cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19064808.v2
title: How you think about an emotion predicts how you regulate: an experience-sampling study

description: Emotion evaluations are assumed to play a crucial role in the emotion regulation process. We tested a postulate from our framework of emotion dysregulation (Nowak, U., Wittkamp, M. F., Clamor, A., & Lincoln, T. M. [2021]. Using the Ball-in-Bowl metaphor to outline an integrative framework for understanding dysregulated emotion. <i>Frontiers in Psychiatry</i>, <i>12</i>, 118), namely that the extent to which individuals evaluate an emotion as harmful and their personal resources to modify and accept/tolerate the emotion as sufficient predict the subsequent use of regulation strategies. Participants (<i>n </i>= 118) from a community sample took part in an experience-sampling assessment over 7 days including 10 daily paired measurements. The first measured momentary affective valence and arousal along with harmfulness evaluations and evaluations of personal resources to modify and accept/tolerate an emotion. The second followed three minutes later and measured emotion regulation strategies. The more harmful individuals evaluated an emotion, the more likely they were to use an emotion regulation strategy. The more harmful individuals evaluated an emotion, and the less sufficient they evaluated their personal resources to accept/tolerate an emotion, the more likely they were to use a maladaptive emotion regulation strategy. We conclude that emotions that people evaluate as harmful or difficult to accept are most likely to be regulated in a maladaptive manner. This implies that modifying beliefs about emotions could represent a promising treatment approach.
DOI: 10.6084/m9.figshare.19064808.v1
title: How you think about an emotion predicts how you regulate: an experience-sampling study

description: Emotion evaluations are assumed to play a crucial role in the emotion regulation process. We tested a postulate from our framework of emotion dysregulation (Nowak, U., Wittkamp, M. F., Clamor, A., & Lincoln, T. M. [2021]. Using the Ball-in-Bowl metaphor to outline an integrative framework for understanding dysregulated emotion. <i>Frontiers in Psychiatry</i>, <i>12</i>, 118), namely that the extent to which individuals evaluate an emotion as harmful and their personal resources to modify and accept/tolerate the emotion as sufficient predict the subsequent use of regulation strategies. Participants (<i>n </i>= 118) from a community sample took part in an experience-sampling assessment over 7 days including 10 daily paired measurements. The first measured momentary affective valence and arousal along with harmfulness evaluations and evaluations of personal resources to modify and accept/tolerate an emotion. The second followed three minutes later and measured emotion regulation strategies. The more harmful individuals evaluated an emotion, the more likely they were to use an emotion regulation strategy. The more harmful individuals evaluated an emotion, and the less sufficient they evaluated their personal resources to accept/tolerate an emotion, the more likely they were to use a maladaptive emotion regulation strategy. We conclude that emotions that people evaluate as harmful or difficult to accept are most likely to be regulated in a maladaptive manner. This implies that modifying beliefs about emotions could represent a promising treatment approach.
DOI: 10.34740/kaggle/dsv/4244476
title: Elon Musk Tweets
DOI: 10.5517/ccdc.csd.cc297xl2
title: CCDC 2122774: Experimental Crystal Structure Determination

description: Related Article: yuma nakagawa|2021|CSD Communication|||
description: Related Article: Yuma Nakagawa, Ryo Nishimura, Masakazu Morimoto, Satoshi Yokojima, Shinichiro Nakamura, Kingo Uchida|2022|Bull.Chem.Soc.Jpn.|95|1438|doi:10.1246/bcsj.20220169
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.cc297xm3
title: CCDC 2122775: Experimental Crystal Structure Determination

description: Related Article: yuma nakagawa|2021|CSD Communication|||
description: Related Article: Yuma Nakagawa, Ryo Nishimura, Masakazu Morimoto, Satoshi Yokojima, Shinichiro Nakamura, Kingo Uchida|2022|Bull.Chem.Soc.Jpn.|95|1438|doi:10.1246/bcsj.20220169
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.cc2bfw5v
title: CCDC 2158287: Experimental Crystal Structure Determination

