cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e113833v1-114220
title: Replication data for: The Power of Political Voice: Women's Political Representation and Crime in India

description: Using state-level variation in the timing of political reforms, we find that an increase in female representation in local government induces a large and significant rise in documented crimes against women in India. Our evidence suggests that this increase is good news, driven primarily by greater reporting rather than greater incidence of such crimes. In contrast, we find no increase in crimes against men or in gender-neutral crimes. We also examine the effectiveness of alternative forms of political representation. Large scale membership of women in local councils affects crime against them more than their presence in higher-level leadership positions. (JEL D72, J16, K42, O15, O17)
DOI: 10.5281/zenodo.6413128
title: Pseudomonas MWU 12.2029

description: Pseudomonas MWU 12.2029 wild cranberry bogs isolated from Cape Cod, National Seashore
DOI: 10.5517/ccdc.csd.cc2bfzc4
title: CCDC 2158386: Experimental Crystal Structure Determination

description: Related Article: Susanne Bieller, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bfzd5
title: CCDC 2158387: Experimental Crystal Structure Determination

description: Related Article: Susanne Bieller, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bfzg7
title: CCDC 2158389: Experimental Crystal Structure Determination

description: Related Article: Monika Haberecht, Michael Bolte, Hans-Wolfram Lerner, Matthias Wagner|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bg3kh
title: CCDC 2158516: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, J.D. Kennedy, M.J. Carr, M.G.S. Londesborough|2022|CSD Communication|||
DOI: 10.6084/m9.figshare.19516858
title: Dataset For Software Defect Predictions

description: SOFTLAB: dataset were from a Turkish software company which develops embedded controllers for home appliances [1] <br>RELINK: Dataset with has 26 static code features and contains three projects (Apache HTTP Server (Apache), OpenIntents Safe (Safe), and ZXing) [2]<br>NASA: Dataset from e C-based and are extracted from a software system which is made of a set of static code features [3].<br>4. AEEEM: dataset was compiled by Marco et al. [4], and contains five open source projects with 5,371 data points. The projects include: Apache Lucene (LC), Equinox (EQ), Eclipse JDT Core (JDT), Eclipse PDE UI (PDE) and Mylyn (ML).<br><br>AEEEM software defect datasets which has a format of ARFF was collected by D’Ambros et al. [1]<br>[1] M. D’Ambros, M. Lanza, and R. Robbes, “Evaluating defect prediction approaches: A benchmark and an extensive comparison,”Empirical Softw. Engg., vol. 17, no. 4-5, pp. 531–577, Aug. 2012.<br>[3] ] B. Ghotra, S. McIntosh, and A. E. Hassan, “Revisiting the impact of classification techniques on the performance of defect prediction models,” in 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, vol. 1. IEEE, 2015, pp. 789–800.<br>[4] M. D’Ambros, M. Lanza, and R. Robbes, “Evaluating defect prediction approaches: a benchmark and an extensive comparison,” Empirical Software Engineering, vol. 17, no. 4, pp. 531–577, 2012
DOI: 10.5281/zenodo.6410368
title: A dataset of archaeobotanical macroremains (staple crops) during the Neolithic and Bronze Ages in northern China

description: Dataset of archaeobotanical macroremains includes a total of 538 flotation results from 381 sites with millets excavated during the Neolithic and Bronze Ages in northern China. Two steps of screening have been applied to eliminate inapplicable sites. The dataset only consisted of staple crops according to traditional Chinese agriculture: foxtail millet (Setaria italica), broomcorn millet (<em>Panicum miliaceum</em>), rice (<em>Oryza sativa</em>), wheat and barley (<em>Triticum aestivum</em> and <em>Hordeum vulgare</em>), and soybean (<em>Glycine max</em>). Informations of absolute counts and weights of staple crops, percentage of counts and weights for each site together with their locations and ages were assembled.
DOI: 10.34740/kaggle/dsv/3408375
title: Russian Invasion of Ukraine
DOI: 10.6073/pasta/e9ac5ed3ba1c846f86d46207f97f3fc3
title: Shark catches (longline), water temperatures, salinities, and dissolved oxygen levels, and stable isotope values in the Shark River Slough, Everglades National Park (FCE LTER), Florida, USA: May 2005 - ongoing

