cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4680466
title: (1S)-camphor on Cu(111), 6D surface adsorbate search with BOSS

description: This dataset was employed in manuscripts:<br> [1] J. Järvi, B. Alldritt, O. Krejčí, M. Todorović, P. Liljeroth, P. Rinke, Integrating Bayesian inference with scanning probe experiments for robust identification of surface adsorbate configurations, <em>Adv. Funct. Mater.</em> 31, 2010853 (2021). DOI: 10.1002/adfm.202010853<br> [2] J. Järvi, P. Rinke, M. Todorović, Detecting stable adsorbates of (1<em>S</em>)-camphor on Cu(111) with Bayesian optimization, <em>Beilstein J. Nanotechnol.</em> 11, 1577 (2020). DOI: 10.3762/bjnano.11.140 This dataset features DFT sampling of adsorption configurations for (1<em>S</em>)-camphor on Cu(111) using the Bayesian Optimization Structure Search (BOSS) method. The 6D configurational search was conducted over 3 molecular translations (<em>x, y, z</em>) and 3 rotations (<em>alpha, beta, gamma</em>). The search range of on-surface translation (<em>x, y</em>) is [-0.5, 0.5] in fractional coordinates of the orthogonal Cu(111) unit cell. The search range of the height of the molecule (<em>z</em>) is [4, 7] Å. All rotations are done in the range [-180, 180] deg. Energy (<em>E</em><sub>ads</sub>) is the adsorption energy of (1<em>S</em>)-camphor on Cu(111) computed with DFT. For additional details on search coordinates and DFT calculations please refer to [2]. The 6D model of the adsorption energy surface was constructed with 1380 energy points, the full list is provided in the dataset. Rotational and translational symmetries of the system allowed us to build the model using 609 unique DFT calculations, which are denoted by the ID number in each row (note: some IDs appear more than once due to material symmetry). The raw data for each unique calculation ID (001-609) can be found in the NOMAD repository DOI: 10.17172/NOMAD/2021.04.12-1.
DOI: 10.5281/zenodo.6572985
title: Supplementary material for "Inception and evolution of La Corona lava tube system (Lanzarote, Canary Islands, Spain)"

description: Supplementary material for "Inception and evolution of La Corona lava tube system (Lanzarote, Canary Islands, Spain)" Geochemical data and coordinates of the samples; LIDAR, northern region of Lanzarote (resolution 5mt)
DOI: 10.5519/qd.ee33k0nu
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3231 records)
DOI: 10.5281/zenodo.6536051
title: Supplementary material for "Inception and evolution of La Corona lava tube system (Lanzarote, Canary Islands, Spain)"

description: Supplementary material for "Inception and evolution of La Corona lava tube system (Lanzarote, Canary Islands, Spain)" Geochemical data and coordinates of the samples; LIDAR, northern region of Lanzarote (resolution 5mt)
DOI: 10.6084/m9.figshare.19550710
title: Supplementary Material for: Understanding children’s attention to dental caries through eye-tracking

