-Deoxy-Thymidine-B-L-Rhamnose (5IFY)
DOI: 10.14457/kmitl.the.2001.9
title: พัลส์โพสิชั่นมอดูเลตชั่นแบบไม่มีสัญญาซิงโครไนซ์ /
DOI: 10.14457/kmitl.the.2002.391
title: การพัฒนาบทเรียนฝึกปฏิบัติตามทักษะความสามารถแบบอิงเกณฑ์ เรื่องทรานซิสเตอร์ /
DOI: 10.18430/m35i1f
title: X-ray diffraction data for the crystal structure of UTP-glucose-1-phosphate uridylyltransferase from Burkholderia vietnamiensis in complex with Uridine-5'-diphosphate-glucose (5I1F)
DOI: 10.18430/m35idx
title: X-ray diffraction data for the crystal structure of an oxidoreductase from Burkholderia vietnamiensis (5IDX)
DOI: 10.18430/m35ie3
title: X-ray diffraction data for the crystal structure of a plant enzyme (5IE3)
DOI: 10.18430/m35if3
title: X-ray diffraction data for the crystal structure of a Short-chain dehydrogenase/reductase SDR from Burkholderia vietnamiensis (5IF3)
DOI: 10.18430/m35ifz
title: X-ray diffraction data for the crystal Structure of Ribose-5-phosphate Isomerase from Brucella melitensis 16M (5IFZ)
DOI: 10.18430/m35ig2
title: X-ray diffraction data for the crystal structure of a short chain dehydrogenase/reductase SDR from Burkholderia phymatum in complex with NAD (5IG2)
DOI: 10.18430/m35ihv
title: X-ray diffraction data for the crystal structure of a beta-lactamase from Burkholderia ambifaria (5IHV)
DOI: 10.18430/m35iz4
title: X-ray diffraction data for the crystal structure of a putative short-chain dehydrogenase/reductase from Burkholderia xenovorans (5IZ4)
DOI: 10.7488/06783dbe-019d-4503-8eb2-2ae1ae85e87e
title: Emma Raftis PDRA Dataset

description: Some data produced during Emma Raftis postdoc looking at phylogenomics of S. aureus host adaptation
DOI: 10.14457/kmitl.the.2002.383
title: การศึกษาความพร้อมในการจัดการเรียนการสอนอาชีวศึกษาทางไกลของกรมอาชีวศึกษา /
DOI: 10.14457/kmitl.the.2003.473
title: ปัญหาการสอนปฏิบัติการวิทยาศาสตร์ ระดับมัธยมศึกษาตอนต้น ในโรงเรียนโครงการขยายโอกาสทางการศึกษา สังกัดสำนักการศึกษากรุงเทพมหานคร /
DOI: 10.18430/m35ihp
title: X-ray diffraction data for the crystal Structure of Cobyrinic Acid a,c-diamide synthase from Mycobacterium smegmatis with bound ADP and Magnesium (5IHP)
DOI: 10.14457/kmitl.the.2002.390
title: บทเรียนวีดิทัศน์เรื่อง สัญญาณภาพ /
DOI: 10.18430/m35idw
title: X-ray diffraction data for the crystal structure of an oxidoreductase from Burkholderia vietnamiensis in complex with NADP (5IDW)
DOI: 10.18430/m35ixu
title: X-ray diffraction data for the crystal structure of an uncharacterized NIPSNAP domain protein from Burkholderia xenovorans (5IXU)
DOI: 10.5281/zenodo.268416
title: Chemokine G Protein-Coupled Receptor (Gpcr) Mutation Data Set - Revision

description: Curated set of 2004 quantitative mutation data points covering 10 chemokine G protein-coupled receptors, annotated according to GPCRdb standards (http://gpcrdb.org/) building from the data set published by Scholten et al. in BJP 2012, 165, 1617.

