cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7281/t1/fis2qt/lifqwf
title: 00. ReadMe.txt

description: ReadMe file about data, variables, code and runscript.
DOI: 10.34848/fk2/8pktdi/rwfshl
title: ecuador.gml
DOI: 10.34848/fk2/8pktdi/kegjpp
title: venezuela.gml
DOI: 10.34848/fk2/8pktdi/6yn0sw
title: brasil.gml
DOI: 10.34848/fk2/8pktdi/zexibf
title: bolivia.gml
DOI: 10.34848/fk2/8pktdi/mqhakn
title: chile.gml
DOI: 10.34848/fk2/8pktdi/7gpiuh
title: latam.gml
DOI: 10.34848/fk2/8pktdi/3o1tto
title: argentina.gml
DOI: 10.34848/fk2/8pktdi/jnt1ap
title: puerto rico.gml
DOI: 10.34848/fk2/8pktdi/pcztef
title: colombia.gml
DOI: 10.34848/fk2/8pktdi/sy1nnc
title: panama.gml
DOI: 10.34848/fk2/8pktdi/nvaqhr
title: cuba.gml
DOI: 10.34848/fk2/8pktdi/0ufbbt
title: mexico.gml
DOI: 10.34848/fk2/8pktdi/wne8fz
title: honduras.gml
DOI: 10.34848/fk2/8pktdi/ryomgd
title: costa rica.gml
DOI: 10.34848/fk2/8pktdi/unkxyh
title: el salvador.gml
DOI: 10.34848/fk2/8pktdi/i80ne2
title: nicaragua.gml
DOI: 10.34848/fk2/8pktdi/nept2x
title: paraguay.gml
DOI: 10.34848/fk2/8pktdi/1e3dqq
title: uruguay.gml
DOI: 10.34848/fk2/8pktdi/zas0x2
title: rep dominicana.gml
DOI: 10.34848/fk2/8pktdi/k33jgs
title: peru.gml
DOI: 10.34848/fk2/8pktdi/rb3l7t
title: guatemala.gml
DOI: 10.34848/fk2/n80qw0/5z3xdk
title: argentina.tab
DOI: 10.34848/fk2/n80qw0/1acdle
title: peru.tab
DOI: 10.34848/fk2/n80qw0/jjltfg
title: uruguay.tab
DOI: 10.34848/fk2/n80qw0/rylozf
title: mexico.tab
DOI: 10.34848/fk2/n80qw0/2zckt9
title: puerto rico.tab
DOI: 10.34848/fk2/n80qw0/lezpfi
title: cuba.tab
DOI: 10.34848/fk2/n80qw0/9lq0xq
title: brasil.tab
DOI: 10.34848/fk2/n80qw0/y3im8t
title: el salvador.tab
DOI: 10.34848/fk2/n80qw0/xqle6p
title: rep dominicana.tab
DOI: 10.34848/fk2/n80qw0/1uyptf
title: chile.tab
DOI: 10.34848/fk2/n80qw0/c1ekuy
title: paraguay.tab
DOI: 10.34848/fk2/n80qw0/pd51p3
title: colombia.tab
DOI: 10.34848/fk2/n80qw0/5i0mke
title: panama.tab
DOI: 10.34848/fk2/n80qw0/iio3ny
title: costa rica.tab
DOI: 10.34848/fk2/n80qw0/veepho
title: nicaragua.tab
DOI: 10.34848/fk2/n80qw0/oz29ca
title: ecuador.tab
DOI: 10.34848/fk2/n80qw0/zgpunm
title: bolivia.tab
DOI: 10.34848/fk2/n80qw0/ap1yxr
title: venezuela.tab
DOI: 10.34848/fk2/n80qw0/7vwvsr
title: guatemala.tab
DOI: 10.34848/fk2/n80qw0/i7ot7d
title: honduras.tab
DOI: 10.5258/soton/d1417
title: Dataset for PhD thesis "Cell designs and methods for characterisation of lithium protective membranes, solid electrolytes and beyond"

description: Dataset for PhD thesis "Cell designs and methods for characterisation of lithium protective membranes, solid electrolytes and beyond" by Nina Meddings. Awarded 2020
DOI: 10.7910/dvn/xd8ool/w6siqs
title: 04_mht.do

