cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/cu.the.2009.2102
title: An analysis of Diploma of Public Health Program (Technical Pharmacy) based on professional competence

description: To analyze the Diploma of Public Health Program (Technical Pharmacy) based on professional competence and to measure competence level of the curriculum. This was a descriptive study using documentary analysis. The behavioral objectives and credit hours were data collected from the syllabi of the curriculum. The instruments for data collection were 2-dimensional and 4-dimensional matrices. The first matrix was used to map between the behavioral objectives and content topic, while the 4-dimensional matrix mapped objectives with pharmacy technician competence, professional competence, and level of competence. The analysis revealed that the core requirement consisted of 31 subjects with a total of 80 credit hours. All content topics corresponded with 220 objective items classified into 3 teaching methods, lecture, practice, and trainee accounted for 62.5%, 25.0%, and 12.5% of required credit hours respectively. Fifteen subjects under technical pharmacy area with a total of 38 credit hours could be divided into 15.76 credit hours of pharmacy service, 9.62 of public health pharmacy, 9.54 of pharmaceutical production, and 3.08 of pharmaceutical inventory management. The curriculum contained all 5 professional competence domains with 75% of the required credit hours focusing on knowledge/cognitive (32.24 credit hours) and functional competence (27.05 credit hours) but minimum emphasis on both personal/behavioral and values/ethical competences. The assessment on competence level reflected that the curriculum put more weights on “knows how” level with 36.58 credit hours. The study concluded that the Diploma of Public Health Program (Technical Pharmacy) possessed all dimensions of competences, however the share of each element needed further justification.
DOI: 10.14457/cu.the.2009.2103
title: Growth, labour productivity and ICT investment in Thailand

description: Many studies have done on the impact of information and communication technology (ICT) on growth and productivity that they treated ICT capital stock as an individual input in the production process that contributes to productivity and growth. Although ICT has found to be one of the key determinants of productivity in the developed world, few studies have also found out the positive impacts of ICT on the developing world. However, there are still lacks of empirical works in the field that differentiate clearly with the literature which study background and descriptive studies of Thailand’s ICT development. This study attempts to examine the role of ICT in Thailand’s economy in the past decades. The study develops the framework by considering ICT as a production factor and applies it to interpret Thailand’s economic growth. The study contributes to the empirical literature on ICT and economic growth in Thailand. First, it attempts to estimate the ICT capital stock in Thailand by applying the perpetual inventory method. Second, based on such estimates, the study explore the contribution of ICT to Thailand’s economic growth by using growth accounting method that segregates ICT from all others forms of capital. According to study, ICT capital contributes positively on the Thailand economic growth and labour productivity. Average labour productivity growth rate of Thailand is 3.83% during 1991 to 2005. In terms of growth accounting analysis, the study found that, ICT capital contributes 6%, non-ICT capital contributes 41%, human capital contributes 7% and TFP contributes 46% respectively to average growth. In other word, average labour productivity has been positively affected by ICT development which comes from increase in ICT skills and ICT intensity such as PC per user and internet per user. Policy recommendation from our study are as follows: (1) increase ICT utilization in Thailand (2) increase the number of ICT professionals in Thailand (3) increase level of ICT education of the labour force in order to strengthen further labour productivity growth and last but not least (4) reduce digital divide between rural and urban of Thailand.
DOI: 10.17632/tnhv83ty9f.1
title: Data for calculating age-adjusted standardized mortality rate of AMI and SMR of AMI in Fukushima Prefecture.