description: Related Article: Atsuya Muranaka, Hayato Ban, Masaya Naito, Shinobu Miyagawa, Masahiro Ueda, Shin Yamamoto, Mei Harada, Hikaru Takaya, Masaki Kimura, Nagao Kobayashi,   Masanobu Uchiyama, Yuji Tokunaga|2022|Bull.Chem.Soc.Jpn.|95|1428|doi:10.1246/bcsj.20220195
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: 10.6084/m9.figshare.21201692
title: Lushan Data.zip

description: The study area is located in Lushan, Jiangxi, China. This area has a high topography in the northwest and a low topography in the southeast, and the land predominantly consists of mountainous hills
DOI: 10.6084/m9.figshare.21201692.v1
title: Lushan Data.zip

description: The study area is located in Lushan, Jiangxi, China. This area has a high topography in the northwest and a low topography in the southeast, and the land predominantly consists of mountainous hills
DOI: 10.6084/m9.figshare.19379906
title: Data and Results "Prevalence of sexually transmissible infections in adolescents treated in a family planning outpatient clinic for adolescents in the western Amazon."

description: The objective of this study was to evaluate the prevalence of STIs (chlamydia, gonorrhea, trichomoniasis, herpes simplex virus, syphilis, HIV, hepatitis B, hepatitis C) in adolescents treated at a family planning outpatient clinic in the western Amazon: Porto Velho, Rondônia, Brazil.
DOI: 10.5281/zenodo.6389848
title: Extending a High-Performance Prover to Higher-Order Logic

description: This is the package containing raw evaluation data and other related data for the submission<br> Extending a High-Performance Prover to Higher-Order Logic. The problems used for the evaluation are stored in the problems/ directory. Higher-order TPTP benchmarks are in TPTP_HO subdirectory, while first-order TPTP benchmarks are in TPTP_FO subdirectory. Sledgehammer benchmarks are stored in SH subdirectory. Figures 2 and 3 in the submission are automatically created using Python script get_table.py from scripts/ directory. This script processes raw data obtained from StarExec, which is stored in the results/ directory. To create Figure 2 use the following command: python3 scripts/get_table.py results/ scripts/tptp_sh.json Figure 3 is created using: python3 scripts/get_table.py results/ scripts/fo.json Directory e-26-4 contains both first-order (eprover) and higher-order (eprover-ho)<br> binaries compiled under Ubuntu 18-04. The directory e-26-4 can be packaged in<br> an archive and submitted to StarExec for evaluation. starexec_run_as4_serialize.sh<br> was used on SH benchmarks, starexec_run_as8.sh was used on higher-order TPTP<br> benchmarks. starexec_run_fo-boa and starexec_run_ho-boa were used on first-order<br> benchmarks. To run the scripts locally, environment variables STAREXEC_CPU_LIMIT<br> and STAREXEC_WALLCLOCK_LIMIT must be set. Note these scripts do not limit any resources<br> of lambdaE and rely on StarExec facilities for this purpose. lambdaE was compiled from the master_bce_merge, branch of eprover github (https://github.com/eprover/eprover),<br> with the git commit hash 693e48236d18e6b9a8f60db9ff2c56503ed16945.
DOI: 10.5281/zenodo.6389849
title: Extending a High-Performance Prover to Higher-Order Logic