Errors in the previous version were corrected and new data points for CCR2 and CCR9 were added.
DOI: 10.5439/1342391
title: ARM: ARM Aerial Facility (AAF) Passive Cavity Aerosol Spectrometer

description: ARM Aerial Facility (AAF) Passive Cavity Aerosol Spectrometer
DOI: 10.5439/1342392
title: ARM: AOS: Single spot AethalometerÂ®

description: AOS: Single spot AethalometerÂ®
DOI: 10.5439/1342393
title: ARM: AOS: Cloud Condensation Nuclei Counter (Single Column), averaged

description: AOS: Cloud Condensation Nuclei Counter (Single Column), averaged
DOI: 10.5439/1342394
title: ARM: AOS: Cloud Condensation Nuclei Counter (Single Column), spectral data

description: AOS: Cloud Condensation Nuclei Counter (Single Column), spectral data
DOI: 10.5439/1342395
title: ARM: AOS: Particle Soot Absorption Photometer reference data

description: AOS: Particle Soot Absorption Photometer reference data
DOI: 10.5439/1342396
title: ARM: Auxiliary data for the AOS Single Particle Soot Photometer

description: Auxiliary data for the AOS Single Particle Soot Photometer
DOI: 10.5439/1342397
title: ARM: CO2 Flux Data: 4m samples

description: CO2 Flux Data: 4m samples
DOI: 10.5439/1342398
title: ARM: CO2 Flux Data: 60m samples

description: CO2 Flux Data: 60m samples
DOI: 10.5281/zenodo.268425
title: Eclipse Platform Ui

description: Bug localization refers to the automated process of locating the potential buggy files for a given bug report. To help developers focus their attention to those files is crucial. Several existing automated approaches for bug localization from a bug report face a key challenge, called lexical mismatch, in which the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. This paper presents a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files and documentation if they appear frequently enough in the pairs of reports and buggy files. Our empirical evaluation on real-world projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, HyLoc, with a combination of the features built from DNN, rVSM, and project’s bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it is correct with just a single suggested file. Two out of three cases, a correct buggy file is in the list of three suggested files.
DOI: 10.5281/zenodo.268426
title: Kc3

description: This is one of the NASA Metrics Data Program defect data sets.

Be aware that the Promise version of this dataset differs from the NASA MDP version:

Version,Cases(n),Features(p) * KC3 – NASA,458,43. * KC3 – Promise,458,40.

Therefore researchers are <em>strongly</em> advised to indicate which version of these files they are using!
description: {"references": ["http://menzies.us/pdf/03blind.pdf"]}
DOI: 10.17632/psjh4bf96r.25
title: GIMS - The Graz-Innsbruck Market System

description: GIMS is a z-Tree-based software for conducting asset market experiments.
DOI: 10.17632/psjh4bf96r.26
title: GIMS - The Graz-Innsbruck Market System

description: GIMS is a z-Tree-based software for conducting asset market experiments.
DOI: 10.17632/wkcr5gb4t5.1
title: Raw images for LIN41 targets reporter strains (Aeschimann et al., "LIN41 post-transcriptionally silences mRNAs by two distinct and position-dependent mechanisms", Mol Cell 2017 - main figures)

description: Raw images for LIN41 targets reporter strains (Aeschimann et al., "LIN41 post-transcriptionally silences mRNAs by two distinct and position-dependent mechanisms", Mol Cell 2017). Raw images are provided for each main figure and organized in separate archives by figure. Folder names of unzipped archives provide strain numbers and genotypes.
DOI: 10.5281/zenodo.268424
title: Coc8

description: This is a cross-listed data set that contains effort <em>and</em> defect information.

The data in this file confirms to the [model.

