cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6006906
title: Recommender Systems for Science: A basic Taxonomy

description: This dataset is accompanying the "<strong>Recommender system for science: A basic taxonomy</strong>" paper published at IRCDL 2022 conference. This study had a Systematic Mapping Approach on the Recommender system for science. In particular, the study aims at responding to four questions on recommender systems in science cases: users and their interests representation, item typologies and their representation, recommendation algorithms, and evaluation, and then providing a taxonomy. This dataset contains <strong>209 papers </strong>of interest that have been published between 2015 and 2022. The dataset has <strong>11</strong> columns which organised as follows: Column <strong>Title: </strong>This column contains the title of the papers. Column <strong>DOI: </strong>This column contains the DOI of the papers. Column <strong>Publication_year</strong>: This column contains the year that the paper is published. Column <strong>DB: </strong>This column contains the repository that the paper is retrieved. Column <strong>Keywords</strong>: This column contains the keywords provided for the paper. Column <strong>Content_type: </strong>This column contains the paper type which can be: <strong>Article,</strong> <strong>Conference</strong> or <strong>Review.</strong> Column <strong>Citing_paper_count: </strong>This column contains the citation number of the paper. Column <strong>Recommended_artefact: </strong>This column contains the scientific product that is recommended to users which can be <strong>paper</strong>, <strong>workflow</strong>, <strong>collaborator</strong>, <strong>dataset</strong> or <strong>others</strong>. Column <strong>User_type: </strong>This column contains the type of user who receives the recommendation, which can be an<strong> Individual </strong>user or a<strong> Group</strong> of users. Column <strong>Algorithm</strong><strong>: </strong>This column contains the recommendation algorithm that the paper proposed, which can be: <strong>HB </strong>(Hybrid-based), <strong>CB</strong> (Content-based), <strong>CFB</strong> (Collaborative-filtering-based), or <strong>GB</strong> (Graph-based). Column <strong>Evaluation_method</strong><strong>: </strong>This column contains the method of the algorithm evaluation which can be <strong>OFFLINE</strong>, <strong>ONLINE, BOTH, </strong>or<strong> NO_EVALUATION.</strong>
DOI: 10.5281/zenodo.6006905
title: Recommender Systems for Science: A basic Taxonomy

description: This dataset is accompanying the "<strong>Recommender system for science: A basic taxonomy</strong>" paper published at IRCDL 2022 conference. This study had a Systematic Mapping Approach on the Recommender system for science. In particular, the study aims at responding to four questions on recommender systems in science cases: users and their interests representation, item typologies and their representation, recommendation algorithms, and evaluation, and then providing a taxonomy. This dataset contains <strong>209 papers </strong>of interest that have been published between 2015 and 2022. The dataset has <strong>11</strong> columns which organised as follows: Column <strong>Title: </strong>This column contains the title of the papers. Column <strong>DOI: </strong>This column contains the DOI of the papers. Column <strong>Publication_year</strong>: This column contains the year that the paper is published. Column <strong>DB: </strong>This column contains the repository that the paper is retrieved. Column <strong>Keywords</strong>: This column contains the keywords provided for the paper. Column <strong>Content_type: </strong>This column contains the paper type which can be: <strong>Article,</strong> <strong>Conference</strong> or <strong>Review.</strong> Column <strong>Citing_paper_count: </strong>This column contains the citation number of the paper. Column <strong>Recommended_artefact: </strong>This column contains the scientific product that is recommended to users which can be <strong>paper</strong>, <strong>workflow</strong>, <strong>collaborator</strong>, <strong>dataset</strong> or <strong>others</strong>. Column <strong>User_type: </strong>This column contains the type of user who receives the recommendation, which can be an<strong> Individual </strong>user or a<strong> Group</strong> of users. Column <strong>Algorithm</strong><strong>: </strong>This column contains the recommendation algorithm that the paper proposed, which can be: <strong>HB </strong>(Hybrid-based), <strong>CB</strong> (Content-based), <strong>CFB</strong> (Collaborative-filtering-based), or <strong>GB</strong> (Graph-based). Column <strong>Evaluation_method</strong><strong>: </strong>This column contains the method of the algorithm evaluation which can be <strong>OFFLINE</strong>, <strong>ONLINE, BOTH, </strong>or<strong> NO_EVALUATION.</strong>
DOI: 10.5281/zenodo.6036783
title: Olympus VSI "multifile" test dataset