description: Visual attention is a significant gateway to a child's mind, and looking is one of the first behaviours young children develop. Untreated caries and the resulting poor dental aesthetics can have adverse emotional and social impacts on children's oral health-related quality of life due to its detrimental effects on self-esteem and self-concept. Therefore, we explored preschool children's eye movement patterns and visual attention to images with and without dental caries via eye movement analysis using hidden Markov models (EMHMM). We calibrated a convenience sample of 157 preschool children to the eye-tracker (Tobii Nano Pro) to ensure standardisation. Consequently, each participant viewed the same standardised pictures with and without dental caries while an eye-tracking device tracked their eye movements. Subsequently, based on the sequence of viewed regions of interest (ROIs), a transition matrix was developed where the participants previously viewed ROI informed their subsequently considered ROI. Hence, an individual's HMM was estimated from their eye movement data using a variational Bayesian approach to determine the optimal number of ROIs automatically. Consequently, this data-driven approach generated the visual task participants' most representative eye movement patterns. Preschool children exhibited two different eye movement patterns, distributed (78%) and selective (21%), which was statistically significant. Children switched between images with more similar probabilities in the distributed pattern while children remained looking at the same ROI than switching to the other ROI in the selective pattern. Nevertheless, all children exhibited an equal starting fixation on the right or left image and noticed teeth. The study findings reveal that most preschool children did not have an attentional bias to images with and without dental caries. Furthermore, only a few children selectively fixated on images with dental caries. Therefore, selective eye-movement patterns may strongly predict preschool children's sustained visual attention to dental caries. Nevertheless, future studies are essential to fully understand the developmental origins of differences in visual attention to common oral health presentations in children. Finally, EMHMM is appropriate for assessing inter-individual differences in children's visual attention.
DOI: 10.5281/zenodo.6421409
title: F4TCNQ and TTF on approximate supported graphene, 3D surface adsorbate search with BOSS

description: These datasets were employed in the manuscript: [1] J. Järvi, M. Todorović, and P. Rinke, Efficient modeling of organic adsorbates on oxygen-intercalated graphene on Ir(111), <em>Phys. Rev. B</em> 105, 195304 (2022). DOI: 10.1103/PhysRevB.105.195304 The two datasets feature DFT sampling of adsorption configurations for F<sub>4</sub>TCNQ and TTF on approximate supported graphene (Gr) using the Bayesian Optimization Structure Search (BOSS) method. The 3D configurational searches were conducted for each molecule over two molecular translations (<em>x, y</em>) and an in-plane rotation (<em>gamma</em>). The search range of on-surface translation (<em>x, y</em>) is [0, 1] in fractional coordinates of the Gr unit cell. The in-plane rotation (<em>gamma</em>) is done in the range [0, 180] deg. Energy (<em>E</em><sub>ads</sub>) is the adsorption energy of F<sub>4</sub>TCNQ or TTF on the approximate supported Gr, computed with DFT. For additional details on search coordinates and DFT calculations please refer to [1].<br> <br> The 3D models of the adsorption energy surfaces were constructed with 500 energy points for each molecule, the full lists are provided in the datasets. Each calculation is denoted with an ID number (001-500). The raw data for each calculation can be found in the NOMAD repository with the corresponding ID, DOI: 10.17172/NOMAD/2022.04.07-1
DOI: 10.5281/zenodo.6421408
title: F4TCNQ and TTF on approximate supported graphene, 3D surface adsorbate search with BOSS

description: These datasets were employed in the manuscript: [1] J. Järvi, M. Todorović, and P. Rinke, Efficient modeling of organic adsorbates on oxygen-intercalated graphene on Ir(111), <em>Phys. Rev. B</em> 105, 195304 (2022). DOI: 10.1103/PhysRevB.105.195304 The two datasets feature DFT sampling of adsorption configurations for F<sub>4</sub>TCNQ and TTF on approximate supported graphene (Gr) using the Bayesian Optimization Structure Search (BOSS) method. The 3D configurational searches were conducted for each molecule over two molecular translations (<em>x, y</em>) and an in-plane rotation (<em>gamma</em>). The search range of on-surface translation (<em>x, y</em>) is [0, 1] in fractional coordinates of the Gr unit cell. The in-plane rotation (<em>gamma</em>) is done in the range [0, 180] deg. Energy (<em>E</em><sub>ads</sub>) is the adsorption energy of F<sub>4</sub>TCNQ or TTF on the approximate supported Gr, computed with DFT. For additional details on search coordinates and DFT calculations please refer to [1].<br> <br> The 3D models of the adsorption energy surfaces were constructed with 500 energy points for each molecule, the full lists are provided in the datasets. Each calculation is denoted with an ID number (001-500). The raw data for each calculation can be found in the NOMAD repository with the corresponding ID, DOI: 10.17172/NOMAD/2022.04.07-1
DOI: 10.5281/zenodo.6460190
title: Dataset Dental research data availability and quality according to FAIR principles

