cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bglr5
title: CCDC 2158987: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Daniel Franz|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bgls6
title: CCDC 2158988: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Daniel Franz|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bglt7
title: CCDC 2158989: Experimental Crystal Structure Determination

description: Related Article: Michael Bolte, Daniel Franz|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g39hd
title: CCDC 2267293: Experimental Crystal Structure Determination

description: Related Article: Geetika  Patel|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g4y9w
title: CCDC 2268868: Experimental Crystal Structure Determination

description: Related Article: Dmitry Yufit|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g4ycy
title: CCDC 2268870: Experimental Crystal Structure Determination

description: Related Article: zhao Fang|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g6dcg
title: CCDC 2270265: Experimental Crystal Structure Determination

description: Related Article: Jamal Mohamed Hamid Alaboosh|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g6jt1
title: CCDC 2270403: Experimental Crystal Structure Determination

description: Related Article: Yannick Stöferle, Peter Chen|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.23974902
title: Students' academic achievement test , Survey and RTOP data for the implementation of problem-based learning method supplemented by YouTube videos, Uganda

description: The aim of the study was to assess the effectiveness of problem-based learning (PBL) on students' academic achievement in physics. Lesson observation data was collected using the Reformed Teaching Observation Protocol (RTOP) to assess the implementation of PBL in a physics classroom in Uganda. Data on Achievement test included; A total of 829 students from eight schools (four public schools and four private schools) were taught simple machines in physics using PBL and traditional approach. The students were tested on critical thinking and problem-solving (Pre & Post)test. 144 students were taught using PBL method supplemented by videos on simple machines downloaded from YouTube.The students participated in filling the surveys (critical thinking and problem solving) with scales ranging from “strongly agree” to “strongly disagree”. The critical thinking survey had 19 items, while the problem-solving survey had 31 items. These students were from four schools (two government schools and two private schools). Therefore, 973 (829+144) students took part in the study. The data can be viewed in the Excel sheets provided.
DOI: 10.25345/c5sj1b231
title: MassIVE MSV000093020 - GNPS GCMS apple spatial variability
title: Multiscale variation of surface metabolome of Pink Lady apples. Samples collect to assess variability within-fruit, within-tree, and within-orchard.
DOI: 10.6084/m9.figshare.24237313
title: Untitled Item

description: This study examines Teacher Digital Competence, Learning Agility, Student Engagement, and Academic Performance and integrates them into the theoretical framework. A survey methodology was used to create and validate instruments for reliability and validity. Content validity index, face and content validity, and confirmatory factor analysis were used for validation. The investigation included 230 non-sample respondents to guarantee generalizability. Then, the measurement model was created. The questionnaire was presented to 382 individuals after validation. They were among 10,200 Jambi Province vocational school (SMK) and high school (SMA) instructors. The data was then analyzed using PLS-SEM. This study examined significant educational performance indicators in detail. Teacher digital competence was investigated for its effects on learning agility, student engagement, and academic performance. Teacher Digital Competence did not significantly impact Learning Agility (β = 0.490, p = 0.431), Student Engagement (β = 0.595, p = 0.722), or Academic Performance (β = 0.306, p = 0.582). Findings indicate that Learning Agility (β = 0.236, p &lt; 0.001) and Student Engagement (β = 0.094, p &lt; 0.001) positively effect Academic Performance. Learning Agility and Student Engagement are crucial to academic performance, however Teacher Digital Competence may indirectly affect these aspects or warrant more study.It emphasizes the complex interaction of various elements and the need for holistic approaches to improve academic achievements in the digital age. This diverse educational landscape's intricate dynamics and potential mediating elements warrant further study.
DOI: 10.5281/zenodo.8403733
title: A gap in primate default mode network organization (Atlases and Script)

