cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17608/k6.auckland.23272343.v1
title: Dataset III_H5P workshop staff exit survey_v1 0-06-2023.csv

description: The H5P workshop exit survey (see ESM D) incorporated elements of two validated questionaires, Brookfield's (1998) critical incident questionnaire; captured participants' reactions in the emotional domain using an emoji sentiment scale (Marder <i>et al.</i> 2020), and used the Net Promoter Score (Reichheld 2003) as a global satisfaction metric. Members of the research team completed the survey and refined it. As this is a descriptive study, we did not undertake further validity and reliability testing of the dataset III survey. We distributed these surveys at the end of each H5P Faculty Development (FD) workshop and obtained an approximate 30% response rate.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272346
title: Dataset IVb_Open ended student comments_teaching evaluations 2018_v1 01-06-2023.csv

description: <i>Dataset IV:</i> We collected routine anonymous Student Evaluation of Teaching [SET] and formative evaluations from the course 'MEDSCI 203 - Mechanisms of Disease' from 2018 to 2021. We retrospectively analyzed the data to compare students' perceptions of the course before and after incorporating interactive online resources made with H5P, first introduced in 2019 by an early adopter of H5P. The response rate range was 14.1 - 97% out of a class of 182 (2018) - 314 (2021) students per year.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272346.v1
title: Dataset IVb_Open ended student comments_teaching evaluations 2018_v1 01-06-2023.csv

description: <i>Dataset IV:</i> We collected routine anonymous Student Evaluation of Teaching [SET] and formative evaluations from the course 'MEDSCI 203 - Mechanisms of Disease' from 2018 to 2021. We retrospectively analyzed the data to compare students' perceptions of the course before and after incorporating interactive online resources made with H5P, first introduced in 2019 by an early adopter of H5P. The response rate range was 14.1 - 97% out of a class of 182 (2018) - 314 (2021) students per year.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272349
title: Dataset IVa_SET and OLE Course and Teacher Evaluation Reports MEDSCI 203_2018-2021_v1 01-06-2023.pdf

description: <i>Dataset IV:</i> We collected routine anonymous Student Evaluation of Teaching [SET] and formative evaluations from the course 'MEDSCI 203 - Mechanisms of Disease' from 2018 to 2021. We retrospectively analyzed the data to compare students' perceptions of the course before and after incorporating interactive online resources made with H5P, first introduced in 2019 by an early adopter of H5P. The response rate range was 14.1 - 97% out of a class of 182 (2018) - 314 (2021) students per year.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272352
title: Dataset I_All teaching staff survey of opportunities to strengthen online teaching capabilities._v1 01-06-2023.csv

description: <i>Dataset I:</i> We designed the all-teaching staff survey (dataset I, also see ESM B) to capture a holistic view of the teaching staff's perceptions of all the Faculty Development (FD) available and how they have applied any knowledge or skills acquired from them. Members of the research team completed the survey and refined it. We emailed an online survey to the Faculty of Medical and Health Sciences (FMHS), University of Auckland teaching community, comprising approximately 460 staff members. We included the invitation to an interview by a link at the end of the survey. Following face validity, we undertook pilot testing of dataset I (all-teaching staff survey) with the first five survey responses, minor changes were made and the recruiting continued. As this is a descriptive study, we did not undertake further validity and reliability testing of the dataset I survey.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272352.v1
title: Dataset I_All teaching staff survey of opportunities to strengthen online teaching capabilities._v1 01-06-2023.csv

description: <i>Dataset I:</i> We designed the all-teaching staff survey (dataset I, also see ESM B) to capture a holistic view of the teaching staff's perceptions of all the Faculty Development (FD) available and how they have applied any knowledge or skills acquired from them. Members of the research team completed the survey and refined it. We emailed an online survey to the Faculty of Medical and Health Sciences (FMHS), University of Auckland teaching community, comprising approximately 460 staff members. We included the invitation to an interview by a link at the end of the survey. Following face validity, we undertook pilot testing of dataset I (all-teaching staff survey) with the first five survey responses, minor changes were made and the recruiting continued. As this is a descriptive study, we did not undertake further validity and reliability testing of the dataset I survey.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272355
title: Dataset II_Nvivo database on staff interview transcripts about faculty faculty development for online teaching_v1 01-06-2023.nvp

