cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5255/ukda-sn-856656
title: Interviews With Older Individuals Living With Acquired Brain Injury and Undergoing Physical Rehabilitation in the Region of Epirus, Northern Greece, 2021

description: The increasing ageing population and the profound life-changing consequences of acquired brain injury (ABI) make it critical to reorient the focus of care and rehabilitation for older adults towards promoting their potential for a fulfilling and prosperous life. Consequently, there is a growing need to investigate well-being qualitatively, especially in the context of enhancing rehabilitation systems. This necessitates current research to give more attention to older adults’ experiences of well-being and satisfaction levels concerning their care during ABI rehabilitation. Aim: To acquire an in-depth understanding of the lived experiences of older individuals (+65) living with acquired brain injury when undergoing physical rehabilitation in relation to their sense of well-being during care provision within the Greek Healthcare System. To provide an interpretive exposition illustrating the impact that Greece’s healthcare and rehabilitation system has on older individuals’ felt sense of well-being. Design: A qualitative study design with a hermeneutic phenomenological approach was developed to provide a comprehensive exploration and interpretation of the well-being subjective experiences of older individuals living with acquired brain injury, undergoing physical rehabilitation. Method: A total of 14 semi-structured interviews with older individuals living with acquired brain injury were purposively conducted. A thematic analysis of the interviews’ transcriptions was carried out within the perspective of a hermeneutic phenomenological paradigm, following van Manen’s and Clarke and Braun's methods. Findings: Four themes emerged from the analysis: 1) Challenges of new life situation, 2) Seeking emotional and practical support through social interaction, 3) Identifying contextual processes of rehabilitation, 4) The felt sense of self in the aftermath of ABI. The different subjective experiences, intersubjective relations and contextual clinical conditions influence and determine older individuals’ sense of well-being and by extension the realisation of the new changes in their life, leading to various well-being or suffering possibilities. Conclusions: The exploration of the interrelation between these dimensions is crucial to understand further older individuals’ needs regarding their rehabilitation as well as for xii the enhancement of their mental and physical state. A deep and comprehensive knowledge of the factors impacting older individuals’ felt sense of well-being during rehabilitation can contribute to the provision of more dignified and humanising health care.
DOI: 10.48420/24065550
title: Regional capital investment deflators, UK, by industry and asset, 1997 to 2019

description: This data set provides deflators (price indices) for UK regional capital investment (Gross Fixed Capital Formation, GFCF) published by the UK Office for National Statistics (ONS) in May 2022. Deflators are available by region, asset type and industry, consistent with the breakdowns in the ONS published dataset. The geographic breakdown is defined by the International Territorial Levels 1 and 2 (ITL1 and ITL2).The data set is structured long by region, asset and industry. Separate data sets are provided for ITL1 and ITL2. The ITL2 data set contains the associated ITL1 region for each ITL2 region. The industries are sections (letter-level) of the UK Standard Industrial Classification (SIC) 2007. The associated current price investment data (i.e. before adjusting for price changes) are provided in the data set, which are attributable to ONS. More information on the sources and methods for this data set can be obtained from Annex A in the following publication:M. Becker, J. Martin (2023) <i>New insights on regional capital investment in the UK, 1997 to 2019</i>. Productivity Insights Paper No.016, The Productivity Institute.
DOI: 10.48420/24065550.v1
title: Regional capital investment deflators, UK, by industry and asset, 1997 to 2019

description: This data set provides deflators (price indices) for UK regional capital investment (Gross Fixed Capital Formation, GFCF) published by the UK Office for National Statistics (ONS) in May 2022. Deflators are available by region, asset type and industry, consistent with the breakdowns in the ONS published dataset. The geographic breakdown is defined by the International Territorial Levels 1 and 2 (ITL1 and ITL2).The data set is structured long by region, asset and industry. Separate data sets are provided for ITL1 and ITL2. The ITL2 data set contains the associated ITL1 region for each ITL2 region. The industries are sections (letter-level) of the UK Standard Industrial Classification (SIC) 2007. The associated current price investment data (i.e. before adjusting for price changes) are provided in the data set, which are attributable to ONS. More information on the sources and methods for this data set can be obtained from Annex A in the following publication:M. Becker, J. Martin (2023) <i>New insights on regional capital investment in the UK, 1997 to 2019</i>. Productivity Insights Paper No.016, The Productivity Institute.
DOI: 10.5281/zenodo.6466472
title: eSRRF - Supplementary Data

