cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2gx7n9
title: CCDC 2291261: Experimental Crystal Structure Determination

description: Related Article: Peter J. Skabara, Simon J. Coles, Michael B. Hursthouse|2005|University of Southampton, Crystal Structure Report Archive||1165|
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.cc2gx7xk
title: CCDC 2291269: Experimental Crystal Structure Determination

description: Related Article: Simon J. Lancaster, Simon J. Coles, Michael B. Hursthouse|2005|University of Southampton, Crystal Structure Report Archive||1140|
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.cc2gx7zm
title: CCDC 2291271: Experimental Crystal Structure Determination

description: Related Article: Martin B. Smith, Mark E. Light, Michael B. Hursthouse|2000|University of Southampton, Crystal Structure Report Archive||738|
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.cc2gx88y
title: CCDC 2291280: Experimental Crystal Structure Determination

description: Related Article: Martin B. Smith, Simon J. Coles, Michael B. Hursthouse|2000|University of Southampton, Crystal Structure Report Archive||772|
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.cc2gx92s
title: CCDC 2291305: Experimental Crystal Structure Determination

description: Related Article: Simon J. Lancaster, Simon J. Coles, Michael B. Hursthouse|2005|University of Southampton, Crystal Structure Report Archive||1142|
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.cc2gx89z
title: CCDC 2291281: Experimental Crystal Structure Determination

description: Related Article: Simon J. Lancaster, Simon J. Coles, Michael B. Hursthous|2005|University of Southampton, Crystal Structure Report Archive||1143|
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.cc2gx8c1
title: CCDC 2291283: Experimental Crystal Structure Determination

description: Related Article: Martin B. Smith, Simon J. Coles, Michael B. Hursthouse|2000|University of Southampton, Crystal Structure Report Archive||775|
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.cc2gx8g4
title: CCDC 2291286: Experimental Crystal Structure Determination

description: Related Article: Robert W. Lancaster, Peter N. Horton, Michael B. Hursthouse|2005|University of Southampton, Crystal Structure Report Archive||1136|
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.cc2gx8k7
title: CCDC 2291289: Experimental Crystal Structure Determination

description: Related Article: Martin B Smith, Simon J. Coles, Michael B. Hursthouse|2000|University of Southampton, Crystal Structure Report Archive||776|
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.cc2gx8l8
title: CCDC 2291290: Experimental Crystal Structure Determination

description: Related Article: Simon J. Lancaster, Simon J. Coles, Michael B. Hursthouse|2005|University of Southampton, Crystal Structure Report Archive||1138|
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.cc2gx98z
title: CCDC 2291311: Experimental Crystal Structure Determination

description: Related Article: Nick Greeves, Peter N. Horton, Michael B. Hursthouse|2005|University of Southampton, Crystal Structure Report Archive||1123|
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.cc2gx9c2
title: CCDC 2291314: Experimental Crystal Structure Determination

description: Related Article: Nick Greeves and Peter N. Horton and Michael B. Hursthouse|2005|University of Southampton, Crystal Structure Report Archive||1124|
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.24107433
title: <b>Assessing Awareness and Perception of Forest and Biodiversity Conservation Importance among Education and Faith-based Groups in Southeastern Nigeria</b>

description: The data provide information on the awareness of forest and biodiversity conservation importance among faith-based groups and educational institutions, specifically clergies, teachers and students. The numbering in the first row of the data corresponds with the numbering in the questionnaire.
DOI: 10.6084/m9.figshare.24107433.v1
title: <b>Assessing Awareness and Perception of Forest and Biodiversity Conservation Importance among Education and Faith-based Groups in Southeastern Nigeria</b>

description: The data provide information on the awareness of forest and biodiversity conservation importance among faith-based groups and educational institutions, specifically clergies, teachers and students. The numbering in the first row of the data corresponds with the numbering in the questionnaire.
DOI: 10.6084/m9.figshare.24107604
title: CARE CHECKLIST

