cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2d0cwp
title: CCDC 2204902: Experimental Crystal Structure Determination

description: Related Article: Rong Zhang, Bing Wang, Fei Wang, Shu-Mei Chen, Jian Zhang|2022|Inorg.Chem.Front.|10|201|doi:10.1039/D2QI01949C
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: 10.5517/ccdc.csd.cc2d0cxq
title: CCDC 2204903: Experimental Crystal Structure Determination

description: Related Article: Rong Zhang, Bing Wang, Fei Wang, Shu-Mei Chen, Jian Zhang|2022|Inorg.Chem.Front.|10|201|doi:10.1039/D2QI01949C
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: 10.5517/ccdc.csd.cc2c97tr
title: CCDC 2183634: Experimental Crystal Structure Determination

description: Related Article: Shun-Zhong Tan, Peng Chen, Lei Zhu, Meng-Qi Gan, Qin Ouyang, Wei Du, Ying-Chun Chen|2022|J.Am.Chem.Soc.|144|22689|doi:10.1021/jacs.2c10004
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: 10.5517/ccdc.csd.cc2dj7z5
title: CCDC 2220157: Experimental Crystal Structure Determination

description: Related Article: Thomas J. Barnes, Jack Payne, Sebastian D. Pike|2023|Chem.Commun.|59|59|doi:10.1039/D2CC05478G
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: 10.5517/ccdc.csd.cc2djcnz
title: CCDC 2220271: Experimental Crystal Structure Determination

description: Related Article: Gyeong Un Kim, Hyunmi Cho, Jae Kyun Lee, Jae Yeol Lee, Jinsung Tae, Sun-Joon Min, Taek Kang, Yong Seo Cho|2023|Chem.Commun.|59|82|doi:10.1039/D2CC05133H
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: 10.5517/ccdc.csd.cc2cn8fr
title: CCDC 2194224: Experimental Crystal Structure Determination

description: Related Article: Shan-Shan Chen, Xiu-Ying Zheng, HaiQuan Tian, La-Sheng Long, Lan-Sun Zheng, Xiang-Jian Kong|2022|Inorg.Chem.|61|20365|doi:10.1021/acs.inorgchem.2c03007
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: 10.5517/ccdc.csd.cc2cn8jv
title: CCDC 2194227: Experimental Crystal Structure Determination

description: Related Article: Shan-Shan Chen, Xiu-Ying Zheng, HaiQuan Tian, La-Sheng Long, Lan-Sun Zheng, Xiang-Jian Kong|2022|Inorg.Chem.|61|20365|doi:10.1021/acs.inorgchem.2c03007
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: 10.5517/ccdc.csd.cc2cn8kw
title: CCDC 2194228: Experimental Crystal Structure Determination

description: Related Article: Shan-Shan Chen, Xiu-Ying Zheng, HaiQuan Tian, La-Sheng Long, Lan-Sun Zheng, Xiang-Jian Kong|2022|Inorg.Chem.|61|20365|doi:10.1021/acs.inorgchem.2c03007
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: 10.5281/zenodo.7479268
title: Local knowledge, exploitation, and conservation of the African golden cat Caracal aurata in Central Cameroon

description: The data contains the responses from a questionnaire survey. Data were collected in local communities in Cameroon using Kobocollect, to assess the knowledge of the locals on the African golden cat. The questions are listed in the first row and the following rows contain the answers of each participant. The first 13 columns contain socio-demographic questions and the remaining contain specific questions related to the African golden cat knowledge and exploitation. No abbreviation is used in the dataset except YCF (Council Forest of Yoko); MDNP (Mpem et Djim National park); 1 (Yes), 0(NO)
description: 812 Yaoundé
DOI: 10.5281/zenodo.7479267
title: Local knowledge, exploitation, and conservation of the African golden cat Caracal aurata in Central Cameroon