description: We uploaded four excel files; “Population_2019.02.25.xlsx”, “AMI_2019.02.25.xlsx”, “Fukushima_pop_2019.02.25.xlsx”, and “Fukushima_AMI_2019.02.25.xlsx”. Each file is explained in detail below. I. “Population_2019.02.25.xlsx” is composed of five sheets. i. Model population: The Japanese model population in 1985, reported by age group. ii. Japan: Population in Japan, from 2008 to 2016, reported by age group. iii. Iwate: Population in Iwate Prefecture, from 2008 to 2016, reported by age group. iv. Miyagi: Population in Miyagi Prefecture, from 2008 to 2016, reported by age group. v. Fukushima: Population in Fukushima Prefecture, from 2008 to 2016, reported by age group. II. “AMI_2019.02.25.xlsx” is composed of four sheets. i. Japan: The number of death due to AMI in Japan, from 2008 to 2016, reported by age group. ii. Iwate: The number of death due to AMI in Iwate Prefecture, from 2008 to 2016, reported by age group. iii. Miyagi: The number of death due to AMI in Miyagi Prefecture, from 2008 to 2016, reported by age group. iv. Fukushima: The number of death due to AMI in Fukushima Prefecture, from 2008 to 2016, reported by age group. III. “Fukushima_pop_2019.02.25.xlsx” is composed of six sheets. These sheets report the population in each district in Fukushima Prefecture, by age group and the ID codes to the districts. This excel file is divided into six sheets by the years from 2009 to 2016, one by one. IV. “Fukushima_AMI_2019.02.25.xlsx” is composed of one sheet. The sheet, named by Fukushima, reports the number of death due to AMI in each district in Fukushima Prefecture, by the years from 2009 to 2014 and the ID codes to the districts. 1) Fukushima Prefecture is represented by ID: 7000. 2) The evacuation area is composed of six districts (ID: 7543, 7545, 7546, 7547, 7548, 7564). 3) The eastern area is composed of seven districts (ID: 7204, 7209, 7212, 7541, 7542, 7544, 7561). 4) The middle area is composed of 29 districts (ID: 7201, 7203, 7205, 7207, 7210, 7211, 7213, 7214, 7301, 7303, 7308, 7322, 7342, 7344, 7461, 7464, 7465, 7466, 7481, 7482, 7483, 7484, 7501, 7502, 7503, 7504, 7505, 7521, 7522). 5) The western area is composed of 17 districts (ID: 7202, 7208, 7362, 7364, 7367, 7368, 7402, 7405, 7407, 7408, 7421, 7422, 7423, 7444, 7445, 7446, 7447).
DOI: 10.17632/vfwgmp5pnz.1
title: Data for: Effect of community-based lifestyle interventions on weight loss and cardiometabolic risk factors in obese elderly in China: a randomized controlled trial
DOI: 10.21227/h70d-8z17
title: ONERA.ROOM

description: We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM.It comes with RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files) for People detection.Different sets are made available (Easy, Average, Hard, Search’n’Rescue, dark conditions, sunlight interference, etc…) to allow the community to test their algorithms on these challenging data. If you use ONERA.ROOM, please consider citing:@inproceedings{guerry2017lato, title={{"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}}, author={Guerry, Joris and {Le Saux}, Bertrand and Filliat, David}, booktitle={Proc. Eur. Conf. on Mobile Robotics (ECMR)}, year={2017}} @inproceedings{guerry2017lato, title={"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}, author={Guerry, Joris and Le Saux, Bertrand and Filliat, David}, booktitle={to appear}, year={2017}
DOI: 10.5281/zenodo.3518039
title: In Vivo Contactless Brain Nanothermometry

description: Dataset accompanying figures published in the publication https://zenodo.org/record/3552591
description: This project has received funding from the European Union's Horizon 2020 FET Open programme under grant agreement No 801305 (NanoTBtech)
DOI: 10.5281/zenodo.3518040
title: In Vivo Contactless Brain Nanothermometry

description: Dataset accompanying figures published in the publication https://zenodo.org/record/2571555
DOI: 10.21227/vn1v-yd15
title: ONERA.ROOM

description: We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM.It comes with RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files) for People detection.Different sets are made available (Easy, Average, Hard, Search’n’Rescue, dark conditions, sunlight interference, etc…) to allow the community to test their algorithms on these challenging data. If you use ONERA.ROOM, please consider citing:@inproceedings{guerry2017lato, title={{"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}}, author={Guerry, Joris and {Le Saux}, Bertrand and Filliat, David}, booktitle={Proc. Eur. Conf. on Mobile Robotics (ECMR)}, year={2017}} @inproceedings{guerry2017lato, title={"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}, author={Guerry, Joris and Le Saux, Bertrand and Filliat, David}, booktitle={to appear}, year={2017}
DOI: 10.5281/zenodo.3517854
title: Dataset related to article "Phase II trial on SBRT for unresectable liver metastases long-term outcome and prognostic factors of survival after 5 years of follow-up"