description: CARE CHECKLIST for case report on Thanatophoric dysplasia.
DOI: 10.5066/p9ee4n0l
title: Eutrophication and plankton communities (Cyanobacteria and eukaryotic algae) in the Grand Calumet River Area of Concern, Indiana, 2021

description: The data being released were part of a project funded by the United States Environmental Protection Agency (USEPA). This study sought to identify the planktonic communities (cyanobacteria, eukaryotic algae) potentially contributing to eutrophication within the Grand Calumet River Area of Concern (AOC) in northern Indiana along the southern shore of Lake Michigan. In 2021, triplicate water samples were collected from five locations during three events, 4/19/21 and 4/20/21, 7/7/21, and 9/15/21. Water samples were processed and planktonic communities were determined by a DNA-based technology (algal metabarcoding). Sampling locations: 1. Columbia Avenue, Hammond, IN 2. Lake George drainage ditch, Hammond, IN 3. Indiana Harbor Canal at Canal Street in East Chicago, IN 4. Airport Road in Gary, IN 5. Marquette Park Lagoon in Gary, IN
DOI: 10.34740/kaggle/ds/3714772
title: Algiers Weather Data (2002-2023)
DOI: 10.5281/zenodo.8329371
title: Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics

description: <strong>Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics.</strong> Processed ACF level data can be provided by contacting me. <strong>DOI of the publication:</strong> <strong>README file:</strong> PFISRInversions_ASI_v1.1_Fang_01042023_v08162023.h5 HallConductance: Altitude integrated Hall Conductance from 85-150 km altitude, [ntime], mho<br> PedersenConductance: Altitude integrated Pedersen Conductance from 85-150 km, [ntime], mho<br> EnergyFlux: Energy flux after integrating the differential number flux, [ntime], W/m^2<br> AverageEnergy: Average Energy after integrating the differential number flux, [ntime], eV<br> Measured_ElectronDensity: measured electron density from PFISR, [ntime, naltitude], #/m^3<br> Modeled_ElectronDensity: modeled electron density produced by the MEM version, [ntime,naltitude], #/m^3<br> UnixTime: time in seconds since 1970-01-01 00:00:00 UT, [ntime], seconds<br> NumberFlux: differential number flux, [ntime, nenergy], #/m^2 s^-1 eV^-1<br> EnergyGrid: energy grid spanning 1 keV - 100 keV in 25 steps, [nenergy], eV<br> ASIStatus: auroral identification number, [ntime], no units<br> 1: Discrete aurora<br> 2: Diffuse aurora<br> 3: Pulsating aurora<br> 8: Unidentified aurora<br> -1: data that was flagged as unsuitable:<br> either TEC was too low (no auroral E-region possibly associated with red aurora),<br> the modeled electron density was not consistent at all with the observed electron density (bad fit) ASIYEAR-Final_date.xlsx<br> This is an excel file that contains the original auroral image identification near the zenith direction<br> We used all sky imager videos located: http://optics.gi.alaska.edu/realtime/data/MPEG/PKR_DASC_256/<br> These files were converted into python pickle files and used internally for the rest of the investigation. The columns corresponds to days, and the rows correspond to time in UT as decimal hours:<br> The ASI Key is the following:<br> # 1 Discrete<br> # 2 Diffuse<br> # 3 Pulsating<br> # 4 Cloudy/Clear<br> # 5 Moon<br> # 6 Possible faint Aurora with moon out<br> # 7 Substorm Breakup<br> # 8 Cloudy with aurora (can't make out type)
DOI: 10.5281/zenodo.8329370
title: Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics

description: <strong>Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics.</strong> Processed ACF level data can be provided by contacting me. <strong>DOI of the publication:</strong> <strong>README file:</strong> PFISRInversions_ASI_v1.1_Fang_01042023_v08162023.h5 HallConductance: Altitude integrated Hall Conductance from 85-150 km altitude, [ntime], mho<br> PedersenConductance: Altitude integrated Pedersen Conductance from 85-150 km, [ntime], mho<br> EnergyFlux: Energy flux after integrating the differential number flux, [ntime], W/m^2<br> AverageEnergy: Average Energy after integrating the differential number flux, [ntime], eV<br> Measured_ElectronDensity: measured electron density from PFISR, [ntime, naltitude], #/m^3<br> Modeled_ElectronDensity: modeled electron density produced by the MEM version, [ntime,naltitude], #/m^3<br> UnixTime: time in seconds since 1970-01-01 00:00:00 UT, [ntime], seconds<br> NumberFlux: differential number flux, [ntime, nenergy], #/m^2 s^-1 eV^-1<br> EnergyGrid: energy grid spanning 1 keV - 100 keV in 25 steps, [nenergy], eV<br> ASIStatus: auroral identification number, [ntime], no units<br> 1: Discrete aurora<br> 2: Diffuse aurora<br> 3: Pulsating aurora<br> 8: Unidentified aurora<br> -1: data that was flagged as unsuitable:<br> either TEC was too low (no auroral E-region possibly associated with red aurora),<br> the modeled electron density was not consistent at all with the observed electron density (bad fit) ASIYEAR-Final_date.xlsx<br> This is an excel file that contains the original auroral image identification near the zenith direction<br> We used all sky imager videos located: http://optics.gi.alaska.edu/realtime/data/MPEG/PKR_DASC_256/<br> These files were converted into python pickle files and used internally for the rest of the investigation. The columns corresponds to days, and the rows correspond to time in UT as decimal hours:<br> The ASI Key is the following:<br> # 1 Discrete<br> # 2 Diffuse<br> # 3 Pulsating<br> # 4 Cloudy/Clear<br> # 5 Moon<br> # 6 Possible faint Aurora with moon out<br> # 7 Substorm Breakup<br> # 8 Cloudy with aurora (can't make out type)
DOI: 10.6084/m9.figshare.18095984
title: Patients benefit from mentoring students in an interprofessional health mentors program: A contextual-developmental analysis

description: Mentorship programs in health professional education are often characterized as a mutually beneficial relationship between mentor and mentee, but little is known about benefits for mentors. Mentors can be health professionals, academic faculty, other students (peers), and patients (health mentors). We studied the benefits that health mentors (people with chronic health conditions or disabilities, or a caregiver) get from mentoring students, and the contextual factors that contribute to, or explain these benefits. We surveyed 72 health mentors who had mentored between one and eight cohorts of students from different health professions in the health mentors program at the University of British Columbia. Using a contextual-developmental framework of mentorship, we analyzed mentors’ responses to open-ended questions about how they benefit from the program. Benefits fit into three categories: generativity (guiding the next generation), transformation (personal growth and reflection), and ‘career’ development (new activities resulting from increased self-efficacy). Contextual factors that contributed to benefits included the non-clinical setting, informality of meetings and reciprocal learning, and feeling valued by the program and students. Health mentors perceive benefits in passing on their lived experiences to students, leading to personal growth and new activities. Their perspectives offer unique insights into the workings of effective mentorship relationships. There is much to be learned about how benefits of mentoring are linked to program design.
DOI: 10.6084/m9.figshare.18095984.v1
title: Patients benefit from mentoring students in an interprofessional health mentors program: A contextual-developmental analysis

description: Mentorship programs in health professional education are often characterized as a mutually beneficial relationship between mentor and mentee, but little is known about benefits for mentors. Mentors can be health professionals, academic faculty, other students (peers), and patients (health mentors). We studied the benefits that health mentors (people with chronic health conditions or disabilities, or a caregiver) get from mentoring students, and the contextual factors that contribute to, or explain these benefits. We surveyed 72 health mentors who had mentored between one and eight cohorts of students from different health professions in the health mentors program at the University of British Columbia. Using a contextual-developmental framework of mentorship, we analyzed mentors’ responses to open-ended questions about how they benefit from the program. Benefits fit into three categories: generativity (guiding the next generation), transformation (personal growth and reflection), and ‘career’ development (new activities resulting from increased self-efficacy). Contextual factors that contributed to benefits included the non-clinical setting, informality of meetings and reciprocal learning, and feeling valued by the program and students. Health mentors perceive benefits in passing on their lived experiences to students, leading to personal growth and new activities. Their perspectives offer unique insights into the workings of effective mentorship relationships. There is much to be learned about how benefits of mentoring are linked to program design.
DOI: 10.6084/m9.figshare.19229100
title: Implementation of competence committees during the transition to CBME in Canada: A national fidelity-focused evaluation