description: The data contains the responses from a questionnaire survey. Data were collected in local communities in Cameroon using Kobocollect, to assess the knowledge of the locals on the African golden cat. The questions are listed in the first row and the following rows contain the answers of each participant. The first 13 columns contain socio-demographic questions and the remaining contain specific questions related to the African golden cat knowledge and exploitation. No abbreviation is used in the dataset except YCF (Council Forest of Yoko); MDNP (Mpem et Djim National park); 1 (Yes), 0(NO)
description: 812 Yaoundé
DOI: 10.5281/zenodo.7036507
title: Revisiting Machine Learning based Test Case Prioritization for Continuous Integration

description: This repository contains a replication package for a research paper submitted to the 45th International Conference on Software Engineering (https://conf.researchr.org/home/icse-2023). We provide our code, data, and result for the ease of replicating our experiments. <strong>Code</strong> In the <strong>collect_data</strong> subdirectory, scripts for constructing TCP datasets are provided. We do dependency analysis using Understand (https://www.scitools.com/), so please download the related tools in advance. In the<strong> rl</strong> subdirectory, we provide the python implementation for algorithms RL, COLEMAN, PPO2-PO, ACER-PA, PPO1-LI. In the <strong>supervised_learning</strong> subdirectory, we provide implementations for MART, RankNet, RankBoost, CA, L-MART, which mainly rely on Ranklib (https://sourceforge.net/p/lemur/wiki/RankLib/.). We also provide implementation for DeepOrder. <strong>Data</strong> The <strong>origin</strong> subdirectory contains the original datasets collected from github using our scripts, including 11 projects. The <strong>smote</strong> subdirectory contains the datasets pre-processed by SMOTE. <strong>Result</strong> Results for <strong>RQ1</strong>, <strong>RQ2</strong>, <strong>RQ3</strong>, and <strong>threats to validity</strong> are provided in the corresponding subdirectories. Scripts for plotting figures are also provided. <strong>Reference</strong> We adopt code from previous work Learning-to-Rank vs Ranking-to-Learn: Strategies for Regression Testing in Continuous Integration (https://dl.acm.org/doi/abs/10.1145/3377811.3380369) Github repository: https://github.com/icse20/RT-CI Reinforcement Learning for Test Case Prioritization (https://ieeexplore.ieee.org/abstract/document/9394799) Github repository: https://github.com/moji1/tp_rl DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing (https://ieeexplore.ieee.org/abstract/document/9609187) Github repository: https://github.com/AizazSharif/DeepOrder-ICSME21 A Multi-Armed Bandit Approach for Test Case Prioritization in Continuous Integration Environments (https://ieeexplore.ieee.org/abstract/document/9086053) Github repository: https://github.com/jacksonpradolima/coleman4hcs
DOI: 10.5281/zenodo.7036468
title: Revisiting Machine Learning based Test Case Prioritization for Continuous Integration