description: :unav
DOI: 10.7910/dvn/xd8ool/ttdvc8
title: 01_data_reshape_wide_long.do

description: :unav
DOI: 10.7910/dvn/xd8ool/sionxv
title: 03_figures.do

description: :unav
DOI: 10.7910/dvn/xd8ool/jquqco
title: covid_fuels_hostility_clean_long.tab

description: :unav
DOI: 10.7910/dvn/xd8ool/unpsfp
title: covid_fuels_hostility_clean.tab

description: :unav
DOI: 10.7910/dvn/xd8ool/u1rizx
title: 02_tables.do

description: :unav
DOI: 10.7910/dvn/xd8ool/yeqlpm
title: 00_master_do_file_READ_ME.do

description: READ ME master do file
DOI: 10.7910/dvn/xd8ool/rbvyq6
title: 00_master_do_file_READ_ME.do

description: Main file, READ ME
DOI: 10.7910/dvn/xd8ool/ayvysq
title: 02_tables.do

description: :unav
DOI: 10.7910/dvn/xd8ool/k0cezy
title: 05_tests_in_text.do

description: :unav
DOI: 10.7910/dvn/xd8ool/nwl1xx
title: Webscraping_press_statements_czech_government_english_czech.docx

description: See description in Supplementary Information section 1.7
DOI: 10.7910/dvn/xd8ool/7zyal9
title: Twitter_czech_politicians_covid_china.tab

description: See description in Supplementary Information section 1.7
DOI: 10.7910/dvn/xd8ool/z8usin
title: 03_figures.do

description: :unav
DOI: 10.7910/dvn/xd8ool/gfhmkn
title: 01_data_reshape_wide_long.do

description: :unav
DOI: 10.7910/dvn/xd8ool/p4rqqb
title: 02_tables.do

description: :unav
DOI: 10.7910/dvn/xd8ool/ewkj7u
title: 00_master_do_file_READ_ME.do

description: Main file - READ ME
DOI: 10.7910/dvn/xd8ool/hmi368
title: covid_fuels_hostility_clean.tab

description: Main dataset (wide format)
DOI: 10.7910/dvn/xd8ool/3rmwhs
title: covid_fuels_hostility_clean_long.tab

description: Main dataset (long format); obtained from covid_fuels_hostility_clean.dta by running 01_data_reshape_wide_long.do
DOI: 10.7910/dvn/xd8ool/qfzwij
title: 04_mht.do

description: see Supplementary Information section 1.6
DOI: 10.7910/dvn/xd8ool/hnxfgb
title: 03_figures.do

description: :unav
DOI: 10.7910/dvn/xd8ool/cqecjw
title: covid_fuels_hostility_clean_long.tab

description: Main dataset in the long format, obtained from covid_fuels_hostility_clean.dta by running 01_data_reshape_wide_long.do
DOI: 10.7910/dvn/xd8ool/c3erxx
title: 02_tables.do

description: :unav
DOI: 10.7910/dvn/xd8ool/y9b60v
title: 00_master_do_file_READ_ME.do

description: :unav
DOI: 10.7910/dvn/xd8ool/15gbb2
title: 05_tests_in_text.do

description: :unav
DOI: 10.7910/dvn/xd8ool/audz8c
title: covid_fuels_hostility_clean.tab

description: MAIN dataset
DOI: 10.5281/zenodo.3898626
title: Modelling studies on preventive measures and treatments for Covid-19

description: In the course of our PubMed searches and prepreints from MedRxiv, we identified a number of modelling studies on preventive measures and treatments for Covid-19. This file will be updated regularly.
DOI: 10.5281/zenodo.3823991
title: Modelling studies on preventive measures and treatments for Covid-19

description: In the course of our PubMed searches and prepreints from MedRxiv, we identified a number of modelling studies on preventive measures and treatments for Covid-19. This file will be updated regularly.
DOI: 10.3886/e193183
title: Confidence in China's Political System and Covid-19

description: This data set contains the data of two waves of survey conducted in 2020 and 2022 on how was Chinese people’s confidence in the political system linked to their perception on Covid infection risk, perception of health threat of Covid infection, lockdown experience, and attitudes towards the dynamic Zero-Covid policy.<b></b>
DOI: 10.3886/e193183v1
title: Confidence in China's Political System and Covid-19