description: This dataset provides information on the catches of sharks in the Shark River Slough in relation to physical factors including dissolved oxygen, water temperature, salinity, and distance upstream. Analysis of data collected suggest that distance from the Gulf of Mexico and dissolved have the largest effects on shark catch rates, with most juvenile bull sharks being caught in Tarpon Bay. This dataset includes all sharks caught on longline gear.
DOI: 10.17632/cn53hzsh5p.1
title: Diagnostic accuracy of cervical cancer screening strategies for high-grade cervical intraepithelial neoplasia (CIN2+/CIN3+) among women living with HIV: a systematic review and meta-analysis

description: This data was used for the analysis presented in the accompanying manuscript
DOI: 10.17632/fs839x372j.1
title: Supplemental Figure 1

description: Naranjo’s algorithm
DOI: 10.5281/zenodo.6413030
title: Data tables for primary and secondary datasets

description: These tables contain select information used to create Fig. 1 in arxiv: 2102.01079. PRIMARY.csv contains information for pairs of stars in the primary data set. SECONDARY.csv contains information for pairs of stars in the secondary data set.
DOI: 10.5281/zenodo.6413029
title: Data tables for primary and secondary datasets

description: These tables contain select information used to create Fig. 1 in arxiv: 2102.01079. PRIMARY.csv contains information for pairs of stars in the primary data set. SECONDARY.csv contains information for pairs of stars in the secondary data set.
DOI: 10.25919/5w9z-hh98
title: Parkes observations for project P574 semester 2021OCTS_14

description: We request time to observe 260 pulsars regularly 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 90 papers arising from the collaboration and P574 data, including three which use UWL observations. The data have contributed to the PhD theses of students from Bordeaux, Manchester, Oxford, Stanford, and Swinburne.
DOI: 10.18739/a2qr4nr9v
title: Confirmed coronavirus disease (COVID-19) cases and deaths in the Arctic 2020 - 2021

description: This project focuses on the development and deployment of a real-time web-based Coronavirus disease 2019 (COVID-19) data hub managed by an interdisciplinary team of experts in data science, geoinformatics, epidemiology and geography. The team is collecting, analyzing, and disseminating data on the spatial and temporal dynamics of the COVID-19 pandemic in the Arctic. Both datasets and analyses of spatiotemporal trends at the subregional level will be made available to stakeholders including Arctic residents, researchers, and policymakers. Pandemic data are be contextualized through the collection of first-person accounts of the COVID-19 experience in Arctic communities. The team is also developing geovisualization tools and analyze datasets to address urgent questions related to the spread and geography of COVID-19 in the Arctic, assessing containment and mitigation policies and evaluating whether case fatality rates are affected by environmental, socioeconomic, and/or geographic variables. Long-term curation of Arctic COVID-19 data and data products will ensure availability for future analysis, historical study and policy consideration. This project is supported by NSF Award #2034886.
DOI: 10.5281/zenodo.6413159
title: Data and code routines for biosonar data

description: Processed Acoustic data, and plotting routines to visualize them, for the paper titled "A dolphin-inspired compact sonar for underwater acoustic imaging.", Communications Engineering, by Hari Vishnu, Matthias Hoffmann-Kuhnt, Mandar Chitre, Abel Ho and Eszter Matrai. If using this data, please cite the paper above. To obtain the output visualizations for the acoustic data recorded during the echoic-to-visual matching-to-sample trials (plots shown in Figs. 3, 5, and Supplementary Fig. 3 in the paper), run Plot_biosonar_output.m To obtain the output visualizations with biomimetic sonar data (plots shown in Fig. 4 in the paper), run Plot_biomimetic_output.m These perform the template-matching of acoustic outputs with the shading masks (templates), and give the discrimination coefficient in dB, and plot the acoustic outputs (with the option of plotting the shading masks on top). Change the variable to 'Bartlett'/'SA' for Bartlett beamformer output or Sparsity-aware beamformer output, and the variable to SQ/SQset1/SQset2/FF/FFset1/FFset2 as required More details can be found in comments in the code files.
DOI: 10.5281/zenodo.6413158
title: Data and code routines for biosonar data