description: This is the package containing raw evaluation data and other related data for the submission<br> Extending a High-Performance Prover to Higher-Order Logic. The problems used for the evaluation are stored in the problems/ directory. Higher-order TPTP benchmarks are in TPTP_HO subdirectory, while first-order TPTP benchmarks are in TPTP_FO subdirectory. Sledgehammer benchmarks are stored in SH subdirectory. Figures 2 and 3 in the submission are automatically created using Python script get_table.py from scripts/ directory. This script processes raw data obtained from StarExec, which is stored in the results/ directory. To create Figure 2 use the following command: python3 scripts/get_table.py results/ scripts/tptp_sh.json Figure 3 is created using: python3 scripts/get_table.py results/ scripts/fo.json Directory e-23-3 contains both first-order (eprover) and higher-order (eprover-ho)<br> binaries compiled under Ubuntu 18-04. The directory e-23-3 can be packaged in<br> an archive and submitted to StarExec for evaluation. starexec_run_as4_serialize.sh<br> was used on SH benchmarks, starexec_run_as8.sh was used on higher-order TPTP<br> benchmarks. starexec_run_fo-boa and starexec_run_ho-boa were used on first-order<br> FO benchmarks. To run the scripts locally, environment variables STAREXEC_CPU_LIMIT<br> and STAREXEC_WALLCLOCK_LIMIT must be set. Note these scripts do not limit any resources<br> of lambdaE and rely on StarExec facilities for this purpose. lambdaE was compiled from the wip-bce branch of eprover github (https://github.com/eprover/eprover),<br> with the git commit hash 88509bacca22c28f46bfe0890e3d4c8768bd6568.
DOI: 10.5281/zenodo.6498488
title: Extending a High-Performance Prover to Higher-Order Logic

description: This is the package containing raw evaluation data and other related data for the submission<br> Extending a High-Performance Prover to Higher-Order Logic. The problems used for the evaluation are stored in the problems/ directory. Higher-order TPTP benchmarks are in TPTP_HO subdirectory, while first-order TPTP benchmarks are in TPTP_FO subdirectory. Sledgehammer benchmarks are stored in SH subdirectory. Figures 2 and 3 in the submission are automatically created using Python script get_table.py from scripts/ directory. This script processes raw data obtained from StarExec, which is stored in the results/ directory. To create Figure 2 use the following command: python3 scripts/get_table.py results/ scripts/tptp_sh.json Figure 3 is created using: python3 scripts/get_table.py results/ scripts/fo.json Directory e-26-4 contains both first-order (eprover) and higher-order (eprover-ho)<br> binaries compiled under Ubuntu 18-04. The directory e-26-4 can be packaged in<br> an archive and submitted to StarExec for evaluation. starexec_run_as4_serialize.sh<br> was used on SH benchmarks, starexec_run_as8.sh was used on higher-order TPTP<br> benchmarks. starexec_run_fo-boa and starexec_run_ho-boa were used on first-order<br> benchmarks. To run the scripts locally, environment variables STAREXEC_CPU_LIMIT<br> and STAREXEC_WALLCLOCK_LIMIT must be set. Note these scripts do not limit any resources<br> of lambdaE and rely on StarExec facilities for this purpose. lambdaE was compiled from the master_bce_merge, branch of eprover github (https://github.com/eprover/eprover),<br> with the git commit hash 693e48236d18e6b9a8f60db9ff2c56503ed16945.
DOI: 10.6084/m9.figshare.21152446
title: MetaData.Yang

description: Meta data about the effects of N addition on SOC in China
DOI: 10.6084/m9.figshare.21152446.v1
title: MetaData.Yang

description: Meta data about the effects of N addition on SOC in China
DOI: 10.5519/qd.cizh4gdv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.46hkpwwo
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5517/ccdc.csd.cc2c4ybt
title: CCDC 2179496: Experimental Crystal Structure Determination

description: Related Article: Toshiki Ito, Masafumi Ueda, Masahiro Fujisaki, Aya Yoshimura, Takashi Shirahata, Yohji Misaki|2022|Bull.Chem.Soc.Jpn.|95|1419|doi:10.1246/bcsj.20220191
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.cc2c4ycv
title: CCDC 2179497: Experimental Crystal Structure Determination