description: This study evaluated the fidelity of competence committee (CC) implementation in Canadian postgraduate specialist training programs during the transition to competency-based medical education (CBME). A national survey of CC chairs was distributed to all CBME training programs in November 2019. Survey questions were derived from guiding documents published by the Royal College of Physicians and Surgeons of Canada reflecting intended processes and design. Response rate was 39% (113/293) with representation from all eligible disciplines. Committee size ranged from 3 to 20 members, 42% of programs included external members, and 20% included a resident representative. Most programs (72%) reported that a primary review and synthesis of resident assessment data occurs prior to the meeting, with some data reviewed collectively during meetings. When determining entrustable professional activity (EPA) achievement, most programs followed the national specialty guidelines closely with some exceptions (53%). Documented concerns about professionalism, EPA narrative comments, and EPA entrustment scores were most highly weighted when determining resident progress decisions. Heterogeneity in CC implementation likely reflects local adaptations, but may also explain some of the variable challenges faced by programs during the transition to CBME. Our results offer educational leaders important fidelity data that can help inform the larger evaluation and transformation of CBME.
DOI: 10.6084/m9.figshare.19229100.v1
title: Implementation of competence committees during the transition to CBME in Canada: A national fidelity-focused evaluation

description: This study evaluated the fidelity of competence committee (CC) implementation in Canadian postgraduate specialist training programs during the transition to competency-based medical education (CBME). A national survey of CC chairs was distributed to all CBME training programs in November 2019. Survey questions were derived from guiding documents published by the Royal College of Physicians and Surgeons of Canada reflecting intended processes and design. Response rate was 39% (113/293) with representation from all eligible disciplines. Committee size ranged from 3 to 20 members, 42% of programs included external members, and 20% included a resident representative. Most programs (72%) reported that a primary review and synthesis of resident assessment data occurs prior to the meeting, with some data reviewed collectively during meetings. When determining entrustable professional activity (EPA) achievement, most programs followed the national specialty guidelines closely with some exceptions (53%). Documented concerns about professionalism, EPA narrative comments, and EPA entrustment scores were most highly weighted when determining resident progress decisions. Heterogeneity in CC implementation likely reflects local adaptations, but may also explain some of the variable challenges faced by programs during the transition to CBME. Our results offer educational leaders important fidelity data that can help inform the larger evaluation and transformation of CBME.
DOI: 10.6084/m9.figshare.19352227
title: Large-scale computational content analysis on magazines targeting men and women: the case of Argentina 2008-2018

description: Differences in magazines content aimed specifically at women or men are a means to create and reproduce gender stereotypes. Novel computational tools allow to study differences in magazines content taking into account all available articles. In this study, we analyse the case of two Argentinian magazines published by the same publishing house over a decade (2008–2018), advertised by the publishing house as targeting women and men respectively. Using computational tools, we are able to analyse more than 24,000 articles, which would have been an impossible task using manual content analysis methodologies. With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify their different frequency between magazines over time. Then, we performed a word-frequency analysis to validate this methodology and extend the analysis to other subjects. Our results show that topics such as <i>Family, Business</i> and <i>Women as sex objects</i> present an initial bias that tends to disappear over time. Conversely, in <i>Fashion</i> and <i>Science</i> topics, the initial differences are maintained. Also, we identify a considerable increase in the use of words associated with feminism since 2015 and specifically the word <i>abortion</i> in 2018. Furthermore, we develop a website where everyone can perform additional analysis.