description: <strong>eSRRF supplementary data</strong> The eSRRF preprint is available here: https://doi.org/10.1101/2022.04.07.487490 Find the code and documentation here: https://github.com/HenriquesLab/NanoJ-eSRRF <strong>1a: Set01a_DNA-PAINT_COS7-MT.tif</strong> DNA-PAINT of fixed COS-7 cells with indirect immunolabeling against tubulin Pixelsize: 160 nm Exposure time: 30 ms 50 000 frames <strong>1b: Set01b_DNA-PAINT_COS7-MT_bin100.tif</strong> Dataset 1a with 100 frames binning to effectivly increase emitter density. eSRRF analysis parameters: M=5, R=0.5, S=1 all frames <strong>2: Set02_HeLa_ffDronpa-MAP4.tif</strong> Live-cell TIRF imaging dataset of HeLa cells expressing ffDronpa-MAP4 Pixelsize: 156 nm Exposure time: 40 ms eSRRF analysis parameters: M=4 R=2 S=4 100 frames rolling analysis gap = 50 frames <strong>3: Set03_COS7_PrSS-mEmerald-KDEL.tif</strong> Live-cell HiLO-TIRF of COS-7 cells expressing PrSS-mEmerald-KDEL marking the ER lumen. Pixelsize: 160 nm Exposure time: 5 ms eSRRF analysis parameters: M=5 R=2 S=1 100 fr rolling analysis gap = 10 frames <strong>4a: Set4a_MFM_HeLa_TOM20.tif</strong> Live-cell MFM of HeLa cells expressing TOM20-HaloTag labeled with JF549 Pixelsize: 160 nm Exposure time: 20 ms eSRRF analysis parameters: M=4 R=2 S=1 100 fr rolling analysis gap = 25 frames 3D axial offset = 390 nm <strong>4b: Set4b_MFM_beadcalibration.tif</strong> MFM calibration data with TetraSpeck beads immobilized on a coverslip, Pixelsize: 160 nm z-step 60 nm <strong>5: Set5_LLS_Jurkat_ER.zip</strong> Lattice light sheet (LLS) data of the ER in Jurkat cells. The zip-folder contains single image stacks for each z-plane: 98 z-planes with 100 fr per plane at 10 ms exposure time 276 nm step size in the z-axis and 104 nm pixelsize. Each z-plane stack was process with eSRRF followed by deskewing with the LLSM plugin (https://monash-merc.github.io/llsm/docs/fiji/index.html) to recover a single 3D volume reconstruction. eSRRF analysis parameters: M=5 R=3.5 S=2 100 frames <strong>6: Set6_TIRF_NeuronCulture_MT_Skylan-NS.tif</strong> TIRF imaging data of cultured neuron expressing Skylan-NS tagged tubulin. Pixelsize: 64.7 nm Exposure time: 100 ms eSRRF analysis parameters: M=4 R=5 S=2 200 fr <strong>7: Set7_SDC_U2OS_Skylan-S-Actin.tif</strong> Live-cell SDC imaging data of U2OS cells transiently expressing SkylanS-ßActin over a time course of 12 hours by acquiring substacks of 50 frames at 10 min intervalls. Pixelsize: 247.6 nm Exposure time: 500ms eSRRF analysis parameters: M=5 R=2.5 S=1 50 frames
DOI: 10.5281/zenodo.8325164
title: eSRRF - Supplementary Data