description: <strong>Olympus VSI "multifile" test dataset</strong> A publicly available dataset in the proprietary Olympus VSI format intended for testing file readers and other related mechanisms. Key properties / requirements: overall size is as small as possible (~200 kiB in this case) dataset consists of a top-level <code>.vsi</code> file plus a subfolder structure containing one or more <code>.ets</code> files <strong>IMPORTANT</strong>: this dataset was heavily postprocessed (see the section below), it is purely meant to have a valid example of the file format structure. <strong>Dataset Information</strong> <strong>Image Dimensions</strong> channels: 2 (405, 488) size X: 1645 size Y: 1682 size Z: 11 <strong>Software Version</strong> The software used to acquire and postprocess the dataset. cellSens Dimension v3.1 (Build 21199) <strong>Postprocessing</strong> The following steps were applied to reduce the size of the dataset: cropping background subtraction rank filter NxN filter JPEG 2000 compression <strong>MD5 Sums</strong> Available in the file <em>md5sums</em>, generated by: <pre><code class="language-bash">cd data/ find -type f -printf '%P\n' | xargs md5sum &gt; ../md5sums</code></pre>
DOI: 10.6084/m9.figshare.19154018.v1
title: Chevalier_COVID-19_Border_Crossing_Importations

description: Dataset for Figure 1, showing COVID-19 importations per day (per 100,000 population) and the percent change in community transmission over one-month, by Rt.
DOI: 10.6084/m9.figshare.19154018
title: Chevalier_COVID-19_Border_Crossing_Importations

description: Dataset for Figure 1, showing COVID-19 importations per day (per 100,000 population) and the percent change in community transmission over one-month, by Rt.
DOI: 10.5517/ccdc.csd.cc21sp8t
title: CCDC 1900556: Experimental Crystal Structure Determination

description: Related Article: Malee Sinchow, Thammanoon Chuasaard, Bunlawee Yotnoi, Apinpus Rujiwatra|2019|J.Solid State Chem.|278|120902|doi:10.1016/j.jssc.2019.120902
DOI: 10.5517/ccdc.csd.cc21sp9v
title: CCDC 1900557: Experimental Crystal Structure Determination

description: Related Article: Malee Sinchow, Thammanoon Chuasaard, Bunlawee Yotnoi, Apinpus Rujiwatra|2019|J.Solid State Chem.|278|120902|doi:10.1016/j.jssc.2019.120902
DOI: 10.5517/ccdc.csd.cc21spbw
title: CCDC 1900558: Experimental Crystal Structure Determination

description: Related Article: Malee Sinchow, Thammanoon Chuasaard, Bunlawee Yotnoi, Apinpus Rujiwatra|2019|J.Solid State Chem.|278|120902|doi:10.1016/j.jssc.2019.120902
DOI: 10.5517/ccdc.csd.cc21zvhd
title: CCDC 1906484: Experimental Crystal Structure Determination

description: Related Article: Malee Sinchow, Thammanoon Chuasaard, Bunlawee Yotnoi, Apinpus Rujiwatra|2019|J.Solid State Chem.|278|120902|doi:10.1016/j.jssc.2019.120902
DOI: 10.5517/ccdc.csd.cc1jfzgd
title: CCDC 1413614: Experimental Crystal Structure Determination

description: Related Article: Lun Zhao, Lingshu Meng, Xin Liu, Guanlin Guo, Hongwei Jing|2020|J.Solid State Chem.|283|121141|doi:10.1016/j.jssc.2019.121141
DOI: 10.25505/fiz.icsd.cc22297q
title: CSD 1908832: Experimental Crystal Structure Determination

description: Related Article: Evgeny B. Merkulov, Elena I. Voit, Andrey V. Gerasimenko|2019|J.Fluorine Chem.|226|109357|doi:10.1016/j.jfluchem.2019.109357
DOI: 10.25505/fiz.icsd.cc22298r
title: CSD 1908833: Experimental Crystal Structure Determination

description: Related Article: Evgeny B. Merkulov, Elena I. Voit, Andrey V. Gerasimenko|2019|J.Fluorine Chem.|226|109357|doi:10.1016/j.jfluchem.2019.109357
DOI: 10.25505/fiz.icsd.cc2229bt
title: CSD 1908835: Experimental Crystal Structure Determination