description: This repository contains a replication package for a research paper submitted to the 45th International Conference on Software Engineering (https://conf.researchr.org/home/icse-2023). We provide our code, data, and result for the ease of replicating our experiments. <strong>Code</strong> In the <strong>collect_data</strong> subdirectory, scripts for constructing TCP datasets are provided. We do dependency analysis using Understand (https://www.scitools.com/), so please download the related tools in advance. In the<strong> rl</strong> subdirectory, we provide the python implementation for algorithms RL, COLEMAN, PPO2-PO, ACER-PA, PPO1-LI. In the <strong>supervised_learning</strong> subdirectory, we provide implementations for MART, RankNet, RankBoost, CA, L-MART, which mainly rely on Ranklib (https://sourceforge.net/p/lemur/wiki/RankLib/.). We also provide implementation for DeepOrder. <strong>Data</strong> The <strong>origin</strong> subdirectory contains the original datasets collected from github using our scripts, including 11 projects. The <strong>smote</strong> subdirectory contains the datasets pre-processed by SMOTE. <strong>Result</strong> Results for <strong>RQ1</strong>, <strong>RQ2</strong>, <strong>RQ3</strong>, and <strong>threats to validity</strong> are provided in the corresponding subdirectories. Scripts for plotting figures are also provided. <strong>Reference</strong> We adopt code from previous work Learning-to-Rank vs Ranking-to-Learn: Strategies for Regression Testing in Continuous Integration (https://dl.acm.org/doi/abs/10.1145/3377811.3380369) Github repository: https://github.com/icse20/RT-CI Reinforcement Learning for Test Case Prioritization (https://ieeexplore.ieee.org/abstract/document/9394799) Github repository: https://github.com/moji1/tp_rl DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing (https://ieeexplore.ieee.org/abstract/document/9609187) Github repository: https://github.com/AizazSharif/DeepOrder-ICSME21 A Multi-Armed Bandit Approach for Test Case Prioritization in Continuous Integration Environments (https://ieeexplore.ieee.org/abstract/document/9086053) Github repository: https://github.com/jacksonpradolima/coleman4hcs
DOI: 10.25919/fagp-nw58
title: Parkes observations for project P1170 semester 2022OCTS_13

description: We request 15.5 hours of observations with the Parkes UWL receiver to search for giant pulses (GPs) in all seven pulsars in the Southern sky which are known to exhibit this phenomenon. The UWL receiver is particularly well-suited to such a study, since some GPs from known emitters have been found to be very narrow-band and so the observed emission rate is likely to be highly frequency-dependent. Additionally, the wide bandwidth allows one to detect spectral structures such as the 'banding' effect recently observed in GPs from some pulsars, similar to that observed in the spectra of fast radio bursts. These observations should provide a wealth of information about the underlying GP emission mechanism, and a stringent test of current theories.
DOI: 10.34740/kaggle/dsv/4764951
title: AutoXGB
DOI: 10.34740/kaggle/dsv/4764948
title: LUFlow Network Intrusion Detection Data Set
DOI: 10.5281/zenodo.6990599
title: A convection-permitting hindcast based on the MOLOCH model and driven by ERA5: hourly precipitation data for years 1994 and 2011 (sample data)

description: Hourly estimates of rainfall accumulations were produced within the framework of the SPITBRAN Special project, which received computational resources from ECMWF (https://www.ecmwf.int/en/research/special-projects/spitbran-2018). Numerical gridded data at 2.5 km grid spacing were obtained with the MOLOCH model set in a convection-permitting mode and fed by ERA5 data as initial and boundary conditions for the period 1979-2019 and over the Italian domain. Hourly rainfall accumulations of such long-term hindcast are provided for the years 1994 and 2011. File format is Grib2.
description: Reference: Capecchi et al. "A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy." Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06633-2
description: {"references": ["Capecchi, V et al. \"A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.\" Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06633-2"]}
DOI: 10.5281/zenodo.6990598
title: A convection-permitting hindcast based on the MOLOCH model and driven by ERA5: hourly precipitation data for years 1994 and 2011 (sample data)

description: Hourly estimates of rainfall accumulations were produced within the framework of the SPITBRAN Special project, which received computational resources from ECMWF (https://www.ecmwf.int/en/research/special-projects/spitbran-2018). Numerical gridded data at 2.5 km grid spacing were obtained with the MOLOCH model set in a convection-permitting mode and fed by ERA5 data as initial and boundary conditions for the period 1979-2019 and over the Italian domain. Hourly rainfall accumulations of such long-term hindcast are provided for the years 1994 and 2011. File format is Grib2.
description: Reference: Capecchi et al. "A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy." Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06633-2
description: {"references": ["Capecchi, V et al. \"A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.\" Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06633-2"]}
DOI: 10.17196/novac.galeras.001
title: SO2 flux of GALERAS volcano, from the NOVAC data-base