description: <strong>eSRRF supplementary data</strong> The eSRRF preprint is available here: https://doi.org/10.1101/2022.04.07.487490 Find the code and documentation here: https://github.com/HenriquesLab/NanoJ-eSRRF <strong>1a: Set01a_DNA-PAINT_COS7-MT.tif</strong> DNA-PAINT of fixed COS-7 cells with indirect immunolabeling against tubulin Pixelsize: 160 nm Exposure time: 30 ms 50 000 frames <strong>1b: Set01b_DNA-PAINT_COS7-MT_bin100.tif</strong> Dataset 1a with 100 frames binning to effectivly increase emitter density. eSRRF analysis parameters: M=5, R=0.5, S=1 all frames <strong>2: Set02_HeLa_ffDronpa-MAP4.tif</strong> Live-cell TIRF imaging dataset of HeLa cells expressing ffDronpa-MAP4 Pixelsize: 156 nm Exposure time: 40 ms eSRRF analysis parameters: M=4 R=2 S=4 100 frames rolling analysis gap = 50 frames <strong>3: Set03_COS7_PrSS-mEmerald-KDEL.tif</strong> Live-cell HiLO-TIRF of COS-7 cells expressing PrSS-mEmerald-KDEL marking the ER lumen. Pixelsize: 160 nm Exposure time: 5 ms eSRRF analysis parameters: M=5 R=2 S=1 100 fr rolling analysis gap = 10 frames <strong>4a: Set4a_MFM_HeLa_TOM20.tif</strong> Live-cell MFM of HeLa cells expressing TOM20-HaloTag labeled with JF549 Pixelsize: 160 nm Exposure time: 20 ms eSRRF analysis parameters: M=4 R=2 S=1 100 fr rolling analysis gap = 25 frames 3D axial offset = 390 nm <strong>4b: Set4b_MFM_beadcalibration.tif</strong> MFM calibration data with TetraSpeck beads immobilized on a coverslip, Pixelsize: 160 nm z-step 60 nm <strong>5: Set5_LLS_Jurkat_ER.zip</strong> Lattice light sheet (LLS) data of the ER in Jurkat cells. The zip-folder contains single image stacks for each z-plane: 98 z-planes with 100 fr per plane at 10 ms exposure time 276 nm step size in the z-axis and 104 nm pixelsize. Each z-plane stack was process with eSRRF followed by deskewing with the LLSM plugin (https://monash-merc.github.io/llsm/docs/fiji/index.html) to recover a single 3D volume reconstruction. eSRRF analysis parameters: M=5 R=3.5 S=2 100 frames <strong>6: Set6_TIRF_NeuronCulture_MT_Skylan-NS.tif</strong> TIRF imaging data of cultured neuron expressing Skylan-NS tagged tubulin. Pixelsize: 64.7 nm Exposure time: 100 ms eSRRF analysis parameters: M=4 R=5 S=2 200 fr <strong>7: Set7_SDC_U2OS_Skylan-S-Actin.tif</strong> Live-cell SDC imaging data of U2OS cells transiently expressing SkylanS-ßActin over a time course of 12 hours by acquiring substacks of 50 frames at 10 min intervalls. Pixelsize: 247.6 nm Exposure time: 500ms eSRRF analysis parameters: M=5 R=2.5 S=1 50 frames
DOI: 10.48328/tudatalib-1177
title: Data of the publication "Dynamical Coarse-grained Models of Molecular Liquids and their Ideal and Non-ideal Mixtures"

description: Input files and memory kernels for atomistic and coarse-grained simulations. Data files for structural and dynamical properties.
DOI: 10.5281/zenodo.8160777
title: Test

description: _
DOI: 10.5281/zenodo.8160776
title: Test

description: _
DOI: 10.5255/ukda-sn-856641
title: Company, Group, Patent and Inventor Data for Pharmaceuticals, 1988-2015