description: Processed Acoustic data, and plotting routines to visualize them, for the paper titled "A dolphin-inspired compact sonar for underwater acoustic imaging.", Communications Engineering, by Hari Vishnu, Matthias Hoffmann-Kuhnt, Mandar Chitre, Abel Ho and Eszter Matrai. If using this data, please cite the paper above. To obtain the output visualizations for the acoustic data recorded during the echoic-to-visual matching-to-sample trials (plots shown in Figs. 3, 5, and Supplementary Fig. 3 in the paper), run Plot_biosonar_output.m To obtain the output visualizations with biomimetic sonar data (plots shown in Fig. 4 in the paper), run Plot_biomimetic_output.m These perform the template-matching of acoustic outputs with the shading masks (templates), and give the discrimination coefficient in dB, and plot the acoustic outputs (with the option of plotting the shading masks on top). Change the variable to 'Bartlett'/'SA' for Bartlett beamformer output or Sparsity-aware beamformer output, and the variable to SQ/SQset1/SQset2/FF/FFset1/FFset2 as required More details can be found in comments in the code files.
DOI: 10.6084/m9.figshare.19517041.v1
title: Untitled Item

description: Inversion of the gravity gradient data is widely adopted to construct 3-D density models. However, this inversion usually suffers from non-uniqueness and has a limited resolution in depth. This study introduces an effective inversion method for interpreting gravity gradient data based on the mixed L1 and L2 norm regularization. We also apply an efficient forward modeling algorithm to the inversion, which has many time-consuming iterations. Equivalence relations in the sensitivity matrix are employed to reduce the storage and computation time. In addition, a 2-D discrete convolution algorithm is used to reduce the repetitive calculation in the forward modeling. The numerical examples demonstrate that the computational efficiency is increased by about three orders of magnitude compared with the traditional forward method. Furthermore, compared to traditional single-norm inversion, the vertical gradient inversion with the mixed L1 and L2 norm regularization has a higher depth resolution. Finally, we apply the method to the inversion of the airborne gravity gradiometry data over the Vinton Dome, southern Louisiana.
DOI: 10.5281/zenodo.6413087
title: Consistent Bondi SPH for frontiers 2022

description: Data to be combined with the notebook scripts: https://github.com/josemramirez/poly_frontiers2022, to reproduce all the results of the paper. 4 sets for 4 different values of gamma = G1 = 1; G1_1 = 1.1; G3_2 = 1.5; G5_3 = 1.66. (1) Unzip in the same directory of the scripts. ps: last version v3.0 computed with the last version of Qs parameters = BH_v8.0e.
description: This version is run with our BH_v8.0e.
DOI: 10.5281/zenodo.6113402
title: Consistent Bondi SPH for frontiers 2022

description: Data to be combined with the notebook scripts: https://github.com/josemramirez/poly_frontiers2022, to reproduce all the results of the paper. 4 sets for 4 different values of gamma = G1 = 1; G1_1 = 1.1; G3_2 = 1.5; G5_3 = 1.66. (1) Unzip in the same directory of the scripts. ps: last version v3.0 computed with the last version of Qs parameters = BH_v8.0e.
description: This version is run with our BH_v8.0e.
DOI: 10.6084/m9.figshare.19517041.v2
title: Efficient 3-D forward modeling and mixed norm inversion of the gravity gradients with application to the Vinton salt dome