description: This data-set presents volcanic gas emission data from NOVAC, the global Network for Observation of Volcanic and Atmospheric Change. For each volcano, data from one or several NOVAC Scanning mini-DOAS instruments are combined with meteorological information to derive daily statistics of total SO2 emission from the volcano. Different versions of the data exist, depending on the algorithms and meteorological information used in the evaluations. Data is kindly made publicly available by the volcanological observatories and institutions responsible for data collection and evaluation, on the condition that credit is given according to the NOVAC Data Use Agreement.
DOI: 10.17196/novac.nevadodelruiz.001
title: SO2 flux of NEVADO.DEL.RUIZ volcano, from the NOVAC data-base

description: This data-set presents volcanic gas emission data from NOVAC, the global Network for Observation of Volcanic and Atmospheric Change. For each volcano, data from one or several NOVAC Scanning mini-DOAS instruments are combined with meteorological information to derive daily statistics of total SO2 emission from the volcano. Different versions of the data exist, depending on the algorithms and meteorological information used in the evaluations. Data is kindly made publicly available by the volcanological observatories and institutions responsible for data collection and evaluation, on the condition that credit is given according to the NOVAC Data Use Agreement.
DOI: 10.26179/5e9d038c396f2
title: Antarctic Circumnavigation Expedition 2017: WAMOS data product
DOI: 10.17196/novac.sangay.001
title: SO2 flux of SANGAY volcano, from the NOVAC data-base

description: This data-set presents volcanic gas emission data from NOVAC, the global Network for Observation of Volcanic and Atmospheric Change. For each volcano, data from one or several NOVAC Scanning mini-DOAS instruments are combined with meteorological information to derive daily statistics of total SO2 emission from the volcano. Different versions of the data exist, depending on the algorithms and meteorological information used in the evaluations. Data is kindly made publicly available by the volcanological observatories and institutions responsible for data collection and evaluation, on the condition that credit is given according to the NOVAC Data Use Agreement.
DOI: 10.17196/novac.santiaguito.001
title: SO2 flux of SANTIAGUITO volcano, from the NOVAC data-base

description: This data-set presents volcanic gas emission data from NOVAC, the global Network for Observation of Volcanic and Atmospheric Change. For each volcano, data from one or several NOVAC Scanning mini-DOAS instruments are combined with meteorological information to derive daily statistics of total SO2 emission from the volcano. Different versions of the data exist, depending on the algorithms and meteorological information used in the evaluations. Data is kindly made publicly available by the volcanological observatories and institutions responsible for data collection and evaluation, on the condition that credit is given according to the NOVAC Data Use Agreement.
DOI: 10.17632/wy6745bjfj.2
title: ChEMU dataset for information extraction from chemical patents

description: The discovery of new chemical compounds and their synthesis process is of great importance to the chemical industry. Patent documents contain critical and timely information about newly discovered chemical compounds, providing a rich resource for chemical research in both academia and industry. Chemical patents are often the initial venues where a new chemical compound is disclosed. Only a small proportion of chemical compounds are ever published in journals and these publications can be delayed by up to 3 years after the patent disclosure. In addition, chemical patent documents usually contain unique information, such as reaction steps and experimental conditions for compound synthesis and mode of action. These details are crucial for the understanding of compound prior art, and provide a means for novelty checking and validation. Due to the high volume of chemical patents, approaches that enable automatic information extraction from these patents are in demand. To develop natural language processing methods for large-scale mining of chemical information from patent texts, a corpus is created providing chemical patent snippets and annotated entities and reaction steps.
DOI: 10.3886/e117430v1
title: Data and Code for "Interest-Rate Liberalization and Capital Misallocations"

