cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48665/udec/o4f7yd
title: Development and Validation of the BASE-63 inventory for Comprehensive Academic Stress Measurement

description: Academic stress (AS) reflects students' emotional, cognitive, and physiological reactions to academic hurdles. The COVID-19 era reshaped education, emphasizing digital platforms and limiting social interactions. Consequently, daily, and academic boundaries merged. Current tools, like SISCO-AS and SISCO-II-AS, require revisions to address present challenges and coping deficiencies, prompting a novel tool. This study develops and validates BASE-63 (Broad Academic Stress Evaluation). It's a comprehensive AS tool, combining academic and non-AS factors, offering a thorough, valid college AS assessment for Chilean university students. The study's method involved three phases: item creation, core execution, and evaluating BASE-63's psychometric traits. Evolving from SISCO-II-AS's 68 items, BASE-63 addressed urgent online learning demands. 501 students were surveyed, mostly females, across three Chilean universities. Preliminary steps encompassed focus groups for tool fine-tuning and validation. Pilot tests, factor analysis, and test-retests followed. BASE-63's framework was analyzed using Exploratory Factor Analysis and Structural Equation Modeling. The results unveil AS's complex structure, identifying four stressor types. They cover academic, socioeconomic, general social, and classroom interaction stressors. Gender disparities appear in stress reactions, indicating diverse management strategies. Reaction areas are General Stress, Physical Exhaustion, and Social Conflict. The tool showcases strong validity, factor loadings, and unwavering internal consistency. Notable strengths include a comprehensive AS assessment, but biases and missing longitudinal insights limit it. This research underscores the importance of nuanced stress understanding and advocating tailored interventions. In summary, this research illuminates AS nuances among Chilean students, highlighting diverse stressors and reactions. The validated BASE-63 offers a solid foundation for accurate AS assessment and management. Recognizing gender and socio-economic variations emphasize tailored interventions. These insights collectively enhance university student well-being, bolstering their academic success and mental health.
DOI: 10.18112/openneuro.ds004715.v1.0.1
title: Symmetry Luminance BOLD fMRI
DOI: 10.5258/soton/d2756
title: Dataset in support of the thesis "If I can find the right structure and place for me to thrive, I know I can do amazing things": A qualitative exploration of the experience of ADHD for young people and caregivers

description: Data for the empirical paper, including: anonymised participant contributions and demographic data; reflexive journal; initial coding. Data for the metasynthesis, including: coding map, reflexive journal, all data from each included paper, all CASP quality analysis forms.
DOI: 10.25824/redu/gntz3x
title: ARTIGO_1_REDU.pdf

description: Arquivo com resumo da revisão e quadros com achados
DOI: 10.25824/redu/bu1kgp
title: Procedimentos para inserção de sondas de alimentação transpilórica em recém-nascidos e lactentes: uma revisão sistemática

description: A sonda transpilórica em recém-nascido (RN) pré-termo ou criticamente doente é indicado quando há falha na alimentação gástrica, principalmente decorrente de intolerância alimentar grave. Entretanto, há na literatura uma diversidade de abordagens e técnicas, principalmente ao se considerar as particularidades de RNs e lactentes. Objetivo: Realizar uma revisão sistemática sobre os métodos para inserção de sonda de alimentação transpilórica em RN e lactentes. Métodos: Revisão sistemática de estudos experimentais e não experimentais que abordaram a técnica de medida, o procedimento de inserção e a taxa de sucesso da sondagem transpilórica em RN e lactentes, conforme a estratégia PICO (paciente/intervenção/comparação/resultado) e de acordo com o protocolo PRISMA. As bases de dados utilizadas para pesquisa foram: Pubmed e Pubmed PCM, BVS/Bireme, CINAHL, Scopus, Web of Science, Embase, Cochrane Library e PROQUEST. Não houve limites de ano de publicação ou linguagem. Resultados: Após leitura e análise inicial dos títulos e resumos dos 412 artigos encontrados pela estratégia de busca, foram selecionados 52 artigos para leitura completa e extração de dados. Desses, seis foram incluídos na revisão. A taxa de sucesso variou entre 70% e 100%. No entanto, observou-se uma variabilidade importante de materiais utilizados, técnicas de medida e inserção, além de divergências conceituais no que se considerou a posição correta da sonda. Implicações para prática clínica: Verificou-se que as estratégias mais presentes nos estudos para atingir adequadamente o posicionamento foram: a medida glabela-calcâneo, a insuflação de ar quando a sonda atinge o estômago, o posicionamento do paciente em decúbito lateral direito e a confirmação com raio-x em associação a outros métodos. Implicações para pesquisa: O estabelecimento da medida e técnica ideais para inserção da sonda de alimentação transpilórica ainda pode ser considerado uma limitação assistencial envolvendo a nutrição e a segurança de RN e lactentes. Técnicas de inserção, utilizando a insuflação de ar gástrico, por exemplo, podem ser estudadas como medida adjuvante no procedimento de sondagem transpilórica em RN e lactentes.
DOI: 10.14457/nida.the.1995.12
title: โครงสร้างทางเศรษฐกิจและสังคมของชุมชนบ้านอรัญญิกปัจจุบัน
DOI: 10.14457/nida.the.2018.174
title: บทบาทครอบครัวกับการทารุณกรรมทางเพศเด็ก
DOI: 10.14457/nida.the.2006.260
title: Major factors affecting policy implementation on juvenile delinquent rehabilitation
DOI: 10.14457/nida.the.2014.70
title: การเฝ้าระวังคุณภาพน้ำในแม่น้ำเจ้าพระยา
DOI: 10.25387/g3.24018756
title: Supplemental Material for Bapat et al., 2023