description: Inversion of the gravity gradient data is widely adopted to construct 3-D density models. However, this inversion usually suffers from non-uniqueness and has a limited resolution in depth. This study introduces an effective inversion method for interpreting gravity gradient data based on the mixed L1 and L2 norm regularization. We also apply an efficient forward modeling algorithm to the inversion, which has many time-consuming iterations. Equivalence relations in the sensitivity matrix are employed to reduce the storage and computation time. In addition, a 2-D discrete convolution algorithm is used to reduce the repetitive calculation in the forward modeling. The numerical examples demonstrate that the computational efficiency is increased by about three orders of magnitude compared with the traditional forward method. Furthermore, compared to traditional single-norm inversion, the vertical gradient inversion with the mixed L1 and L2 norm regularization has a higher depth resolution. Finally, we apply the method to the inversion of the airborne gravity gradiometry data over the Vinton Dome, southern Louisiana.
DOI: 10.6084/m9.figshare.19410905
title: Anonymized data from Liaw et al. (in preparation)

description: The dataset from Experiment 2 of Liaw et al. (in preparation). A preprint of the study can be found on PsyArXiv: https://doi.org/10.31234/osf.io/cznh9<br>
DOI: 10.6084/m9.figshare.19410905.v2
title: Anonymized data from Liaw et al. (in preparation)

description: The dataset from Experiment 2 of Liaw et al. (in preparation). A preprint of the study can be found on PsyArXiv: https://doi.org/10.31234/osf.io/cznh9<br>
DOI: 10.6084/m9.figshare.19410905.v1
title: Anonymized data from Liaw et al. (in preparation)

description: The dataset from Experiment 2 of Liaw et al. (in preparation). A preprint of the study can be found on PsyArXiv: https://doi.org/10.31234/osf.io/cznh9<br>
DOI: 10.25910/yk0y-0217
title: An interlaboratory collection of datasets and test cases
DOI: 10.6084/m9.figshare.19517242
title: [CSBC'22] Impacto da Pandemia Sob Perspectiva dos Discentes

description: Material Suplementar do artigo "Impactos Educacionais e Sociais da Pandemia de COVID-19 sob a Perspectiva dos Discentes".
DOI: 10.5517/ccdc.csd.cc22zf7r
title: CCDC 1935864: Experimental Crystal Structure Determination

description: Related Article: Chang-Qing Liu, Hang Li, Bo Wang, Zi-Yi Guo, Yu Wang, Ji-Tan Zhang, Mei-Hua Xie|2022|Org.Chem.Front.|9|1855|doi:10.1039/D1QO01906F
DOI: 10.5517/ccdc.csd.cc22zfn5
title: CCDC 1935877: Experimental Crystal Structure Determination

description: Related Article: Chang-Qing Liu, Hang Li, Bo Wang, Zi-Yi Guo, Yu Wang, Ji-Tan Zhang, Mei-Hua Xie|2022|Org.Chem.Front.|9|1855|doi:10.1039/D1QO01906F
DOI: 10.5517/ccdc.csd.cc22zfl3
title: CCDC 1935875: Experimental Crystal Structure Determination

description: Related Article: Chang-Qing Liu, Hang Li, Bo Wang, Zi-Yi Guo, Yu Wang, Ji-Tan Zhang, Mei-Hua Xie|2022|Org.Chem.Front.|9|1855|doi:10.1039/D1QO01906F
DOI: 10.5517/ccdc.csd.cc25j4yt
title: CCDC 2011526: Experimental Crystal Structure Determination

description: Related Article: Mahesh Bhagwan Thoke, Gui-Jun Sun, Rahul Anil Borse, Ping Lin, Shao-Xia Lin|2022|Org.Chem.Front.|9|1797|doi:10.1039/D1QO01817E
DOI: 10.5517/ccdc.csd.cc26srj9
title: CCDC 2049581: Experimental Crystal Structure Determination

description: Related Article: Yihan Zhang, Hongnan Wu, Yajing Sun, Shuyu Li, Si Liu, Lei Zheng, Lingjie Sun, Fangxu Yang, Xiaotao Zhang, Wenping Hu|2021|Sci. China Mater.|65|1320|doi:10.1007/s40843-021-1892-1
DOI: 10.5517/ccdc.csd.cc28vrrn
title: CCDC 2111092: Experimental Crystal Structure Determination