description: <i>Dataset II:</i> We designed the interviews (see ESM C) to capture a holistic view of the teaching staff's perceptions of all the Faculty Development (FD) available and how they have applied any knowledge or skills acquired from them. Members of the research team completed the questionnaire and refined it. We emailed an online survey to the Faculty of Medical and Health Sciences (FMHS) University of Auckland, teaching community, comprising approximately 460 staff members. We included the invitation to an interview by a link at the end of the survey.We went through an iterative approach to refining the interview question guide with our external consulting agency (Academic Consulting LTD).<br>Qualitative methods sampling continues until data saturation—when no new information emerges from the collected data—which, according to Ando et al. (Ando et al. 2014), often occurs with 12 participants in a relatively homogenous sample. We stopped interview recruitment at 20 participants.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272358
title: Dataset IVb_Open ended student comments_teaching evaluations 2019 _v1 01-06-2023.csv

description: <i>Dataset IV</i>: We collected routine anonymous Student Evaluation of Teaching [SET] and formative evaluations from the course 'MEDSCI 203 - Mechanisms of Disease' from 2018 to 2021. We retrospectively analyzed the data to compare students' perceptions of the course before and after incorporating interactive online resources made with H5P, first introduced in 2019 by an early adopter of H5P. The response rate range was 14.1 - 97% out of a class of 182 (2018) - 314 (2021) students per year.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272358.v1
title: Dataset IVb_Open ended student comments_teaching evaluations 2019 _v1 01-06-2023.csv

description: <i>Dataset IV</i>: We collected routine anonymous Student Evaluation of Teaching [SET] and formative evaluations from the course 'MEDSCI 203 - Mechanisms of Disease' from 2018 to 2021. We retrospectively analyzed the data to compare students' perceptions of the course before and after incorporating interactive online resources made with H5P, first introduced in 2019 by an early adopter of H5P. The response rate range was 14.1 - 97% out of a class of 182 (2018) - 314 (2021) students per year.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272364
title: Dataset V_Raw student H5P assignment grades 2018-19_v1 01-06-2023.csv

description: <i>Dataset V:</i> We analyzed student grades from the MEDSCI 203 course (Faculty of Medical and Health Sciences, University of Auckland), to compare student grades in coursework assignments before (2018) and after incorporating interactive online resources using H5P in 2019. We undertook descriptive analyses using Excel and ran a t-test analysis on dataset V to determine the significance (p&lt;.001) of changes in assignment grades between 2018 and 2019, before and after the introduction of H5P.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).<br>
DOI: 10.17608/k6.auckland.23272367
title: Dataset IVb_Open ended student comments_teaching evaluations 2021_v1 01-06-2023.csv

description: <i>Dataset IV:</i> We collected routine anonymous Student Evaluation of Teaching [SET] and formative evaluations from the course 'MEDSCI 203 - Mechanisms of Disease' from 2018 to 2021. We retrospectively analyzed the data to compare students' perceptions of the course before and after incorporating interactive online resources made with H5P, first introduced in 2019 by an early adopter of H5P. The response rate range was 14.1 - 97% out of a class of 182 (2018) - 314 (2021) students per year.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.17608/k6.auckland.23272367.v1
title: Dataset IVb_Open ended student comments_teaching evaluations 2021_v1 01-06-2023.csv

description: <i>Dataset IV:</i> We collected routine anonymous Student Evaluation of Teaching [SET] and formative evaluations from the course 'MEDSCI 203 - Mechanisms of Disease' from 2018 to 2021. We retrospectively analyzed the data to compare students' perceptions of the course before and after incorporating interactive online resources made with H5P, first introduced in 2019 by an early adopter of H5P. The response rate range was 14.1 - 97% out of a class of 182 (2018) - 314 (2021) students per year.<br>This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, <b>13</b>:127 (https://doi.org/10.12688/mep.19692.1).
DOI: 10.6084/m9.figshare.24128874
title: IBCL Field Test 2 Data

description: Raw data file from Field Test 2 implementation of the Inventory of Biotic Climate Literacy.
DOI: 10.6084/m9.figshare.24128874.v1
title: IBCL Field Test 2 Data

description: Raw data file from Field Test 2 implementation of the Inventory of Biotic Climate Literacy.
DOI: 10.5281/zenodo.8338118
title: time-pathloss data for MANET or V2V

description: CSV time-location ij-pathloss ij for MANET
DOI: 10.5281/zenodo.8338119
title: time-pathloss data for MANET or V2V

description: CSV time-location ij-pathloss ij for MANET
DOI: 10.6084/m9.figshare.24128862
title: Community Lake Ice Collaboration. Lakes and the length of their long-term records.