description: <strong>Figure S1.</strong> Dot plots representing self-alignment of a masked Ga1 genomic region in 5 representative groups. <strong>Figure S2.</strong> Dot plots representing pair-wise alignment of chromosome 4 of B73 with representative members of other groups. <strong>Figure S3. </strong>Phylogenetic tree of ZmGa1P-like sequences in B73. <strong>Figure S4.</strong> Tree showing ZmGa1P hits in teosinte genomes. <strong>Table S1.</strong> List of pangenome markers and their positions that define regions of variability around and including the <em>Ga1</em> locus region across different NAM genomes. <strong>Table S2.</strong> List of pangenome markers and their positions that have been used to define x and y coordinates of sequences in pairwise dotplots. <strong>Table S3.</strong> Summary of<em> ZmPme3</em> and <em>ZmGa1P</em> BLAST hits and their genomic positions in Tripsacum and Teosinte genomes. <strong>Table S4.</strong> List of Blast hits in Tripsacum and Teosinte genomes for<em> ZmGa1P</em> as query sequence. <strong>Table S5</strong>. List of Blast hits in Tripsacum and Teosinte genomes for <em>ZmPme3</em> as query sequence. <strong>Table S6.</strong> <em>ZmPme3</em> and <em>ZmGa1P</em> genomic sequence BLAST analysis results in genes and proteins with sequence similarity in several grass species
DOI: 10.25387/g3.24018756.v1
title: Supplemental Material for Bapat et al., 2023