description: Related Article: Evgeny B. Merkulov, Elena I. Voit, Andrey V. Gerasimenko|2019|J.Fluorine Chem.|226|109357|doi:10.1016/j.jfluchem.2019.109357
DOI: 10.5517/ccdc.csd.cc222s43
title: CSD 1909294: Experimental Crystal Structure Determination

description: Related Article: A.P. Shablinskii, I.E. Kolesnikov, R.S. Bubnova, A.V. Povolotskiy, E. Lähderanta, S.K. Filatov|2019|J.Lumin.|216|116714|doi:10.1016/j.jlumin.2019.116714
DOI: 10.5517/ccdc.csd.cc222s54
title: CSD 1909295: Experimental Crystal Structure Determination

description: Related Article: A.P. Shablinskii, I.E. Kolesnikov, R.S. Bubnova, A.V. Povolotskiy, E. Lähderanta, S.K. Filatov|2019|J.Lumin.|216|116714|doi:10.1016/j.jlumin.2019.116714
DOI: 10.5517/ccdc.csd.cc222s65
title: CSD 1909296: Experimental Crystal Structure Determination

description: Related Article: A.P. Shablinskii, I.E. Kolesnikov, R.S. Bubnova, A.V. Povolotskiy, E. Lähderanta, S.K. Filatov|2019|J.Lumin.|216|116714|doi:10.1016/j.jlumin.2019.116714
DOI: 10.25505/fiz.icsd.cc228jpl
title: CSD 1914829: Experimental Crystal Structure Determination

description: Related Article: Ya-Li Xue, Dan Zhao, Li-Na Zhang, Xiao-Qi Cui, Xiao-Yang Han, Qing-Xia Yao|2019|J.Lumin.|216|116720|doi:10.1016/j.jlumin.2019.116720
DOI: 10.5517/ccdc.csd.cc229bzq
title: CCDC 1915613: Experimental Crystal Structure Determination

description: Related Article: Li-Xin You, Yang Guo, Shi-Yu Xie, Shu-Ju Wang, Gang Xiong, Ileana Dragutan, Valerian Dragutan, Fu Ding, Ya-Guang Sun|2019|J.Solid State Chem.|278|120900|doi:10.1016/j.jssc.2019.120900
DOI: 10.5517/ccdc.csd.cc1mpfqg
title: CCDC 1510156: Experimental Crystal Structure Determination

description: Related Article: Aliyu M. Hamisu, Fendi Y. Wardana, Azhar Ariffin, Irshad Baig, Christos D. Malliakas, Arief C. Wibowo|2019|J.Solid State Chem.|279|120948|doi:10.1016/j.jssc.2019.120948
DOI: 10.5517/ccdc.csd.cc22blst
title: CCDC 1916816: Experimental Crystal Structure Determination

description: Related Article: Fang-Hua Zhao, Wen-Yu Guo, Shi-Yao Li, Zhong-Lin Li, Xiao-Qing Yan, Xin-Meng Jia, Long-Wei Huang, Jin-Mao You|2019|J.Solid State Chem.|278|120926|doi:10.1016/j.jssc.2019.120926
DOI: 10.5517/ccdc.csd.cc22bltv
title: CCDC 1916817: Experimental Crystal Structure Determination

description: Related Article: Fang-Hua Zhao, Wen-Yu Guo, Shi-Yao Li, Zhong-Lin Li, Xiao-Qing Yan, Xin-Meng Jia, Long-Wei Huang, Jin-Mao You|2019|J.Solid State Chem.|278|120926|doi:10.1016/j.jssc.2019.120926
DOI: 10.5517/ccdc.csd.cc22fn4b
title: CCDC 1919741: Experimental Crystal Structure Determination

description: Related Article: Malee Sinchow, Thammanoon Chuasaard, Bunlawee Yotnoi, Apinpus Rujiwatra|2019|J.Solid State Chem.|278|120902|doi:10.1016/j.jssc.2019.120902
DOI: 10.5517/ccdc.csd.cc22htzd
title: CCDC 1921844: Experimental Crystal Structure Determination