description: Related Article: Toshiki Ito, Masafumi Ueda, Masahiro Fujisaki, Aya Yoshimura, Takashi Shirahata, Yohji Misaki|2022|Bull.Chem.Soc.Jpn.|95|1419|doi:10.1246/bcsj.20220191
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.cc2c8hjp
title: CCDC 2182912: Experimental Crystal Structure Determination

description: Related Article: Lyndsy A. Miller-Clark, Peter E. Christ, Brian T. Barbarini, Tong Ren|2022|Inorg.Chem.|61|14871|doi:10.1021/acs.inorgchem.2c02498
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.cc2c8hkq
title: CCDC 2182913: Experimental Crystal Structure Determination

description: Related Article: Lyndsy A. Miller-Clark, Peter E. Christ, Brian T. Barbarini, Tong Ren|2022|Inorg.Chem.|61|14871|doi:10.1021/acs.inorgchem.2c02498
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.cc2c8hlr
title: CCDC 2182914: Experimental Crystal Structure Determination

description: Related Article: Lyndsy A. Miller-Clark, Peter E. Christ, Brian T. Barbarini, Tong Ren|2022|Inorg.Chem.|61|14871|doi:10.1021/acs.inorgchem.2c02498
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.7110061
title: A phenomenological law for complex granular materials from Mohr-Coulomb theory

description: The compressed directory contains the data in .csv format used for the PCA analysis for each dataset (1, 2 and 3).
description: {"references": ["Cares and Falk, 2022"]}
DOI: 10.5281/zenodo.7110060
title: A phenomenological law for complex granular materials from Mohr-Coulomb theory

description: The compressed directory contains the data in .csv format used for the PCA analysis for each dataset (1, 2 and 3).
description: {"references": ["Cares and Falk, 2022"]}
DOI: 10.34740/kaggle/dsv/4244814
title: Mahanagar Yatayat Dataset
DOI: 10.6084/m9.figshare.21201764
title: Safe zone

description: Pneumothorax is one of the most frequent complications of computed tomography (CT)-guided lung biopsies. We aim to identify the safe zone of the needle-pleura angle during a CT-guided lung biopsy. Fifty-two patients underwent CT-guided lung biopsies between January 2020 and September 2022 (27 males, 25 females, median age 70 years). Right and left needle angles were measured and correlated to the incidence of pneumothorax. The minimum delta (δmin) was calculated as the absolute value of the difference between a 90° angle and the right and left angles. T-test p-values for δmin were conducted. We recorded 29 patients with pneumothorax whether intraprocedural and transient, postprocedural with minimal symptoms, or postprocedural and requiring a chest tube insertion. 32 patients had a δmin ≥ 10°, while 20 had a δmin &lt; 10°. Of the patients with a δmin &lt; 10°, 30% experienced pneumothorax compared to 71.8% in patients with δmin ≥ 10° (p= 0.0023). The study results show that as the needle’s angle deviates from the perpendicular, with an absolute value of more than 10°, the likelihood of pneumothorax increases significantly. A needle–pleura angle between 80° and 100° gives the operator a safe zone to reduce the risk of pneumothorax.
DOI: 10.34740/kaggle/dsv/4244914
title: Tales from the Pizza Guy
DOI: 10.5281/zenodo.6864299
title: Supplementary material for 'Deployment pipeline: a review, a recommended model and its usage'

description: This is the replication package of the paper "Deployment pipeline: a review, a recommended model and its usage". This replication package contains two .csv files. The file named SLR-initial-list.csv contains the initial list of papers retrieved from having applied a single iteration of forward snowballing using the work of Hilton et al. [1] as starting point. <br> The file named SLR-final-corpus.csv contains the final corpus resulting from having the study selection step as reported in the paper. This final corpus contains 32 papers.<br> Reference:<br> [1] Michael Hilton, Timothy Tunnell, Kai Huang, Darko Marinov, and Danny Dig. 2016. Usage, Costs, and Benefits of Continuous Integration in Open-Source Projects. In Proceedings of the 31st IEEE/ACM International Conference on Auto- mated Software Engineering (Singapore, Singapore) (ASE 2016). Association for Computing Machinery, New York, NY, USA, 426–437. https://doi.org/10.1145/ 2970276.2970358
DOI: 10.5281/zenodo.6864298
title: Supplementary material for 'Deployment pipeline: a review, a recommended model and its usage'