description: The data include information on (1) Pharmaceutical companies; (2) Pharma Group each of these companies belong to; (3) Patent application to the European Patent Office by pharmaceutical company (assignee); (4) Name of inventors (i.e. employees of a pharmaceutical company) that appear on the patent. The data include unique identifiers that can be used to link a pharma company to a pharma group and to link a pharma company to the patent assignee (i.e. the legal entity that applies for the patent). These data are particularly useful to track the employment history of inventors following a M&A. For instance, Schering-Plough was acquired by Merck and Co in 2009. So if a scientist working for Shering-Plough before 2009 appears after the acquisition on a patent owned by Merck, we can infer that this employee has not changed company (i.e. she keeps working for the company that bought the company she was working for).
DOI: 10.34740/kaggle/dsv/6431352
title: MoviesDatasets
DOI: 10.34740/kaggle/dsv/6431358
title: Mahanagar Yatayat Transport Dataset
DOI: 10.5281/zenodo.8326223
title: MOOC Post test Data

description: Co authors from across institutes in Pakistan
DOI: 10.5281/zenodo.8326222
title: MOOC Post test Data

description: Co authors from across institutes in Pakistan
DOI: 10.7910/dvn/xgwwou
title: Replication Data for: Fetal MRI data for E2MIP_Challenge

description: Fetal MRI data for E2MIP_Challenge
DOI: 10.5281/zenodo.8326436
title: ABA quantification data from 10.3389/fpls.2023.1251442

description: Raw data from ABA quantification by HPLC MS-MS from the manuscript "Biostimulant activity of Galaxaura rugosa seaweed extracts against water deficit stress in tomato seedlings involves activation of ABA signaling" with DOI:10.3389/fpls.2023.1251442
DOI: 10.5281/zenodo.8326435
title: ABA quantification data from 10.3389/fpls.2023.1251442

description: Raw data from ABA quantification by HPLC MS-MS from the manuscript "Biostimulant activity of Galaxaura rugosa seaweed extracts against water deficit stress in tomato seedlings involves activation of ABA signaling" with DOI:10.3389/fpls.2023.1251442
DOI: 10.5281/zenodo.7329211
title: Kaiso-meDNA systems

description: This list contains the pdb, psf and dcd files of 7 different systems. The MeCG2, MeCG2_mutCG, MeECad, MeECad_mod and MeECad_mod_mutTA systems have three replica runs of length 600 ns or 400ns. The MeCG2_E535A and MeECad_mod_mutTA systems each have a single run of 200 ns. Note: sequences with a C/G pair in the 5'-flanking site: MeCG2_mutCG, MeECad, MeECad_mod; Sequences with a T/A pair in the 5'-flanking site: MeCG, MeECad_mod_mutTA. The trajectories uploaded here are saved at 1 ns/frame (i.e. 600 frames = 600 ns). The systems are pbc-wrapped and aligned for protein. Trajectories that are not pbc-wrapped and saved more frequently at 0.2 ns/frame (i.e. 5x as many frames) are available upon request. To load the trajectories in VMD: <pre><code>mol new 01_MeCG2.psf mol addfile 01_MeCG2_run1.dcd waitfor all</code></pre>
DOI: 10.5281/zenodo.7322448
title: Kaiso-meDNA systems

description: This list contains the pdb, psf and dcd files of 7 different systems. The MeCG2, MeCG2_mutCG, MeECad, MeECad_mod and MeECad_mod_mutTA systems have three replica runs of length 600 ns or 400ns. The MeCG2_E535A and MeECad_mod_mutTA systems each have a single run of 200 ns. Note: sequences with a C/G pair in the 5'-flanking site: MeCG2_mutCG, MeECad, MeECad_mod; Sequences with a T/A pair in the 5'-flanking site: MeCG, MeECad_mod_mutTA. The trajectories uploaded here are saved at 1 ns/frame (i.e. 600 frames = 600 ns). The systems are pbc-wrapped and aligned for protein. Trajectories that are not pbc-wrapped and saved more frequently at 0.2 ns/frame (i.e. 5x as many frames) are available upon request. To load the trajectories in VMD: <pre><code>mol new 01_MeCG2.psf mol addfile 01_MeCG2_run1.dcd waitfor all</code></pre>
DOI: 10.6084/m9.figshare.23576784
title: Dependence of electron flux dropouts in the Earth’s outer radiation belt on energy and driving parameters During Geomagnetic Storms

