cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10405535
title: The Pollution from Obsolete Issue Reports: An Empirical Study

description: The Pollution from Obsolete Issue Report: An Empirical Study

Project summary

In software development, programmers use issue trackers to manage their maintenance issues and record valuable maintenance details in issue reports. Based on these issue reports, programmers have enhanced code comprehension and researchers have mined knowledge from issue reports to assist various programming tasks. Although issue reports are useful, some of them can be obsolete, in that their corresponding commits are overwritten or rolled back, with the evolution of software. The obsolete issue reports can invalidate their references and descriptions, and can have far-reaching impacts on the approaches built on them.

To deepen the understanding of obsolete issue reports, we conducted the first empirical study to analyze obsolete issue reports. We consider that an issue report is obsolete if its revisions are partially or totally removed in later commits. To measure how an issue report becomes obsolete, we define an obsolete ratio of an issue report as its deleted lines over all its modified lines. In this paper, we build a tool, ICLinker, to inspect the obsolete issue reports and calculate the obsolete ratios. With ICLinker, we analyze 116,106 commits and 72,136 issue reports that are collected from 9 Apache projects. Taking them as our inputs, we explore four research questions, which concern the distributions, the references, the explanations and the impact of obsolete issue reports. Our findings on these research questions enrich the knowledge of obsolete issue reports, and some are even counterintuitive. For example, we find that obsolete issue reports are mixed with other issue reports. As another example, we find that only a small portion of issue reports are mentioned in code comments, but about half of them are obsolete. We confirm that 48.18% of issue reported recommended by a recent approach are obsolete. As many approaches take issue reports as inputs, obsolete issue reports can affect more research topics than recommending issue reports.
DOI: 10.5281/zenodo.10405536
title: The Pollution from Obsolete Issue Reports: An Empirical Study

description: The Pollution from Obsolete Issue Report: An Empirical Study

Project summary

In software development, programmers use issue trackers to manage their maintenance issues and record valuable maintenance details in issue reports. Based on these issue reports, programmers have enhanced code comprehension and researchers have mined knowledge from issue reports to assist various programming tasks. Although issue reports are useful, some of them can be obsolete, in that their corresponding commits are overwritten or rolled back, with the evolution of software. The obsolete issue reports can invalidate their references and descriptions, and can have far-reaching impacts on the approaches built on them.

To deepen the understanding of obsolete issue reports, we conducted the first empirical study to analyze obsolete issue reports. We consider that an issue report is obsolete if its revisions are partially or totally removed in later commits. To measure how an issue report becomes obsolete, we define an obsolete ratio of an issue report as its deleted lines over all its modified lines. In this paper, we build a tool, ICLinker, to inspect the obsolete issue reports and calculate the obsolete ratios. With ICLinker, we analyze 116,106 commits and 72,136 issue reports that are collected from 9 Apache projects. Taking them as our inputs, we explore four research questions, which concern the distributions, the references, the explanations and the impact of obsolete issue reports. Our findings on these research questions enrich the knowledge of obsolete issue reports, and some are even counterintuitive. For example, we find that obsolete issue reports are mixed with other issue reports. As another example, we find that only a small portion of issue reports are mentioned in code comments, but about half of them are obsolete. We confirm that 48.18% of issue reported recommended by a recent approach are obsolete. As many approaches take issue reports as inputs, obsolete issue reports can affect more research topics than recommending issue reports.
DOI: 10.5281/zenodo.10360825
title: Classification data of Mid-latitude Northwest Pacific

description: The clustering model used for the analysis in Sambe and Suga (2022) and the paper in prep, the training dataset used to create the model, and the dataset to which clustering was applied and labeled. The creation and classification of these models was done using pyXpcm(Maze, 2020).

clutering_model.nc

It contains the parameters of the Gaussian distribution representing each class and information on the principal components, in a format that can be used in pyXpcm.

training_data.nc

Profile information for 4544 points is stored.

Dimensions are as follows. The names and coordinates in the data set are also noted.



Pressure depth (DEPTH, -5~-1000, interval is 5)

Number of profiles (N_PROF, 0~4543)


The variables are as follows. The names in the data set and the dimensions on which they depend are also noted.



Latitude (LATITUDE,(N_PROF))

Longitude (LONGITUDE,(N_PROF))

Observation date and time (TIME,(N_PROF))

Observation year (YEAR,(N_PROF))

Observation month (MONTH,(N_PROF))

Data name of profile (NAME,(N_PROF))

Temperature (TEMP,(N_PROF, DEPTH))

Salinity (PSAL,(N_PROF, DEPTH))


classified_data.nc

Profile information for 57262 points is stored.