description: This dataset contains open access publications in EPMC dental journals from 2016 to 2021 and 500 non-open access dental publications. We evaluated the level of compliance with the FAIR principles. The original dataset and codebook are attached.
description: This research was funded by the MikroTik-RSU "Towards implementing the RSU data repository and the FAIR Data Management Principles" given to SEU and the European Union's Horizon 2020 research and innovation programme under grant agreement No 857287
DOI: 10.5281/zenodo.6460189
title: Dataset Dental research data availability and quality according to FAIR principles

description: This dataset contains open access publications in EPMC dental journals from 2016 to 2021 and 500 non-open access dental publications. We evaluated the level of compliance with the FAIR principles. The original dataset and codebook are attached.
description: This research was funded by the MikroTik-RSU "Towards implementing the RSU data repository and the FAIR Data Management Principles" given to SEU and the European Union's Horizon 2020 research and innovation programme under grant agreement No 857287
DOI: 10.23729/485ff7ca-b0ca-4cf8-830a-28e05e9f739a
title: GRASS Paleoclimate modeling

description: EC-Earth Paleoclimate simulations funded by the Academy of Finland
DOI: 10.21335/nmdc-1000944115
title: Modelled trophic interactions in the Norwegian Sea pelagic food-web, 1988-2020

description: This dataset is a collection of 1000 reconstruction of trophic (predator-prey) interactions and fisheries catches for the dominant pelagic species in the Norwegian Sea from 1988 to 2020. The reconstructions were generated using the CaN modelling framework (doi.org/10.1093/icesjms/fsz173) and the RCaN modelling software (doi.org/10.1101/2021.06.09.447734). The data is provided in Rdata format and can be opened in the programming software R (cran.r-project.org). The file contains a single object which is a list of all the input data used to run the model and all the outputs from the model reconstructions. The publication entitled “Quantification of trophic interactions in the Norwegian Sea pelagic food-web over multiple decades” (doi.org/10.5281/ZENODO.5710848) provides a detailed presentation of the model structure and inputs, and interpretations of the model outputs.
DOI: 10.5517/ccdc.csd.cc2746f1
title: CCDC 2059622: Experimental Crystal Structure Determination

description: Related Article: Peter Kucmierczyk, Stephan Behrens, Christoph Kubis, Wolfgang Baumann, Zhihong Wei, Haijun Jiao, Kaiwu Dong, Anke Spannenberg, Helfried Neumann, Ralf Jackstell, Armin Börner, Robert Franke, Matthias Beller|2022|Cat.Sci.Tech.|12|3175|doi:10.1039/D0CY02248A
DOI: 10.5517/ccdc.csd.cc27pf1f
title: CCDC 2076164: Experimental Crystal Structure Determination

description: Related Article: Dale Xie, Sheng Wang, Shihao Li, Wenqing Yang, Yi-Si Feng|2022|Cat.Sci.Tech.|12|3254|doi:10.1039/D2CY00387B
DOI: 10.5517/ccdc.csd.cc28c774
title: CCDC 2096165: Experimental Crystal Structure Determination

description: Related Article: Larisa Yu. Gurskaya, Yuliya F. Polienko, Tatyana V. Rybalova, Nina P. Gritsan, Alexey A. Dmitriev, Maxim S. Kazantsev, Elena V. Zaytseva, Dmitry A. Parkhomenko, Irina V. Beregovaya, Galina A. Zakabluk, Evgeny V. Tretyakov|2022|Eur.J.Org.Chem.|2022|e202101234|doi:10.1002/ejoc.202101234
DOI: 10.5517/ccdc.csd.cc28c785
title: CCDC 2096166: Experimental Crystal Structure Determination