description: Related Article: Lian-sheng Cui, Zi-qi Cui, Yan-lin Chen, Shao-ming Ling, Jin-qiao Long, Peng-fei Yao|2019|J.Solid State Chem.|279|120932|doi:10.1016/j.jssc.2019.120932
DOI: 10.5517/ccdc.csd.cc22hv0g
title: CCDC 1921845: Experimental Crystal Structure Determination

description: Related Article: Lian-sheng Cui, Zi-qi Cui, Yan-lin Chen, Shao-ming Ling, Jin-qiao Long, Peng-fei Yao|2019|J.Solid State Chem.|279|120932|doi:10.1016/j.jssc.2019.120932
DOI: 10.5517/ccdc.csd.cc22jvzg
title: CCDC 1922836: Experimental Crystal Structure Determination

description: Related Article: Bingcai Luo, Donghui Yu, Jianqiang Huo|2020|J.Solid State Chem.|283|121137|doi:10.1016/j.jssc.2019.121137
DOI: 10.25505/fiz.icsd.cc22yg9t
title: CSD 1934936: Experimental Crystal Structure Determination

description: Related Article: Yuqiao Zhou, Abishek K. Iyer, Anton O. Oliynyk, Manon Heyberger, Yixuan Lin, Yu Qiu, Arthur Mar|2019|J.Solid State Chem.|278|120914|doi:10.1016/j.jssc.2019.120914
DOI: 10.25505/fiz.icsd.cc22ygbv
title: CSD 1934937: Experimental Crystal Structure Determination

description: Related Article: Yuqiao Zhou, Abishek K. Iyer, Anton O. Oliynyk, Manon Heyberger, Yixuan Lin, Yu Qiu, Arthur Mar|2019|J.Solid State Chem.|278|120914|doi:10.1016/j.jssc.2019.120914
DOI: 10.5517/ccdc.csd.cc22yv52
title: CCDC 1935304: Experimental Crystal Structure Determination

description: Related Article: Yun Sun, Bao-Xia Dong, Wen-Long Liu|2019|J.Solid State Chem.|278|120897|doi:10.1016/j.jssc.2019.120897
DOI: 10.5517/ccdc.csd.cc22ywmk
title: CCDC 1935349: Experimental Crystal Structure Determination

description: Related Article: Shaobin Li, Borong Lu, Jianjiao Xin, Li Zhang, Jing Pan, Yue Chen, Xiaoguo Tan|2019|J.Solid State Chem.|278|120905|doi:10.1016/j.jssc.2019.120905
DOI: 10.5517/ccdc.csd.cc22yx76
title: CCDC 1935368: Experimental Crystal Structure Determination

description: Related Article: Dong-Sheng Liu, Zhi-Jun Qiu, Xiao Fu, Yi-Zhuo Liu, Pan Ding, Yun-Xia Zhu, Yan Sui|2019|J.Solid State Chem.|278|120879|doi:10.1016/j.jssc.2019.07.040
DOI: 10.5517/ccdc.csd.cc22yx87
title: CCDC 1935369: Experimental Crystal Structure Determination

description: Related Article: Dong-Sheng Liu, Zhi-Jun Qiu, Xiao Fu, Yi-Zhuo Liu, Pan Ding, Yun-Xia Zhu, Yan Sui|2019|J.Solid State Chem.|278|120879|doi:10.1016/j.jssc.2019.07.040
DOI: 10.5517/ccdc.csd.cc22z6jt
title: CCDC 1935656: Experimental Crystal Structure Determination

description: Related Article: Ziqian Zhu, Chengxia Tong, Pengcheng Wang, Wei Wang, Jing Sun, Jingjing Xue, Li Wang, Yong Fan, Jianing Xu|2019|J.Solid State Chem.|283|121151|doi:10.1016/j.jssc.2019.121151
DOI: 10.5517/ccdc.csd.cc22z5lv
title: CCDC 1935627: Experimental Crystal Structure Determination

description: Related Article: Dong-Sheng Liu, Zhi-Jun Qiu, Xiao Fu, Yi-Zhuo Liu, Pan Ding, Yun-Xia Zhu, Yan Sui|2019|J.Solid State Chem.|278|120879|doi:10.1016/j.jssc.2019.07.040
DOI: 10.6084/m9.figshare.19154147
title: Data and Code to reproduce Evans et al. 2022 'Socio-ecological dynamics in urban systems: an integrative approach to mosquito-borne disease in Bengaluru, India'