Dimensions are as follows:



Number of profiles (N_PROF, 0~57261)

Class (pcm_class, 0~4)

Pressure depth (DEPTH, -5~-1000, interval is 5)

quantile (quantile, 0.05, 0.5, 0.95)


The variables are as follows. The names in the data set and the dimensions on which they depend are also noted.



Latitude (LATITUDE,(N_PROF))

Longitude (LONGITUDE,(N_PROF))

Observation date and time (TIME,(N_PROF))

Observation year (YEAR,(N_PROF))

Observation month (MONTH,(N_PROF))

Data name of profile (NAME,(N_PROF))

Temperature (TEMP,(N_PROF, DEPTH))

Salinity (PSAL,(N_PROF, DEPTH))

Assigned class (PCM_LABELS,(N_PROF))

Posterior probability for each class (PCM_POST,(pcm_class, N_PROF))

Temperature quantile for each class (TEMP_Q,(pcm_class, quantile, DEPTH))

Salinity quantile for each class (PSAL_Q,(pcm_class, quantile, DEPTH))

Labeling metric (robustness,(N_PROF))


Note that the class number starts with 0 in this data set. This means that the profiles belonging to Class 2 in the paper are those with PCM_LABELS=1 in this data set.
DOI: 10.5281/zenodo.10360826
title: Classification data of Mid-latitude Northwest Pacific

description: The clustering model used for the analysis in Sambe and Suga (2022) and the paper in prep, the training dataset used to create the model, and the dataset to which clustering was applied and labeled. The creation and classification of these models was done using pyXpcm(Maze, 2020).

clutering_model.nc

It contains the parameters of the Gaussian distribution representing each class and information on the principal components, in a format that can be used in pyXpcm.

training_data.nc

Profile information for 4544 points is stored.

Dimensions are as follows. The names and coordinates in the data set are also noted.



Pressure depth (DEPTH, -5~-1000, interval is 5)

Number of profiles (N_PROF, 0~4543)


The variables are as follows. The names in the data set and the dimensions on which they depend are also noted.



Latitude (LATITUDE,(N_PROF))

Longitude (LONGITUDE,(N_PROF))

Observation date and time (TIME,(N_PROF))

Observation year (YEAR,(N_PROF))

Observation month (MONTH,(N_PROF))

Data name of profile (NAME,(N_PROF))

Temperature (TEMP,(N_PROF, DEPTH))

Salinity (PSAL,(N_PROF, DEPTH))


classified_data.nc

Profile information for 57262 points is stored.

Dimensions are as follows:



Number of profiles (N_PROF, 0~57261)

Class (pcm_class, 0~4)

Pressure depth (DEPTH, -5~-1000, interval is 5)

quantile (quantile, 0.05, 0.5, 0.95)


The variables are as follows. The names in the data set and the dimensions on which they depend are also noted.



Latitude (LATITUDE,(N_PROF))

Longitude (LONGITUDE,(N_PROF))

Observation date and time (TIME,(N_PROF))

Observation year (YEAR,(N_PROF))

Observation month (MONTH,(N_PROF))

Data name of profile (NAME,(N_PROF))

Temperature (TEMP,(N_PROF, DEPTH))

Salinity (PSAL,(N_PROF, DEPTH))

Assigned class (PCM_LABELS,(N_PROF))

Posterior probability for each class (PCM_POST,(pcm_class, N_PROF))

Temperature quantile for each class (TEMP_Q,(pcm_class, quantile, DEPTH))

Salinity quantile for each class (PSAL_Q,(pcm_class, quantile, DEPTH))

Labeling metric (robustness,(N_PROF))


Note that the class number starts with 0 in this data set. This means that the profiles belonging to Class 2 in the paper are those with PCM_LABELS=1 in this data set.
DOI: 10.25919/nkk9-jt69
title: Parkes observations for project P1176 semester 2023OCTS_01

description: Pulsars and their winds are among nature's most powerful particle accelerators, producing particles with energies up to a few PeVatrons. In addition to high-energy radiation from the magnetosphere, the rotational energy of a neutron star is carried away in the form of magnetized ultra-relativistic pulsar winds. Investigation of pulsar wind nebula (PWN) offers an excellent chance to determine the interaction details of relativistic flows with their surroundings that may never be possible in other classes of source. Our recent analyses of ASKAP observations of Galactic Plane uncovered previously known X-ray PWN CXOU J163802.6-471358 (nicknamed as Potoroo). Recently, we have successfully detected a pulsar candidate associated with Potoroo with Parkes telescope. It is detected at 3 GHz with a dispersion measure of DM = 1553 pc cm-3 and a rotation period of 65.74 ms. Here we propose to continue timing observations for this young pulsar with the Parkes Ultra-Wideband Low (UWL) receiver. Through pulsar timing, we will be able to obtain precise measurements of the pulsar position and proper motions, and the spin-down of the pulsar which enables us to independently estimate the age of the system. Long-term timing will also enable us to measure the parallax of the system and search for pulsation at high energies (i.e. X-ray and gamma-ray)
DOI: 10.25919/e526-gw33
title: Parkes observations for project P456 semester 2023OCTS_04