description: The Community Lake Ice Collaboration project is discussed in detail in "Establishing a long-term citizen science project? Lessons learned from the Community Lake Ice Collaboration spanning over 30 yr and 1000 lakes" by Lianna S. Lopez, Aman Basu, Kevin Blagrave, Gerald Bove, Kenton Stewart, Dawn Bazely, Sapna Sharma.The data shared here contain information on the lake locations and the lengths of their ice phenology records. These data can be used to re-create Figure 2 from the manuscript.<b>Abstract. </b>Recruiting the public to participate in the scientific process can be invaluable in furthering our understanding of global environmental change. Several long-term citizen science projects have been active for over a decade, with most involving the public in the data collection phase of the scientific process. Our team has recently inherited a long-term citizen science project called the Community Lake Ice Collaboration (CLIC). We have benefited from engaging with a community of experienced participants who have collected reliable data for hundreds of lakes across the USA for over 30 yr. Collecting data at this scale would have been logistically and financially challenging without participants volunteering their time and effort. Here, we aim to provide an overview of the lessons we have learned from CLIC and other citizen science projects and develop nine guidelines for establishing and sustaining a long-term citizen science project.
DOI: 10.5281/zenodo.8339475
title: Datos TFM - Estudio de las métricas de uso de las revistas de la Universidad Nacional de Colombia

description: Datos para TFM - Estudio de las métricas de uso de las revistas de la Universidad Nacional de Colombia
DOI: 10.5281/zenodo.8339474
title: Datos TFM - Estudio de las métricas de uso de las revistas de la Universidad Nacional de Colombia

description: Datos para TFM - Estudio de las métricas de uso de las revistas de la Universidad Nacional de Colombia
DOI: 10.6084/m9.figshare.22783052
title: Phase of neural oscillations as a reference frame for attention-based routing in visual cortex

description: The data used to generate Figures 2B, 3A, 3B, 3C, 4A, 4B, 5, 6, 7A, S1, S2, S3, S4, S5, S6, and S8 in our research article titled 'Phase of neural oscillations as a reference frame for attention-based routing in visual cortex.'
DOI: 10.6084/m9.figshare.22783052.v1
title: Phase of neural oscillations as a reference frame for attention-based routing in visual cortex

description: The data used to generate Figures 2B, 3, 4, 5, 6A, S1, S2, S3, and S4 in our research article titled ' Phase of neural oscillations as a reference frame for attention-based routing in visual cortex.'
DOI: 10.5281/zenodo.8141256
title: SNANA curves preproccessed for Neural Network

description: SN light curves generated by SNANA, preprocessed for use in RNN architecture
DOI: 10.5281/zenodo.8141257
title: SNANA curves preproccessed for Neural Network

description: SN lightcurves generated by SNANA. Preproccessed to give NN format
DOI: 10.5281/zenodo.8194081
title: SNANA curves preproccessed for Neural Network

description: SN lightcurves generated by SNANA. Preproccessed to give NN format
DOI: 10.5281/zenodo.8339459
title: SNANA curves preproccessed for Neural Network

description: SN light curves generated by SNANA, preprocessed for use in RNN architecture
DOI: 10.26183/weve-zg60
title: Foreign Accented Australian English dataset

description: This dataset contains: 

Foreign accent is a distinct attribute of second language (L2) speech production. Foreign-accented speech is characterized by deviations in the target language pronunciation due to the learners' native language (L1) influence (Best, 1995; Best & Tyler, 2007; Flege, 1995), specifically due to differences in and contact between L1 and L2 phonology and phonotactics (i.e. consonant/vowel acoustics & articulation, syllable structure), as well as prosody (i.e. intonation, rhythm, stress, pitch). The degree or strength of a foreign accent varies among L2 learners and depends on a number of factors, such as the age of L2 acquisition (Piske, MacKay, & Flege, 2001), language experience (i.e., length of residence in an L2-speaking country: Bohn & Flege, 1990), amount of use (Flege, Frieda, & Nozawa, 1997), motivation/language learning aptitude (Piske et al., 2001).

Automatic speech recognition (ASR) systems are predominantly trained on native speech data. However, while the native speakers have shown flexibility in adaptation to L2 speech, despite variation in L2 speakers’ accents, proficiency, and fluency, automatic recognition of foreign-accented speech degrades considerably in comparison to recognition of native speech (Derwing, Munro, & Carbonaro, 2000). Thus, the key challenges in ASR for accented speech are to ensure fast model adaptation on limited (and often homogenous) speech data and to facilitate recognition of unseen (untrained) accents (He & Zhao, 2002). Standard Australian English (AusE) is a distinct regional variety of the English language (Cox & Palethorpe, 2007). To enable accurate ASR of AusE speech, models need to be specifically trained on AusE speech data, in addition to American English and British English data (Chengalvarayan, 2001).