description: Code should be run with working directory set to project directory. Script 001 has already been run to clean the data and analysis can be started with script 002. Saved models from script 002 are in the `results` directory.<br><br><br>Contact:<br>Michelle Evans (mv.evans.phd@gmail.com)
DOI: 10.6084/m9.figshare.19154147.v1
title: Data and Code to reproduce Evans et al. 2022 'Socio-ecological dynamics in urban systems: an integrative approach to mosquito-borne disease in Bengaluru, India'

description: Code should be run with working directory set to project directory. Script 001 has already been run to clean the data and analysis can be started with script 002. Saved models from script 002 are in the `results` directory.<br><br><br>Contact:<br>Michelle Evans (mv.evans.phd@gmail.com)
DOI: 10.15151/esrf-es-686778488
title: IBS BAG

description: The IBS BAG currently includes 24 IBS teams and 4 external groups from CERMAV and iRTSV/CEA (Grenoble), CEA-Cadarache (St Paul lez Durance) and IRBA (Brétigny-sur-Orge). We study structure-function relationships of proteins emphasizing in human health, biotechnology and methodology. Most of the projects can be grouped in different topics as follows: host-pathogen interaction, immunology, new drug targets, fundamental cellular mechanisms, membrane proteins, intrinsically disordered proteins, biotechnological applications and methodology. Some of these projects use integrative approaches (X-ray crystallography, SAXS, NMR (and cryo-EM), together with other biophysical studies) with the aim to fully understand the complexity of the biological macromolecular systems on study. Some other teams are dedicated to serial crystallography and its developments, for which the new ESRF-EBS might be the perfect tool.
DOI: 10.7294/18306044
title: Dataset for a kirigami composite metamaterial sheet

description: This dataset contains the mechanical properties, stiffness, and shape-fixing data for a kirigami composite metamaterial sheet.
DOI: 10.25678/00053f
title: Data for: Successful mainstream nitritation through NOB inactivation

description: The development of new wastewater treatment processes can assist in reducing the impact of wastewater treatment on the environment. The recently developed partial nitritation anammox (PNA) process, for example, consumes less energy for aeration and reduces nitrate in the effluent without requiring additional organic carbon. However, achieving stable nitritation (ammonium oxidation; NH4+ -&gt; NO2-) at mainstream conditions (T = 10–25°C, C:N &gt; 10, influent ammonium &lt; 50 mgNH4-N/L and effluent &lt; 1 mgNH4-N/L) remains challenging. This study explores the potential and mechanism of nitrite-oxidizing bacteria (NOB) suppression in a bottom-fed sequencing batch reactor (SBR). Two bench-scale (11 L) reactors and a pilot-scale reactor (8 m3) were operated for over a year and were fed with organic substrate depleted municipal wastewater. Initially, nitratation (nitrite oxidation; NO2- -&gt; NO3-) occurred occasionally until an anaerobic phase was integrated into the operating cycle. The introduction of the anaerobic phase effectively suppressed the regrowth of NOB and nitritation was stable over 300 days, down to 8 °C and at ammonium influent concentrations &lt; 25 mgNH4-N/L. Batch experiments and process data revealed that parameters typically affecting NOB growth (e.g., dissolved oxygen, alkalinity, trace elements, lag-phase after anoxia, free nitrous acid (FNA), free ammonia (FA), pH, sulfide, or solids retention time (SRT)) could not fully explain the suppression of nitratation. Experiments in which fresh nitrifying microbial biomass was added to the nitritation system indicated that NOB inactivation explained NOB suppression better than NOB washout at high SRT. This study concludes that bottom-fed SBRs with anaerobic phases allow for stable nitritation over a broad range of operational parameters. Coupling this type of SBR to an anammox reactor can enable efficient mainstream anammox-based wastewater treatment.
DOI: 10.5281/zenodo.6034023
title: eQTL Catalogue mapping from unique variant ids to rsIDs

description: eQTL Catalogue mapping from unique variant ids to rsIDs
DOI: 10.5281/zenodo.6034022
title: eQTL Catalogue mapping from unique variant ids to rsIDs

description: eQTL Catalogue mapping from unique variant ids to rsIDs
DOI: 10.6084/m9.figshare.13567265.v1
title: Original data of manuscript entitled "Pixel-wise kinetic clustering of MSOT images to objectively assess pharmacokinetics and biodistribution of clinical biomarkers" by Hoffmann and Gerst et al.