description: <strong>Figure S1.</strong> Dot plots representing self-alignment of a masked Ga1 genomic region in 5 representative groups. <strong>Figure S2.</strong> Dot plots representing pair-wise alignment of chromosome 4 of B73 with representative members of other groups. <strong>Figure S3. </strong>Phylogenetic tree of ZmGa1P-like sequences in B73. <strong>Figure S4.</strong> Tree showing ZmGa1P hits in teosinte genomes. <strong>Table S1.</strong> List of pangenome markers and their positions that define regions of variability around and including the <em>Ga1</em> locus region across different NAM genomes. <strong>Table S2.</strong> List of pangenome markers and their positions that have been used to define x and y coordinates of sequences in pairwise dotplots. <strong>Table S3.</strong> Summary of<em> ZmPme3</em> and <em>ZmGa1P</em> BLAST hits and their genomic positions in Tripsacum and Teosinte genomes. <strong>Table S4.</strong> List of Blast hits in Tripsacum and Teosinte genomes for<em> ZmGa1P</em> as query sequence. <strong>Table S5</strong>. List of Blast hits in Tripsacum and Teosinte genomes for <em>ZmPme3</em> as query sequence. <strong>Table S6.</strong> <em>ZmPme3</em> and <em>ZmGa1P</em> genomic sequence BLAST analysis results in genes and proteins with sequence similarity in several grass species
DOI: 10.14457/nida.the.2007.114
title: การใช้กากตะกอนนมแห้งผสมในอาหารนกกระทาไข่
DOI: 10.14457/nida.the.1994.2
title: ปัจจัยที่มีผลต่อความพึงพอใจในการปฏิบัติงานครูอาสาสมัครการศึกษานอกโรงเรียนในเขตพื้นที่ 5 จังหวัดชายแดนภาคใต้
DOI: 10.14457/nida.the.1991.4
title: การมีส่วนร่วมในกิจกรรมของชุมชน : ศึกษาเปรียบเทียบผู้เคยย้ายถิ่นและผู้ที่ไม่เคยย้ายถิ่น
DOI: 10.14457/nida.the.1993.6
title: การศึกษาเพื่อกำหนดคำนิยามเมืองในประเทศไทย
DOI: 10.14457/nida.the.1992.4
title: อิทธิพลของภาวะผู้นำของคณบดีที่มีต่อประสิทธิผลของการบริหารงานวิชาการในสถาบันอุดมศึกษาเอกชน
DOI: 10.14457/nida.the.1988.3
title: ประเมินผลนโยบายพัฒนาประชาธิปไตย : เปรียบเทียบอุดมการณ์ของประชาชนในเขตกับนอกเขตโครงการศาลาประชาธิปไตยของอำเภอพนมสารคาม จังหวัดฉะเชิงเทรา
DOI: 10.14457/nida.the.1999.13
title: การมีส่วนร่วมของประชาชนในโครงการอนุรักษ์สภาพป่าไม้ในพื้นที่อำเภออมก๋อยอันเนื่องมาจากพระราชดำริ
DOI: 10.14457/nida.the.2014.71
title: บุพปัจจัยและความแตกต่างของความเชื่อถือไว้วางใจในทีมและความผูกพันต่อองค์การระหว่างพนักงานในทีมดั้งเดิมและทีมเสมือนจริง : กรณีศึกษาอุตสาหกรรมสายการบิน
DOI: 10.14457/nida.the.1999.14
title: ผู้ช่วยแม่บ้านไทยในฮ่องกง : ภูมิหลัง สภาพการทำงาน และแนวทางในการจัดทำ "โครงการแรงงานคืนถิ่น"
DOI: 10.5281/zenodo.8283417
title: Hamouda GRL Open Research

description: DATA STATEMENT FOR GRL ARTICLE: On Convection Precipitation during Vb-events in present and Warmer Climate. *run_CPS_spate_dkrz_SPATEsetup.sh: is the namelist to downscale ERA-Interim to convective scale (CPS) using COSMO-CLM<br> *run_int2lm_SPATE_CPS: is the namelist to prepare input data for CPS downscaling using COSMO-CLM<br> *run_int2lm_CMIP6.sh: is the namelist to prepare input data for downscaling with convection parameterization using COSMO-CLM<br> *f1 and f2 are from Poujol et al. 2019 to detect convection.<br> *clusterfx.py is a python function to cluster GPH data using k-means.<br> * Vb-tracks are provided in the text files for ERAI and CMIP6 historical and SSP585.<br>
DOI: 10.5281/zenodo.8283416
title: Hamouda GRL Open Research

description: DATA STATEMENT FOR GRL ARTICLE: On Convection Precipitation during Vb-events in present and Warmer Climate. *run_CPS_spate_dkrz_SPATEsetup.sh: is the namelist to downscale ERA-Interim to convective scale (CPS) using COSMO-CLM<br> *run_int2lm_SPATE_CPS: is the namelist to prepare input data for CPS downscaling using COSMO-CLM<br> *run_int2lm_CMIP6.sh: is the namelist to prepare input data for downscaling with convection parameterization using COSMO-CLM<br> *f1 and f2 are from Poujol et al. 2019 to detect convection.<br> *clusterfx.py is a python function to cluster GPH data using k-means.<br> * Vb-tracks are provided in the text files for ERAI and CMIP6 historical and SSP585.<br>
DOI: 10.6084/m9.figshare.24033471
title: Protocol Paper.docx

description: Protocol of research v 1.
DOI: 10.6084/m9.figshare.24033471.v1
title: Protocol Paper.docx

description: Protocol of research v 1.
DOI: 10.6084/m9.figshare.24033402
title: Kragel 270 participant dataset (18 studies x 15 participants) from Kragel et al. 2018 Nature Neuroscience