description: This record contains raw data related to article "Phase II trial on SBRT for unresectable liver metastases long-term outcome and prognostic factors of survival after 5 years of follow-up" BACKGROUND: The aim of this study was to evaluate long-term efficacy and survival prognostic factors of stereotactic body radiation therapy (SBRT) for un-resectable liver metastases in patients enrolled in a prospective phase II trial. METHODS AND MATERIALS: 5-year local control (LC), overall survival (OS), progression free survival (PFS) and toxicity rates were analyzed in patients with un-resectable liver metastases enrolled in a Phase II Trial on liver SBRT, with a prescription dose of 75Gy in 3 consecutive fractions. RESULTS: A total of 61 patients with 76 lesions were enrolled, with a median follow-up time of 6.1 years. One, three and 5 year LC rates were 94 ± 3.1%, 78.0 ± 5.9% and 78.0 ± 5.9%, without reaching the median LC time. Median OS was 27.6 months and the survival rates were 85.2 ± 4.5%, 31.1 ± 5.9% and 18.0 ± 4.9% at 1, 3 and 5-year after SBRT, respectively. Univariate analysis showed that favorable primary site (colorectal, breast and gynecological) of metastases (p = 0.001) improved survival. Toxicity was moderate. One patient experienced G3 late chest wall pain, which resolved within 1 year from SBRT. No cases of Radiation Induced Liver Disease (RILD) were detected. CONCLUSIONS: Long-term results of this Phase II study suggest the efficacy and safety of SBRT for un-resectable liver metastases after 5-year of follow up. Selection of cases with positive prognostic factors may improve long-term survival of these oligo-metastastic patients and may confirm the role of SBRT as an effective alternative local therapy for liver metastases.
DOI: 10.21227/vv0p-ty26
title: ONERA.ROOM

description: We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM. It comprises RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files)It aims at People detection, in (mostly) indoor and outdoor environments. People in the field of view can be standing, but also lying on the ground as after a fall.Different sets are made available (Easy, Average, Hard, Search’n’Rescue, dark conditions, sunlight interference, etc…) to allow the community to test their RGBD algorithms on these challenging data. Indeed, transitions from indoor to outdoor exist in the dataset, which requires to use both modalities to detect people.If you use ONERA.ROOM, please consider citing: @inproceedings{guerry2017lato, title={{"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}}, author={Guerry, Joris and {Le Saux}, Bertrand and Filliat, David}, booktitle={Proc. Eur. Conf. on Mobile Robotics (ECMR)}, year={2017} } ONERA.ROOM of Joris GUERRY and Bertrand LE SAUX is available under BY-NC-SA 4.0 license.
DOI: 10.21227/xswd-9x23
title: ONERA.ROOM

description: We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM. It comprises RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files)It aims at People detection, in (mostly) indoor and outdoor environments. People in the field of view can be standing, but also lying on the ground as after a fall.Different sets are made available (Easy, Average, Hard, Search’n’Rescue, dark conditions, sunlight interference, etc…) to allow the community to test their RGBD algorithms on these challenging data. Indeed, transitions from indoor to outdoor exist in the dataset, which requires to use both modalities to detect people.If you use ONERA.ROOM, please consider citing: @inproceedings{guerry2017lato, title={{"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}}, author={Guerry, Joris and {Le Saux}, Bertrand and Filliat, David}, booktitle={Proc. Eur. Conf. on Mobile Robotics (ECMR)}, year={2017} } ONERA.ROOM of Joris GUERRY and Bertrand LE SAUX is available under BY-NC-SA 4.0 license.
DOI: 10.21227/kdgk-v119
title: ONERA.ROOM