description: Code deposit in Python for the article: "A gap in primate default mode network organization" published in Cell Report. + Dictionary learning-based functional atlas in four primate species (mouse lemurs, marmosets, macaques, and humans) BOLD images were acquired in mouse lemurs, marmosets, macaques, and humans<br> Each functional atlas was made using a dictionary learning analysis with 7 components on pre-treated BOLD images.<br> Each component was concatenated and labeled to create each atlas.<br> The atlases were broken into functional regions automatically and manually.<br> <br> For each species:<br> DL7cpts_DicL_yung.nii.gz ==&gt; output of the dictionary learning<br> <br> dict_learning_7compos_concat.nii.gz ==&gt; functional atlas<br> <br> dict_learning_7compos_concat_break.nii.gz ==&gt; functional atlas segmented automatically into functional regions<br> or<br> dict_learning_7compos_concat_break_handseg.nii.gz ==&gt; functional atlas segmented automatically and manually into functional regions if used for publication please cite: <strong>An evolutionary gap in primate default mode network organization</strong><br> <strong>Clement M Garin</strong>, Yuki Hori, Stefan Everling, Christopher T Whitlow, Finnegan Calabro, Beatriz Luna, Mathilda Froesel, Maëva Gacoin, Suliann Ben Hamed, Marc Dhenain, Christos Constantinidis Apr 2022<br> <strong>Cell Report</strong> 39, 2, 110669<br> DOI: 10.1016/j.celrep.2022.110669
description: {"references": ["10.1016/j.celrep.2022.110669"]}
DOI: 10.5281/zenodo.6369416
title: A gap in primate default mode network organization (Atlases and Script)

description: Code deposit in Python for the article: "A gap in primate default mode network organization" published in Cell Report. + Dictionary learning-based functional atlas in four primate species (mouse lemurs, marmosets, macaques, and humans) BOLD images were acquired in mouse lemurs, marmosets, macaques, and humans<br> Each functional atlas was made using a dictionary learning analysis with 7 components on pre-treated BOLD images.<br> Each component was concatenated and labeled to create each atlas.<br> The atlases were broken into functional regions automatically and manually.<br> <br> For each species:<br> DL7cpts_DicL_yung.nii.gz ==&gt; output of the dictionary learning<br> <br> dict_learning_7compos_concat.nii.gz ==&gt; functional atlas<br> <br> dict_learning_7compos_concat_break.nii.gz ==&gt; functional atlas segmented automatically into functional regions<br> or<br> dict_learning_7compos_concat_break_handseg.nii.gz ==&gt; functional atlas segmented automatically and manually into functional regions if used for publication please cite: <strong>An evolutionary gap in primate default mode network organization</strong><br> <strong>Clement M Garin</strong>, Yuki Hori, Stefan Everling, Christopher T Whitlow, Finnegan Calabro, Beatriz Luna, Mathilda Froesel, Maëva Gacoin, Suliann Ben Hamed, Marc Dhenain, Christos Constantinidis Apr 2022<br> <strong>Cell Report</strong> 39, 2, 110669<br> DOI: 10.1016/j.celrep.2022.110669
description: {"references": ["10.1016/j.celrep.2022.110669"]}
DOI: 10.5281/zenodo.8403765
title: A gap in primate default mode network organization (Atlases and Script)

description: Code deposit in Python for the article: "A gap in primate default mode network organization" published in Cell Report. + Dictionary learning-based functional atlas in four primate species (mouse lemurs, marmosets, macaques, and humans) BOLD images were acquired in mouse lemurs, marmosets, macaques, and humans<br> Each functional atlas was made using a dictionary learning analysis with 7 components on pre-treated BOLD images.<br> Each component was concatenated and labeled to create each atlas.<br> The atlases were broken into functional regions automatically and manually.<br> <br> For each species:<br> DL7cpts_DicL_yung.nii.gz ==&gt; output of the dictionary learning<br> <br> dict_learning_7compos_concat.nii.gz ==&gt; functional atlas<br> <br> dict_learning_7compos_concat_break.nii.gz ==&gt; functional atlas segmented automatically into functional regions<br> or<br> dict_learning_7compos_concat_break_handseg.nii.gz ==&gt; functional atlas segmented automatically and manually into functional regions if used for publication please cite: <strong>An evolutionary gap in primate default mode network organization</strong><br> <strong>Clement M Garin</strong>, Yuki Hori, Stefan Everling, Christopher T Whitlow, Finnegan Calabro, Beatriz Luna, Mathilda Froesel, Maëva Gacoin, Suliann Ben Hamed, Marc Dhenain, Christos Constantinidis Apr 2022<br> <strong>Cell Report</strong> 39, 2, 110669<br> DOI: 10.1016/j.celrep.2022.110669
description: {"references": ["10.1016/j.celrep.2022.110669"]}
DOI: 10.5281/zenodo.8215040
title: FEDORA. Artifacts from two study groups on inter/multi/transdisciplinary forms of knowledge organization and multi-actor contexts of knowledge construction