description: <br><br>This dataset is an fmri_data object class object created using CANlab tools (canlab.github.io). It contains 270 single-participant fMRI contrast maps across 18 studies (with 15 participants per study). Studies are grouped into three domains: Pain, Cognitive Control, and Negative Emotion, with 9 studies each. Each domain includes 3 subdomains, with 3 studies each. <br>The dataset is from Kragel et al. 2018, Nature Neuroscience. Kragel, P. A., Kano, M., Van Oudenhove, L., Ly, H. G., Dupont, P., Rubio, A., Delon-Martin, C., Bonaz, B. L., Manuck, S. B., Gianaros, P. J., Ceko, M., Losin, R. E. A., Woo, C. W., Nichols, T. E., Wager, T. D. (2018). Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex. <i>Nature neuroscience</i>. 21(2): 283-289. PMCID: PMC5801068.https://www.nature.com/articles/s41593-017-0051-7it's also stored on Neurovault.org as collection #504. Using CANlab tools , you could get it using:[files_on_disk, url_on_neurovault, mycollection, myimages] = retrieve_neurovault_collection(504);This file also has metadata that is not on Neurovault, in data_obj.metadata_table. If you save this file somewhere on your Matlab path, you'll be able to reload and reuse the dataset easily.<pre>[test_images, names] = load_image_set('kragel18_alldata', 'noverbose');<br></pre><br>270×6 tableDomain Subdomain imagenames Studynumber Orig_Studynumber StudyCodes_______________ ___________ __________________________ ___________ ________________ {'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }.... etcNote: Individual participant images are <i>not</i> on the same magnitude scale across studies. For most applications, you will want to normalize the scale (e.g., L2 norm or z-score images).e.g., in CANlab tools:data_obj = rescale(data_obj, 'l2norm_images');A .nii file format image is also included. These images are rescaled by dividing by the L2 norm of each image. <br>
DOI: 10.6084/m9.figshare.24033402.v1
title: Kragel 270 participant dataset (18 studies x 15 participants) from Kragel et al. 2018 Nature Neuroscience

description: <br>Note: Individual participant images are <i>not</i> on the same magnitude scale across studies. For most applications, you will want to normalize the scale (e.g., L2 norm or z-score images).<br>This dataset is an fmri_data object class object created using CANlab tools (canlab.github.io). It contains 270 single-participant fMRI contrast maps across 18 studies (with 15 participants per study). Studies are grouped into three domains: Pain, Cognitive Control, and Negative Emotion, with 9 studies each. Each domain includes 3 subdomains, with 3 studies each. <br>The dataset is from Kragel et al. 2018, Nature Neuroscience. Kragel, P. A., Kano, M., Van Oudenhove, L., Ly, H. G., Dupont, P., Rubio, A., Delon-Martin, C., Bonaz, B. L., Manuck, S. B., Gianaros, P. J., Ceko, M., Losin, R. E. A., Woo, C. W., Nichols, T. E., Wager, T. D. (2018). Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex. <i>Nature neuroscience</i>. 21(2): 283-289. PMCID: PMC5801068.https://www.nature.com/articles/s41593-017-0051-7it's also stored on Neurovault.org as collection #504. Using CANlab tools , you could get it using:[files_on_disk, url_on_neurovault, mycollection, myimages] = retrieve_neurovault_collection(504);However, this may take a while to download, and you'd have to add metadata for the study category labels to use it. Therefore, we suggest you use the function below. It saves a file on your hard drive the first time you run it:kragel_2018_nat_neurosci_270_subjects_test_images.matThis file also has metadata that is not necessarily on Neurovault. If you save this file somewhere on your Matlab path, you'll be able to reload and reuse the dataset easily.<pre>[test_images, names] = load_image_set('kragel18_alldata', 'noverbose');<br></pre><br>270×6 table<br>Domain Subdomain imagenames Studynumber Orig_Studynumber StudyCodes_______________ ___________ __________________________ ___________ ________________ ____________________<br>{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }
DOI: 10.34740/kaggle/dsv/6363936
title: Amazon UK Grocery Dataset (Un)Supervised Learning
DOI: 10.5281/zenodo.7957159
title: Genes associated with plant growth-promoting traits (PGPTs) in acidobacteriota MAGs

description: The prevalence and abundance of Acidobacteriota raise concerns about their ecological function and metabolic activity in the environment. Studies have reported the potential of some members of Acidobacteriota to interact with plants and play a significant role in biogeochemical cycles. However, their role in this context has not been extensively studied. Here, we performed a comprehensive genomic analysis of 758 metagenome-assembled genomes (MAGs) of Acidobacteriota, revealing a high frequency of plant growth-promoting traits (PGPTs) genes.
DOI: 10.5281/zenodo.7957158
title: Genes associated with plant growth-promoting traits (PGPTs) in acidobacteriota MAGs