URL


	Latest version:
	
		coc81-dem (with defect and month counts)
		coc81
		coc81lnh: effort multipliers expresses as “low,medium,hight”
		cocomo2000: cocomo data set transferred to cocomo II via Rosetta Stone
	
	
	With change log:
	
		coc81-dem (with defect and month counts)
		coc81
		coc81lnh
	
	


Change Log

WhenWhatMarch 2010JPL experts added expected number of defects and months to create coc81-demDec 2009Correction: the original PROMISE repository data set had incorrect DATA and CPLX effort multiplier values for project 32 and did not include prjoject_id, dev_mode, and the first row of headers as it does nowFebruary 8, 2006Donated by Tim Menzies

Reference

Reifer, D., Boehm, B. W., and Chulani, S. (1999). The Rosetta Stone: Making COCOMO 81 Estimates Work with COCOMO II. Crosstalk. The Journal of Defense Software Engineering., pages 11–15.
DOI: 10.5281/zenodo.268427
title: Zuzel

description: This dataset is one of the Marian Jureckzo Datasets.

This dataset uses the CK OO metrics.
DOI: 10.5281/zenodo.268430
title: Huihuaprediction

description: Accurate detection of defects prior to product release helps software engineers focus verification activities on defect prone modules, thus improving the effectiveness of software development. A common scenario is to use the defects from prior releases to build the prediction model for the upcoming release, typically through a supervised learning method. As software development is a dynamic process, fault characteristics in subsequent releases may vary. Therefore, supplementing the defect information from prior releases with limited information about the defects from the current release detected early, seems to offer intuitive and practical benefits. We propose active learning as a way to automate the development of models which improve the performance of defect prediction between successive releases. Our results show that the integration of active learning with uncertainty sampling consistently outperforms the corresponding supervised learning approach. We further improve the prediction performance with feature compression techniques, where feature selection or dimensionality reduction is applied to defect data prior to active learning. We observe that dimensionality reduction techniques, particularly multidimensional scaling with random forest similarity, work better than feature selection due to their ability to identify and combine essential information in data set features. We present the improvements offered by this methodology through the prediction of defective modules in the three successive versions of Eclipse.
DOI: 10.5281/zenodo.268432
title: Qualitas Corpus

description: <strong>Overview of Data</strong>

Although the original Qualitas Corpus has provided a valuable contribution for experimentation in software engineering, there are several scenarios—e.g., experiments that rely on Abstract Syntax Tree (AST) or bytecode—in which researchers need to import and compile the source code. Since this task is not trivial in the case of systems with many external dependencies, our goal is to assist researchers by removing the compilation effort when conducting empirical studies.

<strong>Qualitas</strong>

The Qualitas Corpus is a curated collection of software systems intended to be used for empirical studies of code artefacts. The primary goal is to provide a resource that supports reproducible studies of software. The current release of the Corpus contains open-source Java software systems, often multiple versions.
description: {"references": ["Qualitas Corpus: A Curated Collection of Java Code for Empirical Studies  APSEC:2010", "http://java.labsoft.dcc.ufmg.br/qualitas.class/"]}
DOI: 10.5281/zenodo.268434
title: Cm1

description: CM1 is a NASA spacecraft instrument (data collection and processing) written in “C”.

At various times, researchers have negotiated access to the CM source code. Hence, it is somewhat more studied that some of the other NASA data sets. For, example, a UML model<br>
for CM1 is reverse engineered from the artifacts provided to West Virginia University.
DOI: 10.5281/zenodo.268429
title: Camel 1.0