description: Related Article: Larisa Yu. Gurskaya, Yuliya F. Polienko, Tatyana V. Rybalova, Nina P. Gritsan, Alexey A. Dmitriev, Maxim S. Kazantsev, Elena V. Zaytseva, Dmitry A. Parkhomenko, Irina V. Beregovaya, Galina A. Zakabluk, Evgeny V. Tretyakov|2022|Eur.J.Org.Chem.|2022|e202101234|doi:10.1002/ejoc.202101234
DOI: 10.5517/ccdc.csd.cc28c796
title: CCDC 2096167: Experimental Crystal Structure Determination

description: Related Article: Larisa Yu. Gurskaya, Yuliya F. Polienko, Tatyana V. Rybalova, Nina P. Gritsan, Alexey A. Dmitriev, Maxim S. Kazantsev, Elena V. Zaytseva, Dmitry A. Parkhomenko, Irina V. Beregovaya, Galina A. Zakabluk, Evgeny V. Tretyakov|2022|Eur.J.Org.Chem.|2022|e202101234|doi:10.1002/ejoc.202101234
DOI: 10.5517/ccdc.csd.cc28c7b7
title: CCDC 2096168: Experimental Crystal Structure Determination

description: Related Article: Larisa Yu. Gurskaya, Yuliya F. Polienko, Tatyana V. Rybalova, Nina P. Gritsan, Alexey A. Dmitriev, Maxim S. Kazantsev, Elena V. Zaytseva, Dmitry A. Parkhomenko, Irina V. Beregovaya, Galina A. Zakabluk, Evgeny V. Tretyakov|2022|Eur.J.Org.Chem.|2022|e202101234|doi:10.1002/ejoc.202101234
DOI: 10.5517/ccdc.csd.cc28c7c8
title: CCDC 2096169: Experimental Crystal Structure Determination

description: Related Article: Larisa Yu. Gurskaya, Yuliya F. Polienko, Tatyana V. Rybalova, Nina P. Gritsan, Alexey A. Dmitriev, Maxim S. Kazantsev, Elena V. Zaytseva, Dmitry A. Parkhomenko, Irina V. Beregovaya, Galina A. Zakabluk, Evgeny V. Tretyakov|2022|Eur.J.Org.Chem.|2022|e202101234|doi:10.1002/ejoc.202101234
DOI: 10.5517/ccdc.csd.cc28c7d9
title: CCDC 2096170: Experimental Crystal Structure Determination

description: Related Article: Larisa Yu. Gurskaya, Yuliya F. Polienko, Tatyana V. Rybalova, Nina P. Gritsan, Alexey A. Dmitriev, Maxim S. Kazantsev, Elena V. Zaytseva, Dmitry A. Parkhomenko, Irina V. Beregovaya, Galina A. Zakabluk, Evgeny V. Tretyakov|2022|Eur.J.Org.Chem.|2022|e202101234|doi:10.1002/ejoc.202101234
DOI: 10.5517/ccdc.csd.cc28c7fb
title: CCDC 2096171: Experimental Crystal Structure Determination

description: Related Article: Larisa Yu. Gurskaya, Yuliya F. Polienko, Tatyana V. Rybalova, Nina P. Gritsan, Alexey A. Dmitriev, Maxim S. Kazantsev, Elena V. Zaytseva, Dmitry A. Parkhomenko, Irina V. Beregovaya, Galina A. Zakabluk, Evgeny V. Tretyakov|2022|Eur.J.Org.Chem.|2022|e202101234|doi:10.1002/ejoc.202101234
DOI: 10.5517/ccdc.csd.cc28c7hd
title: CCDC 2096173: Experimental Crystal Structure Determination

description: Related Article: Larisa Yu. Gurskaya, Yuliya F. Polienko, Tatyana V. Rybalova, Nina P. Gritsan, Alexey A. Dmitriev, Maxim S. Kazantsev, Elena V. Zaytseva, Dmitry A. Parkhomenko, Irina V. Beregovaya, Galina A. Zakabluk, Evgeny V. Tretyakov|2022|Eur.J.Org.Chem.|2022|e202101234|doi:10.1002/ejoc.202101234
DOI: 10.5517/ccdc.csd.cc28pmf1
title: CCDC 2106153: Experimental Crystal Structure Determination