description: This data set contains the data of two waves of survey conducted in 2020 and 2022 on how was Chinese people’s confidence in the political system linked to their perception on Covid infection risk, perception of health threat of Covid infection, lockdown experience, and attitudes towards the dynamic Zero-Covid policy.<b></b>
DOI: 10.6084/m9.figshare.23936430
title: data.xlsx

description: HRV data
DOI: 10.6084/m9.figshare.23936430.v1
title: data.xlsx

description: HRV data
DOI: 10.5281/zenodo.8227183
title: Literature sources from the GBIF dataset 'Earthworm occurrences from Russian-language literature'

description: These data provide the detailed bibliographic information about sources, included in the GBIF dataset (DOI 10.15468/cvwpyu). The aim of the work is to extract data on earthworm occurrences from scientific literature published in Russian. We processed 159 articles, monographs, collections of theses and PhD theses. Field Description language ISO 639-1 language code indicating the source language of the referent publication authorsRU The author or authors of the referenced work in Russian authorsEN The author or authors of the referenced work in English publicationTitle Title of book or article in Russian journalIdentifier Identifier to the journal, monograph or abstract book journalTitleRU Title of journal or monograph in Russian journalTitleEN Title of journal or monograph in Russian type Publication type year Year of publication issue issue, volume or number bibliographicCitation A text string referring to an un-parsed bibliographic citation. identifier DOI, ISBN, URI, etc refering to the reference.
description: This dataset is part of the project 'Quantifying the factors limiting the distribution of earthworms in European Russia: a model approach' founded by the Russian Science Foundation (23-24-00112, https://rscf.ru/en/project/23-24-00112/).
DOI: 10.34740/kaggle/dsv/6291367
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/6291377
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.8241433
title: Report Data

description: all report data of the article
DOI: 10.5281/zenodo.8241432
title: Report Data

description: all report data of the article
DOI: 10.5281/zenodo.8241418
title: Figures

description: all Figures of the article
DOI: 10.5281/zenodo.8241417
title: Figures

description: all Figures of the article
DOI: 10.5281/zenodo.8241443
title: Tables

description: all tables of the article
DOI: 10.5281/zenodo.8241442
title: Tables

description: all tables of the article
DOI: 10.6084/m9.figshare.23936427
title: RAD23-0341_dataset.xlsx

description: <b>Background: </b>Owing to the global increase in the incidence of nonalcoholic fatty liver disease, the development of noninvasive, widely available, and highly accurate methods for assessing hepatic steatosis is necessary.Purpose: To evaluate the performance of models with different combinations of quantitative US parameters for their ability to predict ≥5% steatosis in patients with chronic liver disease (CLD) as defined using MRI-proton density fat fraction (MRI-PDFF).<b> </b><b>Materials and Methods: </b>Patients with CLD were enrolled in this prospective, multicenter study between February 2020 and April 2021. Integrated backscatter coefficient (IBSC), signal-to-noise ratio (SNR), and ultrasound-guided attenuation parameter (UGAP) were measured in all participants. Participant MRI-PDFF value was used to define ≥5% steatosis. Four models based on different combinations of US parameters were created: Model 1 (UGAP alone), Model 2 (UGAP + IBSC), Model 3 (UGAP + SNR), and Model 4 (UGAP + IBSC + SNR). The diagnostic performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). The model was internally validated using 1000 bootstrap samples.<b> </b><b>Results: </b>A total of 582 participants were included in this study (median age, 64 years [IQR, 52–72]; 274 females), 364 in the steatosis group and 218 in the nonsteatosis group. The AUC values for steatosis diagnosis in Models 1–4 were 0.92, 0.93, 0.95, and 0.96, respectively. The C-indexes of Models adjusted by the bootstrap method were 0.92, 0.93, 0.95, and 0.96, respectively. Compared to other models, Models 3 and 4 demonstrated improved discrimination of ≥5% steatosis (p&lt;.01). <b>Conclusion: </b>A model built using the quantitative US parameters, UGAP, IBSC, and SNR, could accurately discriminate ≥5% steatosis in patients with CLD.<br>
DOI: 10.6084/m9.figshare.23936427.v1
title: RAD23-0341_dataset.xlsx