description: Background: This paper describes an analysis that was conducted on newly collected repository with 92 versions of 38 proprietary, open-source and academic projects. A preliminary study performed before showed the need for a further in-depth analysis in order to identify project clusters. <br>
Aims: The goal of this research is to perform clustering on software projects in order to identify groups of software projects with similar characteristic from the defect prediction point of view. One defect prediction model should work well for all projects that belong to such group. The existence of those groups was investigated with statistical tests and by comparing the mean value of prediction efficiency. <br>
Method: Hierarchical and k-means clustering, as well as Kohonen’s neural network was used to find groups of similar projects. The obtained clusters were investigated with the discriminant analysis. For each of the identified group a statistical analysis has been conducted in order to distinguish whether this group really exists. Two defect prediction models were created for each of the identified groups. The first one was based on the projects that belong to a given group, and the second one - on all the projects. Then, both models were applied to all versions of projects from the investigated group. If the predictions from the model based on projects that belong to the identified group are significantly better than the all-projects model (the mean values were compared and statistical tests were used), we conclude that the group really exists. <br>
Results: Six different clusters were identified and the existence of two of them was statistically proven: 1) cluster proprietary B – T=19, p=0.035, r=0.40; 2) cluster proprietary/open - t(17)=3.18, p=0.05, r=0.59. The obtained effect sizes (r) represent large effects according to Cohen’s benchmark, which is a substantial finding. <br>
Conclusions: The two identified clusters were described and compared with results obtained by other researchers. The results of this work makes next step towards defining formal methods of reuse defect prediction models by identifying groups of projects within which the same defect prediction model may be used. Furthermore, a method of clustering was suggested and applied.
DOI: 10.5281/zenodo.268433
title: Cocomosdr

description: Data from a Turkish Software Industry (Donated by Software Research Laboratory- Softlab- Bogazici University, Istanbul, Turkey

Software effort data collected in the form of COCOMO II. Consists of data from 12 projects and 5 different software companies in various domains.
DOI: 10.5281/zenodo.268436
title: Xalan

description: Background: This paper describes an analysis that was conducted on newly collected repository with 92 versions of 38 proprietary, open-source and academic projects. A preliminary study performed before showed the need for a further in-depth analysis in order to identify project clusters. <br>
Aims: The goal of this research is to perform clustering on software projects in order to identify groups of software projects with similar characteristic from the defect prediction point of view. One defect prediction model should work well for all projects that belong to such group. The existence of those groups was investigated with statistical tests and by comparing the mean value of prediction efficiency. <br>
Method: Hierarchical and k-means clustering, as well as Kohonen’s neural network was used to find groups of similar projects. The obtained clusters were investigated with the discriminant analysis. For each of the identified group a statistical analysis has been conducted in order to distinguish whether this group really exists. Two defect prediction models were created for each of the identified groups. The first one was based on the projects that belong to a given group, and the second one - on all the projects. Then, both models were applied to all versions of projects from the investigated group. If the predictions from the model based on projects that belong to the identified group are significantly better than the all-projects model (the mean values were compared and statistical tests were used), we conclude that the group really exists. <br>
Results: Six different clusters were identified and the existence of two of them was statistically proven: 1) cluster proprietary B – T=19, p=0.035, r=0.40; 2) cluster proprietary/open - t(17)=3.18, p=0.05, r=0.59. The obtained effect sizes (r) represent large effects according to Cohen’s benchmark, which is a substantial finding. <br>
Conclusions: The two identified clusters were described and compared with results obtained by other researchers. The results of this work makes next step towards defining formal methods of reuse defect prediction models by identifying groups of projects within which the same defect prediction model may be used. Furthermore, a method of clustering was suggested and applied.
DOI: 10.5281/zenodo.268438
title: Nrpclassic

description: Jifeng Xuan, He Jiang, Zhilei Ren, Zhongxuan Luo, “Solving the Large Scale Next Release Problem with a Backbone Based Multilevel Algorithm”, IEEE Transactions on Software Engineering, vol. 99, no. PrePrints, , 2011
DOI: 10.5281/zenodo.268439
title: Pc5

description: This is one of the NASA Metrics Data Program defect data sets.
description: {"references": ["\"How Good is Your Blind Spot Sampling Policy?\" Available from http://menzies.us/pdf/03blind.pdf."]}
DOI: 10.5281/zenodo.268440
title: Ant

description: This dataset is one of the Marian Jureckzo Datasets.