description: Related Article: Kshitish Chandra Hati, Santosh Kumar, Sahabaj Mondal, Surajit Singh, Ananda Shit, Sujay Kumar Nandi, Debasish Haldar|2022|Mat.Advs.|3|4194|doi:10.1039/D2MA00084A
DOI: 10.5517/ccdc.csd.cc28pmg2
title: CCDC 2106154: Experimental Crystal Structure Determination

description: Related Article: Kshitish Chandra Hati, Santosh Kumar, Sahabaj Mondal, Surajit Singh, Ananda Shit, Sujay Kumar Nandi, Debasish Haldar|2022|Mat.Advs.|3|4194|doi:10.1039/D2MA00084A
DOI: 10.5517/ccdc.csd.cc29g5pn
title: CCDC 2128791: Experimental Crystal Structure Determination

description: Related Article: Andrew Howe, Timofey Liseev, Marcos Gil-Sepulcre, Carolina Gimbert-Suriñach, Jordi Benet-Buchholz, Antoni Llobet, Sascha Ott|2022|Mat.Advs.|3|4227|doi:10.1039/D2MA00128D
DOI: 10.5517/ccdc.csd.cc2b9gns
title: CCDC 2154055: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Jean’ne M. Shreeve|2022|Mat.Advs.|3|4289|doi:10.1039/D2MA00307D
DOI: 10.5517/ccdc.csd.cc2b9gpt
title: CCDC 2154056: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Jean’ne M. Shreeve|2022|Mat.Advs.|3|4289|doi:10.1039/D2MA00307D
DOI: 10.5517/ccdc.csd.cc2b9gqv
title: CCDC 2154057: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Jean’ne M. Shreeve|2022|Mat.Advs.|3|4289|doi:10.1039/D2MA00307D
DOI: 10.5517/ccdc.csd.cc2b9grw
title: CCDC 2154058: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Jean’ne M. Shreeve|2022|Mat.Advs.|3|4289|doi:10.1039/D2MA00307D
DOI: 10.5517/ccdc.csd.cc249jxx
title: CCDC 1975379: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Jean’ne M. Shreeve|2022|Mat.Advs.|3|4289|doi:10.1039/D2MA00307D
DOI: 10.5517/ccdc.csd.cc249jww
title: CCDC 1975378: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Jean’ne M. Shreeve|2022|Mat.Advs.|3|4289|doi:10.1039/D2MA00307D
DOI: 10.5517/ccdc.csd.cc2b9gsx
title: CCDC 2154059: Experimental Crystal Structure Determination

description: Related Article: Ajay Kumar Chinnam, Richard J. Staples, Jean’ne M. Shreeve|2022|Mat.Advs.|3|4289|doi:10.1039/D2MA00307D
DOI: 10.5517/ccdc.csd.cc29g99d
title: CCDC 2128903: Experimental Crystal Structure Determination

description: Related Article: Jonas Bruckmoser, Daniel Henschel, Sergei Vagin, Bernhard Rieger|2022|Cat.Sci.Tech.|12|3295|doi:10.1039/D2CY00436D
DOI: 10.5281/zenodo.6572942
title: Pycirk exiobase data

description: A version of EXIOBASE multi-regional SUTs V3.3 for 2011 and that has been: Expanded in labels for all categories (including synonyms, country regions and names in the multiindexes to facilitate slicing). Expanded by including characterization tables (originally developed under DESIRE FP7) The datasets are pickled and are meant to be used with pycirk a modelling software to simulate EEIO structural change due to technological and policy interventions. https://cmlplatform.github.io/pycirk/ This repository has been updated to contain the pxp ITA mrEEIO tables that are created by pycirk: mrIO_V3.3.pkl contains the regular pxp ITA mrEEIO of EXIOBASE for the year 2011 mrIO_V3.3.sm.pkl contains the pxp ITA mrEEIO of EXIOBASE for the year which have been modified to show the secondary materials industry which is typically missing from the EXIOBASE mrEEIO tables in pxp format
DOI: 10.5281/zenodo.4695822
title: Pycirk exiobase data