description: The prevalence and abundance of Acidobacteriota raise concerns about their ecological function and metabolic activity in the environment. Studies have reported the potential of some members of Acidobacteriota to interact with plants and play a significant role in biogeochemical cycles. However, their role in this context has not been extensively studied. Here, we performed a comprehensive genomic analysis of 758 metagenome-assembled genomes (MAGs) of Acidobacteriota, revealing a high frequency of plant growth-promoting traits (PGPTs) genes.
DOI: 10.5281/zenodo.8283244
title: MS3 Sentinel-1 Egadi

description: This dataset is only a portion of an unprecedented dataset for vessel detection in Synthetic Aperture Radar (SAR) images, capitalizing on multi-frequency and multi-polarization data obtained from the Sentinel-1, COSMO-SkyMed, and SAOCOM missions
DOI: 10.5281/zenodo.8283243
title: MS3 Sentinel-1 Egadi

description: This dataset is only a portion of an unprecedented dataset for vessel detection in Synthetic Aperture Radar (SAR) images, capitalizing on multi-frequency and multi-polarization data obtained from the Sentinel-1, COSMO-SkyMed, and SAOCOM missions
DOI: 10.14469/hpc/12785
title: Exo-TS1 (PCM – chlorobenzene) Replication 1

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12998
title: Endo_TS1_G16_PCM_chlorobenzene_def2tzvpp_v_01

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/13024
title: Endo_TS1_G16_PCM_chlorobenzene_WB97XD_def2tzvpp_v_01

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12965
title: Endo_TS1_G16_PCM_carbon_tetrachloride_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/12970
title: Endo_TS1_G16_PCM_carbon_tetrachloride_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12966
title: Endo_TS1_G16_PCM_chlorobenzene_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/12971
title: Endo_TS1_G16_PCM_chlorobenzene_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12967
title: Endo_TS1_G16_PCM_chloroform_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/12972
title: Endo_TS1_G16_PCM_chloroform_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12968
title: Endo_TS1_G16_PCM_DCM_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/12973
title: Endo_TS1_G16_PCM_DCM_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12969
title: Endo_TS1_G16_PCM_dichloroethane_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/12974
title: Endo_TS1_G16_PCM_dichloroethane_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12976
title: Endo_TS1_G03_PCM_carbon_tetrachloride_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.6084/m9.figshare.24032196
title: Data from "Spatial filtering strategies for mitigating sampling bias in species distribution models"

description: Results obtained and analysed in Lamboley & Fourcade, Spatial filtering strategies for mitigating sampling bias in species distribution models. Briefly, we used two virtual species with contrasting levels of specialisation to explore the impact of spatial filtering distances on the performance of ecological niche models. This investigation was conducted across a spectrum of modelling conditions, encompassing diverse types and degrees of bias, as well as varying sample sizes.Results reporting the overlap between modelled and true distributions:Unbiased_distribution.csv: results for the models trained from unbiased, i.e. randomly sampled, datasetsBiased_corrected_distribution.csv: results for the models trained from biased datasets, corrected with various spatial filtering distancesResults reporting the overlap between modelled and true response curves:Unbiased_response_curves.csv: results for the models trained from unbiased, i.e. randomly sampled, datasetsBiased_corrected_response_curves.csv: results for the models trained from biased datasets, corrected with various spatial filtering distances
DOI: 10.14469/hpc/12977
title: Endo_TS1_G03_PCM_carbon_tetrachloride_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/12978
title: Endo_TS1_G03_PCM_chlorobenzene_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/12979
title: Endo_TS1_G03_PCM_chlorobenzene_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12980
title: Endo_TS1_G03_PCM_chloroform_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/12981
title: Endo_TS1_G03_PCM_chloroform_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/12982
title: Endo_TS1_G03_PCM_DCM_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/12983
title: Endo_TS1_G03_PCM_DCM_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/13004
title: Endo_TS1_G16_PCM_dichloroethane_def2qzvpp_v_01_reactant

description: Gaussian 16 (C01) calculation
DOI: 10.14469/hpc/13005
title: Endo_TS1_G16_PCM_dichloroethane_def2qzvpp_v_01

description: Kinisot.py command line KIE calculation: "python3 /usr/local/bin/Kinisot.py REACTANT.log TS.log --iso ATOM -t TEMP", where ATOM is number label of transferred proton and TEMP is the temperature (Kelvin)
DOI: 10.14469/hpc/13006
title: Endo_TS1_G16_PCM_chloroform_def2qzvpp_v_01_reactant

description: Gaussian 16 (C01) calculation