description: This dataset contains all original data used in the manuscript entitled "Pixel-wise kinetic clustering of MSOT images to objectively assess pharmacokinetics and biodistribution of clinical biomarkers" by Hoffmann and Gerst et al.<br><br>Contained data:<br><br>1. Anatomical_RoiSets.zip<br>This folder contains anatomical ROI sets used in the manuscript.<br><br>2. Signal-oriented_analysis_Data.zip<br>This folder contains original MSOT data used for the signal-oriented analysis.<br>The data consists of two different treatments: Sham (control animals) and PCI (diseased animals). Each animal folder is identified by a unique name and contains an MSOT image in .tif file format and a region of interest (ROI) in .roi file format. The MSOT image is a time-resolved 4d image with 4 channels (water, indocyanine green, deoxygenated blood and oxygenated blood). The ROI comprises the whole animal and is referred to as animal-ROI in the manuscript.<br><br>3. Tissue-oriented_analysis_Data.zip<br>This folder contains the data used for the tissue-oriented analysis.<br>The data consists of two different treatments: Sham (control animals) and PCI (diseased animals). For each animal there exist two files prefixed with the animal identifier: a pre-processed MSOT image in .tif file format and a region of interest (ROI) in .roi file format. The MSOT image is a pre-processed, time-resolved 3d image of the indocyanine green channel (see manuscript for details on image pre-processing). The ROI comprises the liver tissue and is referred to as liver-ROI in the manuscript.<br><br>
DOI: 10.5281/zenodo.6037429
title: Ash (Fraxinus excelsior L.) in vitro survival data for the UKs Living Ash Project

description: <em>In-vitro</em> propagation and survival data sets (including nursery survival) of the ash plants generated i.e. <em>Fraxinus excelsior</em> L., plus the PCR primers used and conditions applied. Surveyed from a range of ash seed material taken from across the UK, and held at the UK ash collection hosted by the Earth Trust in Oxfordshire, UK. A more detailed analysis of this data is currently expected to be be published in the <em>Annals of Forest Science</em>, and which has already provisionally accepted this work for publication, subject to the underlying data being made available i.e. here The data deposited here represents the underlying data that will be presented in graphical form in the forthcoming paper by Fenning et al., plus the associated metadata and statistical analyses, along with the original .jpg of the photos used.
description: This work was funded by the UK Department for Environment, Food and Rural Affairs and the Forestry Commission, as WP6 of the Living Ash Project grant TH0133, coordinated by Jo Clark, previously of the Earth Trust (Oxfordshire, UK) and now of the Future Trees Trust (UK). This supplementary data underlies the data and graphs analysed in the publication to which this data is intended to relate c/o The Annals of Forest Science, which has already provisionally accepted this work.
DOI: 10.5281/zenodo.6037428
title: Ash (Fraxinus excelsior L.) in vitro survival data for the UKs Living Ash Project

description: <em>In-vitro</em> propagation and survival data sets (including nursery survival) of the ash plants generated i.e. <em>Fraxinus excelsior</em> L., plus the PCR primers used and conditions applied. Surveyed from a range of ash seed material taken from across the UK, and held at the UK ash collection hosted by the Earth Trust in Oxfordshire, UK. A more detailed analysis of this data is currently expected to be be published in the <em>Annals of Forest Science</em>, and which has already provisionally accepted this work for publication, subject to the underlying data being made available i.e. here The data deposited here represents the underlying data that will be presented in graphical form in the forthcoming paper by Fenning et al., plus the associated metadata and statistical analyses, along with the original .jpg of the photos used.
description: This work was funded by the UK Department for Environment, Food and Rural Affairs and the Forestry Commission, as WP6 of the Living Ash Project grant TH0133, coordinated by Jo Clark, previously of the Earth Trust (Oxfordshire, UK) and now of the Future Trees Trust (UK). This supplementary data underlies the data and graphs analysed in the publication to which this data is intended to relate c/o The Annals of Forest Science, which has already provisionally accepted this work.
DOI: 10.6084/m9.figshare.19153127.v1
title: Dataset for HCW and Non-HCW.xlsx

description: This is the dataset for the injected health information in Poland’s healthcare system. The research is used to compare trust between non-healthcare workers (i.e., lay people) and healthcare workers.