This dataset uses the CK OO metrics.

Reference

@inproceedings{Jureczko:2010:TIS:1868328.1868342, author = {Jureczko, Marian and Madeyski, Lech}, title = {Towards Identifying Software Project Clusters with Regard to Defect Prediction}, booktitle = {Proceedings of the 6th International Conference on Predictive Models in Software Engineering}, series = {PROMISE '10}, year = {2010}, isbn = {978-1-4503-0404-7}, location = {Timi\şoara, Romania}, pages = {9:1--9:10}, articleno = {9}, numpages = {10}, url = {http://doi.acm.org/10.1145/1868328.1868342}, doi = {10.1145/1868328.1868342}, acmid = {1868342}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {clustering, defect prediction, design metrics, size metrics}, }

 

Paper abstract

Background: This paper describes an analysis that was conducted on newly collected repository with 92 versions of 38 proprietary, open-source and academic projects. A preliminary study performed before showed the need for a further in-depth analysis in order to identify project clusters. <br>
Aims: The goal of this research is to perform clustering on software projects in order to identify groups of software projects with similar characteristic from the defect prediction point of view. One defect prediction model should work well for all projects that belong to such group. The existence of those groups was investigated with statistical tests and by comparing the mean value of prediction efficiency. <br>
Method: Hierarchical and k-means clustering, as well as Kohonen’s neural network was used to find groups of similar projects. The obtained clusters were investigated with the discriminant analysis. For each of the identified group a statistical analysis has been conducted in order to distinguish whether this group really exists. Two defect prediction models were created for each of the identified groups. The first one was based on the projects that belong to a given group, and the second one - on all the projects. Then, both models were applied to all versions of projects from the investigated group. If the predictions from the model based on projects that belong to the identified group are significantly better than the all-projects model (the mean values were compared and statistical tests were used), we conclude that the group really exists. <br>
Results: Six different clusters were identified and the existence of two of them was statistically proven: 1) cluster proprietary B – T=19, p=0.035, r=0.40; 2) cluster proprietary/open - t(17)=3.18, p=0.05, r=0.59. The obtained effect sizes (r) represent large effects according to Cohen’s benchmark, which is a substantial finding. <br>
Conclusions: The two identified clusters were described and compared with results obtained by other researchers. The results of this work makes next step towards defining formal methods of reuse defect prediction models by identifying groups of projects within which the same defect prediction model may be used. Furthermore, a method of clustering was suggested and applied.
DOI: 10.5281/zenodo.268441
title: Kc1

description: <strong>Overview of Data</strong>

The data is a weka .arff file. It contains 94 independent variables and 1 dependent variable.

<strong>Paper Abstract</strong>

Modern requirements tracing tools employ information retrieval methods to automatically generate candidate links. Due to the inherent trade-off between recall and precision, such methods cannot achieve a high coverage without also retrieving a great number of false positives, causing a significant drop in result accuracy. In this paper, we propose an approach to improving the quality of candidate link generation for the requirements tracing process. We base our research on the cluster hypothesis which suggests that correct and incorrect links can be grouped in high-quality and low-quality clusters respectively. Result accuracy can thus be enhanced by identifying and filtering out low-quality clusters. We describe our approach by investigating three open-source datasets, and further evaluate our work through an industrial study. The results show that our approach outperforms a baseline pruning strategy and that improvements are still possible.
DOI: 10.15468/3fa9qi
title: The Thomisidae and Philodromidae (Arachnida: Araneae) of the Galápagos Islands (Ecuador)

description: This dataset contains the digitized treatments in Plazi based on the original journal article l. Baert (2013): The Thomisidae and Philodromidae (Arachnida: Araneae) of the Galápagos Islands (Ecuador). European Journal of Taxonomy 43: 1-23, DOI: 10.5852/ejt.2013.43, URL: http://www.europeanjournaloftaxonomy.eu/index.php/ejt/article/view/148