description: Related Article: Jinhang Yu, Mengjiao Xu, Xinyuan Wang, Bei Zhang, Hui Mao, Xin Lv, Liejin Zhou|2022|Org.Chem.Front.|9|1850|doi:10.1039/D1QO01733K
DOI: 10.5517/ccdc.csd.cc28vsmk
title: CCDC 2111119: Experimental Crystal Structure Determination

description: Related Article: Jinhang Yu, Mengjiao Xu, Xinyuan Wang, Bei Zhang, Hui Mao, Xin Lv, Liejin Zhou|2022|Org.Chem.Front.|9|1850|doi:10.1039/D1QO01733K
DOI: 10.5517/ccdc.csd.cc2901yb
title: CCDC 2115221: Experimental Crystal Structure Determination

description: Related Article: Jinhang Yu, Mengjiao Xu, Xinyuan Wang, Bei Zhang, Hui Mao, Xin Lv, Liejin Zhou|2022|Org.Chem.Front.|9|1850|doi:10.1039/D1QO01733K
DOI: 10.5517/ccdc.csd.cc292kzy
title: CCDC 2117640: Experimental Crystal Structure Determination

description: Related Article: Chang-Qing Liu, Hang Li, Bo Wang, Zi-Yi Guo, Yu Wang, Ji-Tan Zhang, Mei-Hua Xie|2022|Org.Chem.Front.|9|1855|doi:10.1039/D1QO01906F
DOI: 10.34740/kaggle/dsv/3408632
title: NASA Project; Marine Debris Machine Learning
DOI: 10.6084/m9.figshare.19517272
title: PSO-BP codes for modelling the relationships between the national GDP and nighttime light data

description: PSO-BP codes for modelling the relationships between the national GDP and nighttime light data
DOI: 10.6084/m9.figshare.19517257
title: Juno Cruise Phase Magnetometer and Position Data

description: Python pickle file of in situ magnetometer data taken by the Juno spacecraft during its cruise phase to Jupiter from 2011 to 2016. <br>Magnetometer data is in RTN coordinates. Corresponding spacecraft position data is in HEEQ coordinates. 1 minute resolution. Column headings are as follows:<br>Timestamp, B_TOT, B_R, B_T, B_N, r_HEEQ, lat_HEEQ, lon_HEEQ, pos_xyz_HEEQ. <br>Pickle file was created using pandas. To unpickle into pandas DataFrame:<br>&gt; import pickle&gt; import pandas as pd<br>&gt; unpickled_df = pd.read_pickle("juno_2011_2016_rtn.pkl")<br><br>If data is used for peer reviewed scientific publications, please contact emma.davies@unh.edu.<br><i>We acknowledge the Juno MAG Principal Investigator, J.E.P. Connerney, and instrument team. We also thank the Planetary Data Science archive (PDS; https://pds.nasa.gov) for their distribution of the original Juno cruise phase data.</i><br><br>
DOI: 10.6084/m9.figshare.19517257.v1
title: Juno Cruise Phase Magnetometer and Position Data

description: Python pickle file of in situ magnetometer data taken by the Juno spacecraft during its cruise phase to Jupiter from 2011 to 2016. <br>Magnetometer data is in RTN coordinates. Corresponding spacecraft position data is in HEEQ coordinates. 1 minute resolution. Column headings are as follows:<br>Timestamp, B_TOT, B_R, B_T, B_N, r_HEEQ, lat_HEEQ, lon_HEEQ, pos_xyz_HEEQ. <br>Pickle file was created using pandas. To unpickle into pandas DataFrame:<br>&gt; import pickle&gt; import pandas as pd<br>&gt; unpickled_df = pd.read_pickle("juno_2011_2016_rtn.pkl")<br><br>If data is used for peer reviewed scientific publications, please contact emma.davies@unh.edu.<br><i>We acknowledge the Juno MAG Principal Investigator, J.E.P. Connerney, and instrument team. We also thank the Planetary Data Science archive (PDS; https://pds.nasa.gov) for their distribution of the original Juno cruise phase data.</i><br><br>
DOI: 10.5439/1860939
title: microbasew.c1