description: The Parkes Pulsar Timing Array (PPTA) project has three primary goals: (a) detection of gravitational waves from astronomical sources, (b) establishment of a pulsar timescale, and (c) improvement of our understanding of Solar-system dynamics. We are now in an exciting regime where a signal detected in our (and other data sets) has the form expected for a gravitational wave background. We are therefore now attempting to confirm, or deny, this exciting result. With this proposal we aim to maintain our pre-eminent position in the field.

Unlike most observing proposals, this is a continuing proposal for which the observations will continue to improve bounds on ultra-low-frequency gravitational waves until they are finally detected. Continued Parkes observations will remain valuable at least until the first stage of the SKA is able to improve on our sensitivity and observing cadence. Even after the gravitational wave background is detected we will want to continue observations in support of the nascent field of gravitational wave astronomy!
DOI: 10.25901/z4vr-ry44
title: Alpine Peatland Flux Data Release 2023_v2
DOI: 10.6084/m9.figshare.24882231
title: Table S2.xlsx

description: <b>miR-497-5p Expression and Biological Activity in Gastric Cancer</b>
DOI: 10.6084/m9.figshare.24873822
title: weather_raw

description: daily weather dataset without data clean
DOI: 10.6084/m9.figshare.24873783
title: weather_cleaned

description: daily weather dataset after data clean
DOI: 10.6084/m9.figshare.24873756
title: crop_disaster_raw

description: crop disaster dataset without data clean
DOI: 10.6084/m9.figshare.24873756.v1
title: crop_disaster_raw

description: crop disaster dataset without data clean
DOI: 10.6084/m9.figshare.24873732
title: crop_disaster_cleaned

description: crop disaster dataset after data clean
DOI: 10.6084/m9.figshare.24882249
title: Supplementary File 1 for the manuscript <b>Citation analysis of papers dealing with dental videos on YouTube</b>

description: Supplementary File 1 for the manuscript <b>Citation analysis of papers dealing with dental videos on YouTube</b>
DOI: 10.6084/m9.figshare.24882249.v1
title: Supplementary File 1 for the manuscript <b>Citation analysis of papers dealing with dental videos on YouTube</b>

description: Supplementary File 1 for the manuscript <b>Citation analysis of papers dealing with dental videos on YouTube</b>
DOI: 10.25919/n3qq-5504
title: Parkes observations for project P960 semester 2023OCTS_04

description: We aim to carry out receiver pointing and characterisation tests of Parkes receivers. Typically, these tests are carried out after the receiver package is installed, but time does not always allow for proper characterisation. Receivers for consideration include UWL, MARS and 13MM packages. In addition to cross-referencing C007 for flux calibration via pointing, we aim to conduct gain-elevation and system temperature experiments using the Moon, to ensure users of the Parkes Observatory have the most accurate information available for calibration purposes.
DOI: 10.5281/zenodo.10416680
title: Jump Cell painting transient dataset
DOI: 10.5281/zenodo.10416681
title: Jump Cell painting transient dataset
DOI: 10.5281/zenodo.10416654
title: Jump Cell painting transient dataset
DOI: 10.5281/zenodo.10416655
title: Jump Cell painting transient dataset
DOI: 10.5281/zenodo.10416662
title: Jump Cell painting transient dataset
DOI: 10.25919/zek7-es57
title: Parkes observations for project P574 semester 2023OCTS_03

description: We request time to observe 260 pulsars on a regular basis in order to provide the accurate ephemerides necessary for the detection and characterisation of gamma-ray pulsars with the Fermi satellite. The main science goals are to increase the number of known gamma-ray pulsars (both radio loud and radio quiet), to determine accurate pulse profiles, and to characterise their high energy (phase-resolved) spectra. In the radio, the observations will also allow us to investigate: neutron stars and pulsar emission via detecting glitches and determining pulse timing parameters; and the ISM via measuring and monitoring dispersion measures, Faraday rotation measures, and scintillation. To date, we are (co-)authors on over 95 papers arising from the collaboration and P574 data, including four which use UWL observations. The data have contributed to the PhD theses of students from Bordeaux, Manchester, Oxford, Stanford, and Swinburne.
DOI: 10.25919/75j5-z340
title: Parkes observations for project P1178 semester 2023OCTS_03