description: <b>Background: </b>Owing to the global increase in the incidence of nonalcoholic fatty liver disease, the development of noninvasive, widely available, and highly accurate methods for assessing hepatic steatosis is necessary.Purpose: To evaluate the performance of models with different combinations of quantitative US parameters for their ability to predict ≥5% steatosis in patients with chronic liver disease (CLD) as defined using MRI-proton density fat fraction (MRI-PDFF).<b> </b><b>Materials and Methods: </b>Patients with CLD were enrolled in this prospective, multicenter study between February 2020 and April 2021. Integrated backscatter coefficient (IBSC), signal-to-noise ratio (SNR), and ultrasound-guided attenuation parameter (UGAP) were measured in all participants. Participant MRI-PDFF value was used to define ≥5% steatosis. Four models based on different combinations of US parameters were created: Model 1 (UGAP alone), Model 2 (UGAP + IBSC), Model 3 (UGAP + SNR), and Model 4 (UGAP + IBSC + SNR). The diagnostic performance of all models was assessed using the area under the receiver operating characteristic curve (AUC). The model was internally validated using 1000 bootstrap samples.<b> </b><b>Results: </b>A total of 582 participants were included in this study (median age, 64 years [IQR, 52–72]; 274 females), 364 in the steatosis group and 218 in the nonsteatosis group. The AUC values for steatosis diagnosis in Models 1–4 were 0.92, 0.93, 0.95, and 0.96, respectively. The C-indexes of Models adjusted by the bootstrap method were 0.92, 0.93, 0.95, and 0.96, respectively. Compared to other models, Models 3 and 4 demonstrated improved discrimination of ≥5% steatosis (p&lt;.01). <b>Conclusion: </b>A model built using the quantitative US parameters, UGAP, IBSC, and SNR, could accurately discriminate ≥5% steatosis in patients with CLD.<br>
DOI: 10.5281/zenodo.8241468
title: ARRIVE ESSENTIAL

description: The arrive guidelines for animal reporting according to the ethical approval
DOI: 10.5281/zenodo.8241467
title: ARRIVE ESSENTIAL

description: The arrive guidelines for animal reporting according to the ethical approval
DOI: 10.5281/zenodo.8230992
title: Results of the poll in the study "Information Scientists' Motivations for Research Data Sharing and Reuse"

description: This is a dataset with results of the poll conducted in the study “Information Scientists’ Motivations for Research Data Sharing and Reuse”. In terms of the Uses and Gratifications Theory (Questions 1 and 2), the most popular uses relate to the categories of research support and information. Researchers share, or would share, their research data in general for any reusability purposes and especially for combination of different datasets to produce new evidence. Also, the vast majority of study participants associate research data sharing with possibilities to accelerate scientific progress and to increase research efficiency. In case of research data reuse, all the researchers indicated that they use, or would use, others’ data first of all for inspiration. Interestingly, study participants put relatively high the category of recognition in case of sharing, but at the same time they do not associate increased recognition among colleagues and other researchers with research data reuse. The remaining categories belonging to the categories of self-esteem and social interaction, i.e. increased citation level and visibility of the research as well as enhanced scientific reputation, possible cooperations and co-authorship, were selected only by few respondents. Also remarkably, data reuse is more frequently linked to entertainment then data sharing. In terms of the Self-Determination Theory (Questions 3 and 4), all but one of the interviewees indicated that they have shared or would share their research data because it can accelerate scientific progress which they consider important and would like to contribute to it (i.e., identified regulation). The second most popular motivation turned out to be the obligation by employer, project funder and/or journals (i.e., external regulation). The third most popular option was social influence, i.e. because many other researchers participate in data sharing and they feel obligated to do the same (i.e., external regulation).This way, the participants demonstrate a mixture of identified motivation and external regulation, both material and social. In the case of data reuse, the participants demonstrate more homogeneous results with identification and intrinsic motivation having most of the votes. The role of external regulation seems to be much less important as in the case with data sharing. So, researchers reuse, or would reuse, research data because it can accelerate scientific progress which is important for them. Additionally, researchers enjoy exploring and using third party research data. Thus, interviewees participate or would participate in data sharing because they consider it important, but also feel or are obliged to do so. At the same time, study participants do not feel pressure from outside when deciding whether to reuse data or not. For more information about the study and its results, please read the article “Information Scientists’ Motivations for Research Data Sharing and Reuse” by Shutsko and Stock (2023).
DOI: 10.34740/kaggle/dsv/6291591
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.8241489
title: Raw data of healthy young adults in the Weather Prediction Task