description: This is the dataset used to produce figures for the paper under review with the title of "Dependence of electron flux dropouts in the Earth’s outer radiation belt on energy and driving parameters".<br>The authors gratefully acknowledge subgrant 1559841 to the University of California, Los Angeles, from the University of Colorado Boulder under NASA Prime Grant agreement 80NSSC20K1580 and NSF GEM award 2025706.
DOI: 10.3886/e193643
title: Land Rights in Historical Vietnam: Theory and Evidence

description: I develop a theory to study the determination of land rights in precolonial Vietnam, in which the state uses restrictive land rights to tie landless peasants to their land, in order to collect head taxes and enforce unpaid labor services and military conscription. Using a unique national land registry in nineteenth-century Vietnam, I find suggestive evidence supporting a hypothesis that higher population density is associated with lower prevalence of private land rights. The experience of historical Vietnam stands in contrast to the standard prediction that private land rights should become more widespread when population density increases and land becomes more valuable. A comparison with the enclosure movement in premodern England provides useful lessons on the development of private land rights in an agricultural economy.
description: All (251) cantons in 12 provinces in the south of nineteenth-century Vietnam
description: Cantons in nineteenth-century Vietnam. Smallest Geographic Unit: Canton
DOI: 10.3886/e193643v1
title: Land Rights in Historical Vietnam: Theory and Evidence

description: I develop a theory to study the determination of land rights in precolonial Vietnam, in which the state uses restrictive land rights to tie landless peasants to their land, in order to collect head taxes and enforce unpaid labor services and military conscription. Using a unique national land registry in nineteenth-century Vietnam, I find suggestive evidence supporting a hypothesis that higher population density is associated with lower prevalence of private land rights. The experience of historical Vietnam stands in contrast to the standard prediction that private land rights should become more widespread when population density increases and land becomes more valuable. A comparison with the enclosure movement in premodern England provides useful lessons on the development of private land rights in an agricultural economy.
description: All (251) cantons in 12 provinces in the south of nineteenth-century Vietnam
description: Cantons in nineteenth-century Vietnam. Smallest Geographic Unit: Canton
DOI: 10.3886/e176021
title: Replication Files for "De-prosecution and death: A comment on the fatal flaws in Hogan (2022)"

description: <b></b>Version 3: Added code in Code/Aletheia-response. This subfolder contains code that was used to incorporate suggestions from Dr. Lauren Schechter (reviewer).<br><br>Version 2: Added two files in the Code/ folder that were used in our response to the response. These are in the Response-to-the-response-code folder. <br><b>Data Creation</b><br>Run "~\code\cpp_response.R" to create clean datasets:<br>"~\input\shr_top_cities.dta""~\input\srs_top_cities.dta"Note this file will also produce some descriptive graphs that are featured throughout the article. This file is optional, in that the clean DTA datasets it produces are provided in the repo in the "input\" folder.<br><b>Replication of main results</b>After downloading the repo to your local machine insert path to repo and run "~\code\set_dirs.do". This will build a directory structure that allows you to run all do files without edits.Run "~\code\required_packages.do" to install all required Stata packages for analysis.If you want the graphs to look similar to the ones in the paper run "~\code\stata_style.do", else ignore and set your own style.To replicate results in Hogan 2022 and compare them to bias-corrected results, run "~\code\main_replication_code.do"To replicate the figure illustrating robustness to extending the pre-treatment start time, run "~\code\vary_preperiod_start_time.do".To replicate the figure illustrating robustness to restricting the donor pool, run "~\code\vary_topN_juris.do".To replicate findings using homicide rates (instead of totals), run "~\code\rep_homicide_rate.do"<br><br><br>
DOI: 10.3886/e176021v1
title: Replication Files for "De-prosecution and death: A comment on the fatal flaws in Hogan (2022)"