description: We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM. It comprises RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files)It aims at People detection, in (mostly) indoor and outdoor environments. People in the field of view can be standing, but also lying on the ground as after a fall.Different sets are made available (Easy, Average, Hard, Search’n’Rescue, dark conditions, sunlight interference, etc…) to allow the community to test their RGBD algorithms on these challenging data. Indeed, transitions from indoor to outdoor exist in the dataset, which requires to use both modalities to detect people.If you use ONERA.ROOM, please consider citing: @inproceedings{guerry2017lato, title={{"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}}, author={Guerry, Joris and {Le Saux}, Bertrand and Filliat, David}, booktitle={Proc. Eur. Conf. on Mobile Robotics (ECMR)}, year={2017} } ONERA.ROOM of Joris GUERRY and Bertrand LE SAUX is available under BY-NC-SA 4.0 license.
DOI: 10.21227/rrnn-tj36
title: New figure from the simulation of the odometric model

description: The odometric model is simulated herein. We described the trajectory of such one odometric model, with the delta of the heading angle given as one parameter of the simulation. The iterations show that the trajectory is well in the continuity of the variations of the heading angle. Moreover the distance in X and in Y are shown for the vehicle to be driven in the trajectory of the odometric model.
DOI: 10.7488/6fee4875-894f-4613-a861-59d5e46d8598
title: Becoming Animal film

description: Audiovisual file for the film Becoming Animal.
description: An inspired collaboration between filmmakers Emma Davie (I Am Breathing) and Peter Mettler (The End of Time) and radical writer and philosopher David Abram (The Spell of the Sensuous), Becoming Animal is an urgent and immersive audiovisual quest, forging a path into the places where humans and other animals meet, where we pry open our senses to witness the so-called natural world—which in turn witnesses us, prompting us to reflect on the very essence of what it means to inhabit our animal bodies. Shot in and around Grand Teton National Park, with its dizzying diversity of wildlife, trails of curious humans in RV’s and billion-year-old geology, the film is a geyser of provocative ideas and heightened sensations related to the sublime circuitry that connects us to our ever-shifting surroundings. A snail’s body becomes an immense landscape as the soundscape immerses us in shivering leaves, rushing rivers and the weird spacey pitch of elk bugling at night. Becoming Animal embraces the sensory tools of cinema to trace how the written word and technology has affected how we see. Driven by wonder, curiosity and a desire for balance between ecological and technological imperatives, Becoming Animal is an invitation to explore our relationship with this “more than human world” and recognise it for what it is: an exquisitely intricate system in which everything is alive and expressive, humans, animals and landscapes are inextricably interdependent, and there is no such thing as empty space. Countries: Switzerland / UK Year: 2018 Duration: 78min Format: DCP / BluRay Language: English Production Companies: Maximage / SDI Productions Ltd ## Access ## This dataset is held in the Edinburgh DataVault, directly accessible only to authorised University of Edinburgh users. External users may request access to a copy of the data by contacting the Principal Investigator, Contact Person or Data Manager named on this page. University of Edinburgh users who wish to have direct access should consult the information about retrieving data from the DataVault at: http://www.ed.ac.uk/is/research-support/datavault .
description: Davie, Emma; Mettler, Peter "Becoming Animal film" (2019) Edinburgh DataVault
DOI: 10.21227/sphw-8z29
title: ONERA.ROOM

description: We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM. It comprises RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files)It aims at People detection, in (mostly) indoor and outdoor environments. People in the field of view can be standing, but also lying on the ground as after a fall.Different sets are made available (Easy, Average, Hard, Search’n’Rescue, dark conditions, sunlight interference, etc…) to allow the community to test their RGBD algorithms on these challenging data. Indeed, transitions from indoor to outdoor exist in the dataset, which requires to use both modalities to detect people.If you use ONERA.ROOM, please consider citing: @inproceedings{guerry2017lato, title={{"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}}, author={Guerry, Joris and {Le Saux}, Bertrand and Filliat, David}, booktitle={Proc. Eur. Conf. on Mobile Robotics (ECMR)}, year={2017} } ONERA.ROOM of Joris GUERRY and Bertrand LE SAUX is available under BY-NC-SA 4.0 license.
DOI: 10.5281/zenodo.3518182
title: Flood Masks Doñana 1984/2019