description: A version of EXIOBASE multi-regional SUTs V3.3 for 2011 and that has been: Expanded in labels for all categories (including synonyms, country regions and names in the multiindexes to facilitate slicing). Expanded by including characterization tables (originally developed under DESIRE FP7) The datasets are pickled and are meant to be used with pycirk a modelling software to simulate EEIO structural change due to technological and policy interventions. https://cmlplatform.github.io/pycirk/ This repository has been updated to contain the pxp ITA mrEEIO tables that are created by pycirk: mrIO_V3.3.pkl contains the regular pxp ITA mrEEIO of EXIOBASE for the year 2011 mrIO_V3.3.sm.pkl contains the pxp ITA mrEEIO of EXIOBASE for the year which have been modified to show the secondary materials industry which is typically missing from the EXIOBASE mrEEIO tables in pxp format
DOI: 10.5281/zenodo.6572961
title: Benchmark Repository for Floating-Point Support in KeY

description: The benchmark repository for the added floating-point support to the KeY deductive verifier
DOI: 10.5281/zenodo.6572940
title: Benchmark Repository for Floating-Point Support in KeY

description: The benchmark repository for the added floating-point support to the KeY deductive verifier
DOI: 10.5281/zenodo.6573016
title: syntact_cat

description: Speech emotion recognition is in the focus of research since several decades and has many applications. One problem is sparse data for supervised learning. One way to tackle this problem is the synthesis of data with emotion-simulating speech synthesis approaches. We present a synthesized database of three basic emotions and neutral expression based on rule-based manipulation for a diphone synthesizer which we release to the public. The database has been validated in several machine learning experiments as a training set to detect emotional expression from natural speech data. The scripts to generate such a database have been made open source and could be used to aid speech emotion recognition for a low resourced language, as MBROLA supports 35 languages.<br>
description: Creation framework available at https://github.com/felixbur/syntAct/
description: {"references": ["Burkhardt et al: (2022): SyntAct: A Synthesized Database of Basic Emotions"]}
DOI: 10.5281/zenodo.6573015
title: syntact_cat

description: Speech emotion recognition is in the focus of research since several decades and has many applications. One problem is sparse data for supervised learning. One way to tackle this problem is the synthesis of data with emotion-simulating speech synthesis approaches. We present a synthesized database of three basic emotions and neutral expression based on rule-based manipulation for a diphone synthesizer which we release to the public. The database has been validated in several machine learning experiments as a training set to detect emotional expression from natural speech data. The scripts to generate such a database have been made open source and could be used to aid speech emotion recognition for a low resourced language, as MBROLA supports 35 languages.<br>
description: Creation framework available at https://github.com/felixbur/syntAct/
description: {"references": ["Burkhardt et al: (2022): SyntAct: A Synthesized Database of Basic Emotions"]}
DOI: 10.25392/leicester.data.19804360
title: 1000 Genome Project samples STR genotypes

description: STR genotypes generated by GangSTR and HipSTR on selected 1000 Genomes samples. A readme is included for further technical information.
DOI: 10.25392/leicester.data.19804360.v1
title: 1000 Genome Project samples STR genotypes

description: STR genotypes generated by GangSTR and HipSTR on selected 1000 Genomes samples. A readme is included for further technical information.
DOI: 10.14457/cu.the.1999.538
title: ผลของการใช้คอมพิวเตอร์ช่วยสอนเรื่องกระบวนการพยาบาล และสัมพันธภาพเพื่อการบำบัดในผู้ป่วยจิตเวชต่อความรู้ และการคิดอย่างมีวิจารณญาณของนักศึกษาพยาบาล