description: The dataset contains artifacts from two intensive multi-actors and inter-disciplinary study groups realized within the FEDORA project. The basic artifacts are jamboard used to collect and share experiences of interdisciplianity in terms of: barriers, features of good interdisciplinarity experiences, interdisciplinary skills, forms of knowledge organization. The study groups involved 14 participants with diverse backgrounds (physics, chemistry, educational science, management, mathematics, philosophy, history of science, sociology, science communication). The interdisciplinary group discussions drew on the reading selected by UNIBO and experience from international projects and yielded three narratives of the barriers, conditions that facilitate interdisciplinarity, and interdisciplinary attitudes and skills to inter/multi/transdisciplinarity. The results are detailed in the deliverable D1.1 (DOI 10.5281/zenodo.7519006)
description: {"references": ["Raminta Pucetaite, & Rimantas Rauleckas. (2023). Framework for aligning science teaching/learning in formal contexts with the modus operandi of R&I: new inter-multi-transdisciplinary forms of knowledge organisation for co- teaching and open-schooling - Deliverable 1.2 (V4.0). Zenodo. https://doi.org/10.5281/zenodo.7519006"]}
DOI: 10.5281/zenodo.8215041
title: FEDORA. Artifacts from two study groups on inter/multi/transdisciplinary forms of knowledge organization and multi-actor contexts of knowledge construction

description: The dataset contains artifacts from two intensive multi-actors and inter-disciplinary study groups realized within the FEDORA project. The basic artifacts are jamboard used to collect and share experiences of interdisciplianity in terms of: barriers, features of good interdisciplinarity experiences, interdisciplinary skills, forms of knowledge organization. The study groups involved 14 participants with diverse backgrounds (physics, chemistry, educational science, management, mathematics, philosophy, history of science, sociology, science communication). The interdisciplinary group discussions drew on the reading selected by UNIBO and experience from international projects and yielded three narratives of the barriers, conditions that facilitate interdisciplinarity, and interdisciplinary attitudes and skills to inter/multi/transdisciplinarity. The results are detailed in the deliverable D1.1 (DOI 10.5281/zenodo.7519006)
description: {"references": ["Raminta Pucetaite, & Rimantas Rauleckas. (2023). Framework for aligning science teaching/learning in formal contexts with the modus operandi of R&I: new inter-multi-transdisciplinary forms of knowledge organisation for co- teaching and open-schooling - Deliverable 1.2 (V4.0). Zenodo. https://doi.org/10.5281/zenodo.7519006"]}
DOI: 10.5281/zenodo.8199146
title: FEDORA. Data from teaching module implementations in Finland