description: Time Series of flooded areas derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, these products and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/ Methodology is described in this paper: Remote Sensing 8(9):775 · September 2016. DOI: 10.3390/rs8090775
DOI: 10.5281/zenodo.3518181
title: Flood Masks Doñana 1984/2019

description: Time Series of flooded areas derived from Landsat TM, ETM+ & OLI in the Path 202 Row 34 (Doñana). Also, these products and its metadata are freely available to consult or downloaded in the LAST-EBD Cartography Server: http://mercurio.ebd.csic.es/imgs/ Methodology is described in this paper: Remote Sensing 8(9):775 · September 2016. DOI: 10.3390/rs8090775
DOI: 10.15129/06540872-c30f-45c8-93fc-a2bfd524e944
title: Data for: "Desiccation behaviour of colloidal silica grouted sand: A new material for the creation and report of near surface hydraulic barriers"

description: This dataset refer to the data published in "A new material for the creation and report of near surface hydraulic barriers". Data corresponding to figure 9, figure 13, figure 15, figure 16 and figure 17. For X-ray tomography data please contact the author as data size is 262 GB.
DOI: 10.21227/vqkf-7195
title: ONERA.ROOM

description: We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM. It comprises RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files)It aims at People detection, in (mostly) indoor and outdoor environments. People in the field of view can be standing, but also lying on the ground as after a fall.Different sets are made available (Easy, Average, Hard, Search’n’Rescue, dark conditions, sunlight interference, etc…) to allow the community to test their RGBD algorithms on these challenging data. Indeed, transitions from indoor to outdoor exist in the dataset, which requires to use both modalities to detect people.If you use ONERA.ROOM, please consider citing: @inproceedings{guerry2017lato, title={{"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}}, author={Guerry, Joris and {Le Saux}, Bertrand and Filliat, David}, booktitle={Proc. Eur. Conf. on Mobile Robotics (ECMR)}, year={2017} } ONERA.ROOM of Joris GUERRY and Bertrand LE SAUX is available under BY-NC-SA 4.0 license.
DOI: 10.5281/zenodo.3250138
title: Data set related to the manuscript "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors"

description: Graphical files in the agr format for all the figures in the main text of the manuscript entitled "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors" (10.1016/j.electacta.2019.135022).
DOI: 10.5281/zenodo.3250139
title: Data set related to the manuscript "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors"

description: Graphical files in the agr format for all the figures in the main text of the manuscript entitled "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors" (10.1016/j.electacta.2019.135022).
DOI: 10.21227/8t56-1p75
title: ONERA.ROOM

description: We introduce a new robotic RGBD dataset with difficult luminosity conditions: ONERA.ROOM. It comprises RGB-D data (as pairs of images) and corresponding annotations in PASCAL VOC format (xml files)It aims at People detection, in (mostly) indoor and outdoor environments. People in the field of view can be standing, but also lying on the ground as after a fall.Different sets are made available (Easy, Average, Hard, Search’n’Rescue, dark conditions, sunlight interference, etc…) to allow the community to test their RGBD algorithms on these challenging data. Indeed, transitions from indoor to outdoor exist in the dataset, which requires to use both modalities to detect people.If you use ONERA.ROOM, please consider citing: @inproceedings{guerry2017lato, title={{"Look At This One" Detection sharing between modality-independent classifiers for robotic people discovery}}, author={Guerry, Joris and {Le Saux}, Bertrand and Filliat, David}, booktitle={Proc. Eur. Conf. on Mobile Robotics (ECMR)}, year={2017} } ONERA.ROOM of Joris GUERRY and Bertrand LE SAUX is available under BY-NC-SA 4.0 license.
DOI: 10.21942/uva.10033298
title: Raw Data - Experiment 1 and Experiment 2

description: Raw data collected in Experiment 1 and Experiment 2.