Audio data for 226 speakers of Australian English with an Arabic accent (150 males and 76 females)
This dataset contains sensitive information. To discuss the data, please contact d.estival@westernsydney.edu.au ORCID - 0000-0002-6178-3825
DOI: 10.6084/m9.figshare.24129144
title: applied

description: Data for applied intelligence.
DOI: 10.6084/m9.figshare.24129144.v1
title: applied

description: Data for applied intelligence.
DOI: 10.5281/zenodo.8339544
title: Attitude_AI_and_General_Tech

description: Datasets of the study
DOI: 10.5281/zenodo.8339543
title: Attitude_AI_and_General_Tech

description: Datasets of the study
DOI: 10.5281/zenodo.8339527
title: Nanostring PanCancer IO360 Data of Gastroenteropancreatic High-Grade Neuroendocrine Carcinoma

description: Zip file includes raw RCC files used by Nanostring for data visualization of genomic information along with links to the Nanostring website where this data can be viewed in graphical format.
DOI: 10.5281/zenodo.8339526
title: Nanostring PanCancer IO360 Data of Gastroenteropancreatic High-Grade Neuroendocrine Carcinoma

description: Zip file includes raw RCC files used by Nanostring for data visualization of genomic information along with links to the Nanostring website where this data can be viewed in graphical format.
DOI: 10.6084/m9.figshare.24129330
title: Investigating gut contents of the leptocleidian plesiosaur <i>Umoonasaurus demoscyllus</i> using micro-CT imaging

description: The holotype of <i>Umoonasaurus demoscyllus</i> (AM F.99374) is one of the most complete plesiosaur skeletons ever discovered in Australia. It preserves a gastric mass in the pectoral girdle region that has not yet been documented in detail. Here, we use high resolution micro-computed tomography (micro-CT) to image the gut contents of this specimen. These comprise 17 vertebrae from an unidentified teleost, together with at least 60 rounded gastroliths averaging about 5 mm in maximum length. Our study demonstrates the potential for 3D tomographic imaging to investigate Mesozoic marine reptile diets and ecology. <i>Joshua M. White [Joshua.white@anu.edu.au], Research School of Physics, Department of Materials Physics, Australian National University, Canberra, ACT 2601, Australia; Australian Museum Research Institute, 1 William Street, Sydney, New South Wales 2010, Australia. Aleese Barron [aleese.barron@anu.edu.au], Research School of Physics, Department of Materials Physics, Australian National University, Canberra ACT, 2601, Australia; School of Archaeology and Anthropology, Banks Building, Australian National University, Canberra, ACT 2601, Australia. Matthew R. McCurry [matthew.mccurry@austmus.gov.au], Australian Museum Research Institute, 1 William Street, Sydney, New South Wales 2010, Australia; Earth and Sustainability Science Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales 2052, Australia; Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20560, USA. Tim Denham [tim.denham@anu.edu.au], School of Archaeology and Anthropology, Banks Building, Australian National University, Canberra, ACT 2601, Australia.</i>
DOI: 10.6084/m9.figshare.24129330.v1
title: Investigating gut contents of the leptocleidian plesiosaur <i>Umoonasaurus demoscyllus</i> using micro-CT imaging

description: The holotype of <i>Umoonasaurus demoscyllus</i> (AM F.99374) is one of the most complete plesiosaur skeletons ever discovered in Australia. It preserves a gastric mass in the pectoral girdle region that has not yet been documented in detail. Here, we use high resolution micro-computed tomography (micro-CT) to image the gut contents of this specimen. These comprise 17 vertebrae from an unidentified teleost, together with at least 60 rounded gastroliths averaging about 5 mm in maximum length. Our study demonstrates the potential for 3D tomographic imaging to investigate Mesozoic marine reptile diets and ecology. <i>Joshua M. White [Joshua.white@anu.edu.au], Research School of Physics, Department of Materials Physics, Australian National University, Canberra, ACT 2601, Australia; Australian Museum Research Institute, 1 William Street, Sydney, New South Wales 2010, Australia. Aleese Barron [aleese.barron@anu.edu.au], Research School of Physics, Department of Materials Physics, Australian National University, Canberra ACT, 2601, Australia; School of Archaeology and Anthropology, Banks Building, Australian National University, Canberra, ACT 2601, Australia. Matthew R. McCurry [matthew.mccurry@austmus.gov.au], Australian Museum Research Institute, 1 William Street, Sydney, New South Wales 2010, Australia; Earth and Sustainability Science Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales 2052, Australia; Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC 20560, USA. Tim Denham [tim.denham@anu.edu.au], School of Archaeology and Anthropology, Banks Building, Australian National University, Canberra, ACT 2601, Australia.</i>