description: The continuous baseline microphysical retrieval, Wacr-based (MICROBASEW) VAP is a baseline retrieval of cloud microphysical properties. MICROBASEW uses a combination of observations from the W-band, zenith pointing radar (WACR), the ceilometer, the micropulse lidar (MPL), the microwave radiometer (MWR) and a merged thermodynamic profile (MERGED SOUNDING) VAP in order to determine the profiles of liquid/ice water content (L/IWC), liquid/ice cloud particle effective radius (re) and cloud fraction.
DOI: 10.6084/m9.figshare.16947391.v2
title: JWP_SupTable

description: Sup table 1 List of samples used in this study "A DNA barcode database for the woody plants of Japan"<br>Sup table 2 A summary of the representation of genera and species, sequence success of each barcode region, and identification rate at the speceis, genus and family level for each plant family native to Japan included in the Japanese Woody Plant DNA Barcoding Database.
DOI: 10.34740/kaggle/dsv/3408825
title: Our World in Data - COVID-19
DOI: 10.6084/m9.figshare.16947391
title: JWP_SupTable

description: Sup table 1 List of samples used in this study "A DNA barcode database for the woody plants of Japan"<br>Sup table 2 A summary of the representation of genera and species, sequence success of each barcode region, and identification rate at the speceis, genus and family level for each plant family native to Japan included in the Japanese Woody Plant DNA Barcoding Database.
DOI: 10.6084/m9.figshare.16947391.v3
title: JWP_SupTable

description: Sup table 1 List of samples used in this study "A DNA barcode database for the woody plants of Japan"<br>Sup table 2 A summary of the representation of genera and species, sequence success of each barcode region, and identification rate at the speceis, genus and family level for each plant family native to Japan included in the Japanese Woody Plant DNA Barcoding Database.
DOI: 10.6084/m9.figshare.19517317
title: “Data of Wasted Mask Collection Robot”

description: “Data of Wasted Mask Collection Robot”
DOI: 10.6084/m9.figshare.19517336.v1
title: Additional file 2 of p52 signaling promotes cellular senescence

description: Additional file 2. Table S1.
DOI: 10.6084/m9.figshare.19517336
title: Additional file 2 of p52 signaling promotes cellular senescence

description: Additional file 2. Table S1.
DOI: 10.6084/m9.figshare.19517351.v1
title: Additional file 5 of Transcriptome analysis of blastoderms exposed to prolonged egg storage and short periods of incubation during egg storage

description: Additional file 5. Top expressed transcripts. FPKM values for the top 20 transcripts exhibiting the highest expression in each experimental group. Gene symbols are color coded to show the degree of overlap among the experimental groups. The total number of transcripts exhibiting a FPKM value over 20 for each experimental group is also shown.
DOI: 10.6084/m9.figshare.19517348
title: Additional file 4 of Transcriptome analysis of blastoderms exposed to prolonged egg storage and short periods of incubation during egg storage

description: Additional file 4. Differential expression data for experimental comparisons. For each comparison, differential expression output from CLC Genomics Workbench is presented.
DOI: 10.6084/m9.figshare.19517351
title: Additional file 5 of Transcriptome analysis of blastoderms exposed to prolonged egg storage and short periods of incubation during egg storage

description: Additional file 5. Top expressed transcripts. FPKM values for the top 20 transcripts exhibiting the highest expression in each experimental group. Gene symbols are color coded to show the degree of overlap among the experimental groups. The total number of transcripts exhibiting a FPKM value over 20 for each experimental group is also shown.
DOI: 10.6084/m9.figshare.19517348.v1
title: Additional file 4 of Transcriptome analysis of blastoderms exposed to prolonged egg storage and short periods of incubation during egg storage

description: Additional file 4. Differential expression data for experimental comparisons. For each comparison, differential expression output from CLC Genomics Workbench is presented.