description: This is the replication package of the paper "Deployment pipeline: a review, a recommended model and its usage". This replication package contains two .csv files. The file named SLR-initial-list.csv contains the initial list of papers retrieved from having applied a single iteration of forward snowballing using the work of Hilton et al. [1] as starting point. <br> The file named SLR-final-corpus.csv contains the final corpus resulting from having the study selection step as reported in the paper. This final corpus contains 32 papers.<br> Reference:<br> [1] Michael Hilton, Timothy Tunnell, Kai Huang, Darko Marinov, and Danny Dig. 2016. Usage, Costs, and Benefits of Continuous Integration in Open-Source Projects. In Proceedings of the 31st IEEE/ACM International Conference on Auto- mated Software Engineering (Singapore, Singapore) (ASE 2016). Association for Computing Machinery, New York, NY, USA, 426–437. https://doi.org/10.1145/ 2970276.2970358
DOI: 10.5281/zenodo.7110300
title: Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)

description: This vcf file constitute underlying raw data material for the manuscript "Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)". <br> The SNP genotype data came from a whole-genome resequencing and were called using the Nipponbare IRGSP 1.0 rice reference genome. SNPs with a miss rate greater than 30% and minor allele frequency (MAF) less than 5% were removed. Heterozygous alleles were also excluded. Finally, 160,498 SNPs were selected and used in the GWAS analysis.
DOI: 10.5281/zenodo.7110299
title: Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)

description: This vcf file constitute underlying raw data material for the manuscript "Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)". <br> The SNP genotype data came from a whole-genome resequencing and were called using the Nipponbare IRGSP 1.0 rice reference genome. SNPs with a miss rate greater than 30% and minor allele frequency (MAF) less than 5% were removed. Heterozygous alleles were also excluded. Finally, 160,498 SNPs were selected and used in the GWAS analysis.
DOI: 10.26007/a718-6y64
title: Giotto Dust Impact Detector Observations near Comet Halley

description: This dataset contains data acquired by the Dust Impact Detection System (DIDSY) during the post-perihelion encounter with Comet Halley in 1986. Mounted on Giotto's front dust shield, the detectors determine the mass spectrum of the dust. An additional detector is located on the rear shield to monitor those dust particles that are able to penetrate the front dust shield. An ambient plasma monitor is also incorporated into DIDSY to measure the impact plasma generated by both dust and gas impacts on the spacecraft.
DOI: 10.5281/zenodo.7105855
title: 1-km Antarctic net snow accumulation predictions

description: <strong>Overview</strong> We provide static predictions of net snow accumulation over the Antarctic Ice Sheet derived using a machine learning approach. Here, we train random forest models to predict variability in net accumulation using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the random forest models. The data file includes all the predictors and predictands, an independent stake transect used for evaluation, four random forest gridded accumulation anomalies and their uncertainties, and a combined accumulation anomalies and its uncertainty. For a thorough description of how the predictions were generated generated see Medley et al. (2022).
DOI: 10.5281/zenodo.7105854
title: 1-km Antarctic net snow accumulation predictions

description: <strong>Overview</strong> We provide static predictions of net snow accumulation over the Antarctic Ice Sheet derived using a machine learning approach. Here, we train random forest models to predict variability in net accumulation using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the random forest models. The data file includes all the predictors and predictands, an independent stake transect used for evaluation, four random forest gridded accumulation anomalies and their uncertainties, and a combined accumulation anomalies and its uncertainty. For a thorough description of how the predictions were generated generated see Medley et al. (2022).