description: <b>Data Creation</b><br>Run "~\code\cpp_response.R" to create clean datasets:<br>"~\input\shr_top_cities.dta""~\input\srs_top_cities.dta"Note this file will also produce some descriptive graphs that are featured throughout the article. This file is optional, in that the clean DTA datasets it produces are provided in the repo in the "input\" folder.<br><b>Replication of main results</b>After downloading the repo to your local machine insert path to repo and run "~\code\set_dirs.do". This will build a directory structure that allows you to run all do files without edits.Run "~\code\required_packages.do" to install all required Stata packages for analysis.If you want the graphs to look similar to the ones in the paper run "~\code\stata_style.do", else ignore and set your own style.To replicate results in Hogan 2022 and compare them to bias-corrected results, run "~\code\main_replication_code.do"To replicate the figure illustrating robustness to extending the pre-treatment start time, run "~\code\vary_preperiod_start_time.do".To replicate the figure illustrating robustness to restricting the donor pool, run "~\code\vary_topN_juris.do".To replicate findings using homicide rates (instead of totals), run "~\code\rep_homicide_rate.do"<br><br>
DOI: 10.3886/e176021v2
title: Replication Files for "De-prosecution and death: A comment on the fatal flaws in Hogan (2022)"

description: <b></b>Version 2: Added two files in the Code/ folder that were used in our response to the response. These are in the Response-to-the-response-code folder. <br><b>Data Creation</b><br>Run "~\code\cpp_response.R" to create clean datasets:<br>"~\input\shr_top_cities.dta""~\input\srs_top_cities.dta"Note this file will also produce some descriptive graphs that are featured throughout the article. This file is optional, in that the clean DTA datasets it produces are provided in the repo in the "input\" folder.<br><b>Replication of main results</b>After downloading the repo to your local machine insert path to repo and run "~\code\set_dirs.do". This will build a directory structure that allows you to run all do files without edits.Run "~\code\required_packages.do" to install all required Stata packages for analysis.If you want the graphs to look similar to the ones in the paper run "~\code\stata_style.do", else ignore and set your own style.To replicate results in Hogan 2022 and compare them to bias-corrected results, run "~\code\main_replication_code.do"To replicate the figure illustrating robustness to extending the pre-treatment start time, run "~\code\vary_preperiod_start_time.do".To replicate the figure illustrating robustness to restricting the donor pool, run "~\code\vary_topN_juris.do".To replicate findings using homicide rates (instead of totals), run "~\code\rep_homicide_rate.do"<br><br><br>
DOI: 10.3886/e176021v3
title: Replication Files for "De-prosecution and death: A comment on the fatal flaws in Hogan (2022)"

description: <b></b>Version 3: Added code in Code/Aletheia-response. This subfolder contains code that was used to incorporate suggestions from Dr. Lauren Schechter (reviewer).<br><br>Version 2: Added two files in the Code/ folder that were used in our response to the response. These are in the Response-to-the-response-code folder. <br><b>Data Creation</b><br>Run "~\code\cpp_response.R" to create clean datasets:<br>"~\input\shr_top_cities.dta""~\input\srs_top_cities.dta"Note this file will also produce some descriptive graphs that are featured throughout the article. This file is optional, in that the clean DTA datasets it produces are provided in the repo in the "input\" folder.<br><b>Replication of main results</b>After downloading the repo to your local machine insert path to repo and run "~\code\set_dirs.do". This will build a directory structure that allows you to run all do files without edits.Run "~\code\required_packages.do" to install all required Stata packages for analysis.If you want the graphs to look similar to the ones in the paper run "~\code\stata_style.do", else ignore and set your own style.To replicate results in Hogan 2022 and compare them to bias-corrected results, run "~\code\main_replication_code.do"To replicate the figure illustrating robustness to extending the pre-treatment start time, run "~\code\vary_preperiod_start_time.do".To replicate the figure illustrating robustness to restricting the donor pool, run "~\code\vary_topN_juris.do".To replicate findings using homicide rates (instead of totals), run "~\code\rep_homicide_rate.do"<br><br><br>
DOI: 10.26300/ffba-3x69
title: Data documentation for Amyloid-β PET Scans and Economic Strain among Persons with Cognitive Impairment and Care Partners: A mixed-methods analysis of the CARE-IDEAS Study