description: Raw data of 22 healthy young adults (11 females; average age: 26.29 years; range: 21.72–30.82) in the Weather Prediction Task with 100 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials. Raw data of 15 healthy young adults (9 females; average age: 26.58 years; range: 20.37–28.84) in the Weather Prediction Task with 200 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials. Bochud-Fragnière E, Banta Lavenex P and Lavenex P (2022) What Is the Weather Prediction Task Good for? A New Analysis of Learning Strategies Reveals How Young Adults Solve the Task. Front. Psychol. 13:886339. doi: 10.3389/fpsyg.2022.886339
DOI: 10.5281/zenodo.8241488
title: Raw data of healthy young adults in the Weather Prediction Task

description: Raw data of 22 healthy young adults (11 females; average age: 26.29 years; range: 21.72–30.82) in the Weather Prediction Task with 100 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials. Raw data of 15 healthy young adults (9 females; average age: 26.58 years; range: 20.37–28.84) in the Weather Prediction Task with 200 training trials with associative outcome probabilities of 0.20, 0.40, 0.60, 0.80 and 4 test trials. Bochud-Fragnière E, Banta Lavenex P and Lavenex P (2022) What Is the Weather Prediction Task Good for? A New Analysis of Learning Strategies Reveals How Young Adults Solve the Task. Front. Psychol. 13:886339. doi: 10.3389/fpsyg.2022.886339
DOI: 10.5281/zenodo.8241522
title: Population pharmacokinetic studies of critically ill adults receiving beta-lactam antimicrobials: covariate dataset

description: A dataset of reported covariates from a systematic review of population pharmacokinetic studies of critically ill adults receiving beta-lactam antimicrobials, including R script of statistical and graphical analyses
DOI: 10.5281/zenodo.8241521
title: Population pharmacokinetic studies of critically ill adults receiving beta-lactam antimicrobials: covariate dataset

description: A dataset of reported covariates from a systematic review of population pharmacokinetic studies of critically ill adults receiving beta-lactam antimicrobials, including R script of statistical and graphical analyses
DOI: 10.5281/zenodo.8241504
title: ifilot/microkinetic-datasets-methanation-fts: v.1.0.0

description: This repository contains three datasets for performing microkinetic simulations. CO2 methanation over Co(1121) lattice [1] CO2 methanation over a NiMn catalyst [2] Fischer-Tropsch synthesis over a dual-site Co(0001)xCo(1121) lattice [3] These datasets are based on the following publications 1. W. Chen; R. Pestman; B. Zijlstra; I.A.W. Filot; E.J.M. Hensen, Mechanism of cobalt-catalyzed co hydrogenation: 1. methanation, <br> ACS Catal., 2017, 7, 8061-8071.<br> 2. W.L. Vrijburg; E. Moioli; W. Chen; M. Zhang; B.J.P. Terlingen; B. Zijlstra; I.A.W. Filot; A.Zuttel; E.A. Pidko; E.J.M. Hensen, Efficient Base-Metal NiMn/TiO2 Catalyst for CO2 Methanation, ACS Catal., 2019, 9, 7823-7839.<br> 3. B. Zijlstra; R. J. P. Broos; W. Chen; G. L. Bezemer; I. A. W. Filot; E. J. M. Hensen, The vital role of step-edge sites for both co activation and chain growth on cobalt fischer-tropsch catalysts revealed through first-principles-based microkinetic modeling including lateral interactions, ACS Catal., 2020, 10, 9376-9400.<br>
description: {"references": ["W. Chen; R. Pestman; B. Zijlstra; I.A.W. Filot; E.J.M. Hensen, Mechanism of cobalt-catalyzed co hydrogenation: 1. methanation, ACS Catal., 2017, 7, 8061-8071", "W.L. Vrijburg; E. Moioli; W. Chen; M. Zhang; B.J.P. Terlingen; B. Zijlstra; I.A.W. Filot; A.Zuttel; E.A. Pidko; E.J.M. Hensen, Efficient Base-Metal NiMn/TiO2 Catalyst for CO2 Methanation, ACS Catal., 2019, 9, 7823-7839", "B. Zijlstra; R. J. P. Broos; W. Chen; G. L. Bezemer; I. A. W. Filot; E. J. M. Hensen, The vital role of step-edge sites for both co activation and chain growth on cobalt fischer-tropsch catalysts revealed through first-principles-based microkinetic modeling including lateral interactions, ACS Catal., 2020, 10, 9376-9400"]}
DOI: 10.21227/31nb-0j75
title: NJU Auditory Attention Decoding Dataset