description: We are actively developing a new version of the ATNF pulsar catalogue. In the paper describing the new catalogue we wish to take the opportunity to publish important pulsar parameters that have, to-date, not been published. The fundamental property of a pulsar is its pulse period and yet we have identified a large number of pulsars that do not having a measured spin-down rate and hence we do not know their evolutionary state, nor have good positions or knowledge of whether they are in a binary system. We have therefore selected 53 pulsars with the goal of measuring their spin-down rates. During this project we will also develop an automated pulsar-fold-mode online processing pipeline that includes online RFI mitigation. This pipeline will be made available as an ATNF service for other users.
DOI: 10.25505/fiz.icsd.cc2fcz5z
title: CSD 2245831: Experimental Crystal Structure Determination

description: Related Article: Hiroki Ubukata, Daichi Kato, Shougo Kitade, Thibault Broux, Cédric Tassel, David Schnieders, Richard Dronskowski, Hiroshi Kageyama|2023|Inorg.Chem.|62|6696|doi:10.1021/acs.inorgchem.3c00238
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2fcz60
title: CSD 2245832: Experimental Crystal Structure Determination

description: Related Article: Hiroki Ubukata, Daichi Kato, Shougo Kitade, Thibault Broux, Cédric Tassel, David Schnieders, Richard Dronskowski, Hiroshi Kageyama|2023|Inorg.Chem.|62|6696|doi:10.1021/acs.inorgchem.3c00238
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fdflw
title: CCDC 2246278: Experimental Crystal Structure Determination

description: Related Article: Yongjuan Jiao, Tao Wu, Xinyu Zhang, Yaojia Jiang|2023|Org.Chem.Front.|11|67|doi:10.1039/D3QO01537H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ff221
title: CSD 2246882: Experimental Crystal Structure Determination

description: Related Article: Dandan Wang, Xinyuan Zhang, Pifu Gong, Zheshuai Lin, Zhanggui Hu, Yicheng Wu|2023|Inorg.Chem.|62|6565|doi:10.1021/acs.inorgchem.3c00868
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ff658
title: CSD 2247009: Experimental Crystal Structure Determination

description: Related Article: Mohammed Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Ritobroto Sikdar, Diganta Sarkar, Mengran Sun, Jiyong Yao, Vladimir K. Michaelis, Arthur Mar|2023|Inorg.Chem.|62|7491|doi:10.1021/acs.inorgchem.3c00786
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ff669
title: CSD 2247010: Experimental Crystal Structure Determination

description: Related Article: Mohammed Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Ritobroto Sikdar, Diganta Sarkar, Mengran Sun, Jiyong Yao, Vladimir K. Michaelis, Arthur Mar|2023|Inorg.Chem.|62|7491|doi:10.1021/acs.inorgchem.3c00786
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ff67b
title: CSD 2247011: Experimental Crystal Structure Determination

description: Related Article: Mohammed Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Ritobroto Sikdar, Diganta Sarkar, Mengran Sun, Jiyong Yao, Vladimir K. Michaelis, Arthur Mar|2023|Inorg.Chem.|62|7491|doi:10.1021/acs.inorgchem.3c00786
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ff68c
title: CSD 2247012: Experimental Crystal Structure Determination

description: Related Article: Mohammed Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Ritobroto Sikdar, Diganta Sarkar, Mengran Sun, Jiyong Yao, Vladimir K. Michaelis, Arthur Mar|2023|Inorg.Chem.|62|7491|doi:10.1021/acs.inorgchem.3c00786
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ff69d
title: CSD 2247013: Experimental Crystal Structure Determination

description: Related Article: Mohammed Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Ritobroto Sikdar, Diganta Sarkar, Mengran Sun, Jiyong Yao, Vladimir K. Michaelis, Arthur Mar|2023|Inorg.Chem.|62|7491|doi:10.1021/acs.inorgchem.3c00786
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ff6bf
title: CSD 2247014: Experimental Crystal Structure Determination

description: Related Article: Mohammed Jomaa, Vidyanshu Mishra, Dundappa Mumbaraddi, Ritobroto Sikdar, Diganta Sarkar, Mengran Sun, Jiyong Yao, Vladimir K. Michaelis, Arthur Mar|2023|Inorg.Chem.|62|7491|doi:10.1021/acs.inorgchem.3c00786
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2ffd2c
title: CSD 2247192: Experimental Crystal Structure Determination

description: Related Article: Xinan Zhang, Yanling Jin, Wenxuan Zhu, Dan Chen, Qingqing Huang, Yaping Li, Olivier Mentré, Claire Minaud, Minfeng Lü|2023|J.Solid State Chem.|325|124139|doi:10.1016/j.jssc.2023.124139
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.