description: The dataset contains data collected in two-round implementations of new experimental teaching-learning course “My city of the future”, with Finnish secondary school students, that are analysed to capture the main trends in the data regarding the established framework, with specific focus on effects on the course module on students, and the capacity of the module to address higher-level standards of future-oriented science education. Specifically, the dataset includes: (i) answers to questionnaires, (ii) transcripts of interviews (iii) materials created by students (in Finnish), (iv) processed versions of aforementioned data such as coded interview transcripts or numeric data on code frequencies, and (v) results from the analysis of this data using thematic, discourse and phenomenographic qualitative methods. An aggregated version of student’s backgrounds (e.g. gender ratios) are also provided. FEDORA Project README: README Data Set Title: FEDORA. Data from teaching modules implementations in Finland Data Set Author/s: Antti Laherto, Tapio Rasa, Iina Hyyppä (University of Helsinki) Data Set Contact Person/s: Tapio Rasa (University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi; Data Set License: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Publication Year: 2024 Project Info: FEDORA (Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty, funded by European Union, Horizon 2020 Programme. Grant Agreement num. 872841,<br> www.fedora-project.eu) Data set Contents The data set consists of: 1) Interview transcripts from students who participated in an experimental, twice implemented future-oriented science learning module ("My City of the Future") in Finland over 2022-2023; 2) Students' output in future-oriented learning tasks over the module; 3) Questionnaire data on students' future thinking; 4) Students' background data in aggregate, anonymised form (e.g. gender ratio); 5) Processed forms of aforementioned data (such as qualitatively coded datasets). Data set Documentation <em>See DOI:10.5281/zenodo.8199147 (TBA)</em>
DOI: 10.5281/zenodo.8199147
title: FEDORA. Data from teaching module implementations in Finland

description: The dataset contains data collected in two-round implementations of new experimental teaching-learning course “My city of the future”, with Finnish secondary school students, that are analysed to capture the main trends in the data regarding the established framework, with specific focus on effects on the course module on students, and the capacity of the module to address higher-level standards of future-oriented science education. Specifically, the dataset includes: (i) answers to questionnaires, (ii) transcripts of interviews (iii) materials created by students (in Finnish), (iv) processed versions of aforementioned data such as coded interview transcripts or numeric data on code frequencies, and (v) results from the analysis of this data using thematic, discourse and phenomenographic qualitative methods. An aggregated version of student’s backgrounds (e.g. gender ratios) are also provided. FEDORA Project README: README Data Set Title: FEDORA. Data from teaching modules implementations in Finland Data Set Author/s: Antti Laherto, Tapio Rasa, Iina Hyyppä (University of Helsinki) Data Set Contact Person/s: Tapio Rasa (University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi; Data Set License: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Publication Year: 2024 Project Info: FEDORA (Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty, funded by European Union, Horizon 2020 Programme. Grant Agreement num. 872841,<br> www.fedora-project.eu) Data set Contents The data set consists of: 1) Interview transcripts from students who participated in an experimental, twice implemented future-oriented science learning module ("My City of the Future") in Finland over 2022-2023; 2) Students' output in future-oriented learning tasks over the module; 3) Questionnaire data on students' future thinking; 4) Students' background data in aggregate, anonymised form (e.g. gender ratio); 5) Processed forms of aforementioned data (such as qualitatively coded datasets). Data set Documentation <em>See DOI:10.5281/zenodo.8199147 (TBA)</em>
DOI: 10.5281/zenodo.4734161
title: DATASET: Future, technology and agency: Students' experiences from a course on futures thinking and quantum computing

description: Finnish-language dataset. Related to a research article that is awaiting acceptance for publication: <em>Future, technology and agency: Students’ experiences from a course on futures thinking and quantum computing</em>. As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Instead of students' interviews (the context of which is given in the article). In a nutshell, 21 upper-secondary school students were interviewed in 2018 regarding their experiences on taking an experimental science course that combined ideas from futures thinking and quantum computing. The present dataset contains all 245 transcribed passages from 21 student interviews that were initially marked as relevant to the research goals (i.e. how students saw their conceptions change over the course). Additionally, for each passage the final coding that was used in the analysis for the research paper is shown. The "number-letter codes" were used as shorthands; the full names of the codes correspond closely with the final, English-language codes in the paper. To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the passages are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters. Please also note that the character &gt; marks change of speaker. Identifying the interviewer and interviewee should be straighforward based on the context. Please contact the corresponding author for more information.