description: This is an auditory attention decoding dataset including EEG recordings of 21 subjects when they were instructed to attend to one of the two competing speakers at two different locations. Unlike previous datasets (such as the KUL dataset), the locations of the two speakers are randomly drawn from fifteen alternatives.All subjects have given formal written consent approved by the Nanjing University ethical committee before the experiment and received financial compensation upon completion.The EEG data were recorded with the 32-channel EMOTIV Epoc Flex Saline system at a sampling rate of 128 Hz (downsampled from 1024 Hz) in a low-reverberant listening room.For each subject, the experiment includes 32 trials. In each trial, the subject was exposed to a pair of randomly selected stimuli, the directions of whom were randomly drawn from the 15 possible competing speaker directions, i.e., ±135°, ±120°, ±90°, ±60°, ±45°, ±30°, ±15° and 0°.This dataset provides the preprocessed EEG recordings and the aligned audio stimuli signals, as long as the attention information. All data are provided as zipped files.Please contact the author at yuanming.zhang@smail.nju.edu.cn for additional information.Note that there are 28 subjects participated in our experiments and the data of seven subjects were removed from further analysis due to device failure.
DOI: 10.5281/zenodo.8241527
title: ifilot/microkinetic-datasets-methanation-fts: Fix error in description

description: This repository contains three datasets for performing microkinetic simulations. CO2 methanation over Co(1121) lattice [1] CO2 methanation over a NiMn catalyst [2] Fischer-Tropsch synthesis over a dual-site Co(0001)xCo(1121) lattice [3] These datasets are based on the following publications 1. W. Chen; R. Pestman; B. Zijlstra; I.A.W. Filot; E.J.M. Hensen, Mechanism of cobalt-catalyzed co hydrogenation: 1. methanation, <br> ACS Catal., 2017, 7, 8061-8071.<br> 2. W.L. Vrijburg; E. Moioli; W. Chen; M. Zhang; B.J.P. Terlingen; B. Zijlstra; I.A.W. Filot; A.Zuttel; E.A. Pidko; E.J.M. Hensen, Efficient Base-Metal NiMn/TiO2 Catalyst for CO2 Methanation, ACS Catal., 2019, 9, 7823-7839.<br> 3. B. Zijlstra; R. J. P. Broos; W. Chen; G. L. Bezemer; I. A. W. Filot; E. J. M. Hensen, The vital role of step-edge sites for both co activation and chain growth on cobalt fischer-tropsch catalysts revealed through first-principles-based microkinetic modeling including lateral interactions, ACS Catal., 2020, 10, 9376-9400. For more information on the formatting of these files, please consult the<br> MKMCXX wiki.
description: {"references": ["W. Chen; R. Pestman; B. Zijlstra; I.A.W. Filot; E.J.M. Hensen,\u00a0Mechanism of cobalt-catalyzed co hydrogenation: 1. methanation,\u00a0 ACS Catal., 2017, 7, 8061-8071", "W.L. Vrijburg; E. Moioli; W. Chen; M. Zhang; B.J.P. Terlingen; B. Zijlstra; I.A.W. Filot; A.Zuttel; E.A. Pidko; E.J.M. Hensen, Efficient Base-Metal\u00a0NiMn/TiO2 Catalyst for CO2 Methanation, ACS Catal., 2019, 9, 7823-7839", "B. Zijlstra; R. J. P. Broos; W. Chen; G. L. Bezemer; I. A. W. Filot; E. J. M.\u00a0Hensen, The vital role of step-edge sites for both co activation and chain\u00a0growth on cobalt fischer-tropsch catalysts revealed through\u00a0first-principles-based microkinetic modeling including lateral interactions,\u00a0ACS Catal., 2020, 10, 9376-9400"]}