description: We study the consequences of interest-rate liberalization in a two-sectorgeneral equilibrium model of China. The model captures a key feature ofChina's distorted financial system: state-owned enterprises (SOEs) havegreater incentive to expand production and easier access to credit thanprivate firms. In this second-best environment, interest-rate liberalization can improve capital allocations within each sector, but can also exacerbatemisallocations across sectors. Under calibrated parameters, theliberalization policy can reduce aggregate productivity and welfare unless otherpolicy reforms are also implemented to alleviate SOEs' distorted incentivesor improve private firms' credit access.
DOI: 10.5285/e41e9f83-b7ef-4a0e-b630-b73bdc881390
title: Vadose zone nitrate contained within cave drip waters and speleothems

description: This data set contains a record of nitrate concentrations and isotopic composition present within the drip waters and speleothem carbonate deposits of Cueva-cubío del Llanío, northern Spain. Data were collected between 2018 to 2020, and specifically address the nitrate composition of the cave drip waters, pool waters, rocks, soil, vegetation and contemporary speleothem carbonate. Calculations are also undertaken to assess the partitioning of nitrate between cave drip water and speleothem carbonate deposits. Data pertaining to speleothem nitrate content also extend to other cave locations (Pooles cavern, UK; Brown's Folly Mine, UK, Ease Gill Caverns, UK; Rukiesa cave, Ethiopia; Cueva Perlas, N. Spain). Sample analysis was performed at Lancaster University and the UK Centre for Ecology and Hydrology under NERC facility access grant LSMSF\CEH\L\125\11\2018. The data presented represent the full underlying dataset to Wynn et al., 2021, Chemical Geology: DOI.org/10.1016/j.chemgeo.2021.120172.
DOI: 10.6084/m9.figshare.21630002.v1
title: Hala Data dictioonary.docx

description: data was collected using online google form questionnaire from medical labrotarits ,nurses and medwives which carried out at khartoum state.
DOI: 10.5281/zenodo.3477842
title: Culture-Aware Music Recommendation Dataset

description: <strong>LFM-1b dataset extended by acoustic track features and cultural cues describing users</strong> This dataset is based on the LFM-1b dataset (cf. http://www.cp.jku.at/datasets/LFM-1b/), however, adds acoustic features describing the tracks to the original dataset as well as cultural aspects describing users (taken from Hofstede's six dimension model and the World Happiness Report) on the country-level. For the creation of the dataset, we extract all users for which the original dataset contains country information for. We extract the listening events of these users and match the tracks against the Spotify API to subsequently retrieve the acoustic features of these tracks (cf. [Spotify Audio Feature Description](https://developer.spotify.com/documentation/web-api/reference/object-model/#audio-features-object)). The final dataset contains only events of users with country information and tracks with acoustic features, which can be matched with the country-level data of the World Happiness Report and Hofstede's cultural dimensions to add cultural and socio-economic aspects for users. This new dataset contains 55,190 users 3,471,884 tracks including acoustic features 351,469,333 listening events of those users for tracks we have obtained acoustic features for Hofstede's cultural dimensions for 47 countries World Happiness Report (WHR) data for 164 countries <strong>Files</strong><br> All files are tab-separated, with no quoting of strings. The dataset contains the following files, whose content we describe in more detail in the following parts. * acoustic_features_lfm_id.tsv: acoustic features for all tracks in the dataset, identified by their LFM track identifier<br> * events.tsv: listening events for all users<br> * hofstede.tsv: Hofstede's cultural dimensions<br> * users.tsv: user metadata<br> * world_happiness_report_2018.tsv: World Happiness Report data For further information on the contents of these files, please cf. the Readme file. Please cite the following paper when using the dataset:<br> Zangerle, E., Pichl, M. and Schedl, M., 2020. User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. <em>Transactions of the International Society for Music Information Retrieval</em>, 3(1), pp.1–16. DOI: http://doi.org/10.5334/tismir.37
description: {"references": ["Zangerle, E., Pichl, M. and Schedl, M., 2020. User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. Transactions of the International Society for Music Information Retrieval, 3(1), pp.1\u201316. DOI: http://doi.org/10.5334/tismir.37"]}