description: การวิจัยครั้งนี้เป็นการวิจัยกึ่งทดลองมีวัตถุประสงค์ เพื่อศึกษาผลของการใช้คอมพิวเตอร์ช่วยสอนเรื่องกระบวนการพยาบาล และสัมพันธภาพ เพื่อการบำบัดในผู้ป่วยจิตเวชต่อความรู้และการคิดอย่างมีวิจารณญาณ ของนักศึกษาพยาบาลก่อนและหลังได้รับการสอนโดยใช้คอมพิวเตอร์ช่วยสอน และเปรียบเทียบความรู้เรื่องกระบวนการพยาบาลและสัมพันธภาพเพื่อการบำบัด ในผู้ป่วยจิตเวชและการคิดอย่างมีวิจารณญาณของนักศึกษากลุ่ม ที่ได้รับการสอนโดยใช้คอมพิวเตอร์ช่วยสอนกับนักศึกษากลุ่ม ที่ได้รับการสอบแบบปกติ กลุ่มตัวอย่าง คือ นักศึกษาพยาบาลชั้นปีที่ 2 วิทยาลัยพยาบาลตำรวจ จำนวน 40 คน จัดเข้ากลุ่มทดลองและกลุ่มควบคุมกลุ่มละ 20 คน ด้วยวิธีการสุ่มแบบเฉพาะเจาะจงและจัดกลุ่มด้วยวิธีการจับคู่ เครื่องมือที่ใช้ในการวิจัยประกอบด้วยบทเรียนคอมพิวเตอร์ช่วยสอน แบบทดสอบความรู้เรื่องกระบวนการพยาบาลจิตเวช และเรื่องสัมพันธภาพเพื่อการบำบัดในผู้ป่วยจิตเวช และแบบทดสอบการคิดอย่างมีวิจารณญาณ เครื่องมือทั้งหมดนี้ผู้วิจัยสร้างขึ้นเอง และได้ผ่านการตรวจสอบความตรงเชิงเนื้อหา โดยผู้ทรงคุณวุฒิด้านบทเรียนคอมพิวเตอร์ช่วยสอน และผู้ทรงคุณวุฒิทางการพยาบาลจิตเวช สำหรับแบบทดสอบความรู้เรื่องกระบวนการพยาบาลจิตเวช และสัมพันธภาพเพื่อบำบัดในผู้ป่วยจิตเวช และแบบทดสอบการคิดอย่างมีวิจารณญาณได้ผ่านการตรวจสอบความเที่ยง และได้ค่าความเที่ยงเป็น 0.83 และ 0.77 ตามลำดับ สถิติที่ใช้ในการวิเคราะห์ข้อมูลคือ ค่าเฉลี่ย ส่วนเบี่ยงเบนมาตรฐานและสถิติทดสอบที ผลการวิจัยพบว่า 1. ความรู้เรื่องกระบวนการพยาบาลและสัมพันธภาพ เพื่อการบำบัดในผู้ป่วยจิตเวช และการคิดอย่างมีวิจารณญาณของนักศึกษาพยาบาล กลุ่มที่ได้รับการสอนโดยใช้คอมพิวเตอร์ช่วยสอนภายหลังการทดลอง สูงกว่าก่อนการทดลองสูงกว่าก่อนการทดลองอย่างมีนัยสำคัญทางสถิติที่ระดับ .05 2. ความรู้เรื่องกระบวนการพยาบาลและสัมพันธภาพ เพื่อการบำบัดในผู้ป่วยจิตเวช และการคิดอย่างมีวิจารณญาณของนักศึกษาพยาบาล กลุ่มที่ได้รับการสอนโดยใช้คอมพิวเตอร์ช่วยสอนสูงกว่ากลุ่ม ที่ได้รับการสอนแบบปกติอย่างมีนัยสำคัญทางสถิติที่ระดับ .05