cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48420/14152163
title: JSI Gamma Radiation Dataset and SLAM Map

description: Three files consisting of a txt file dataset of gamma radiation count observations, with SLAM estimated position, and a 2D SLAM map of the Jožef Stefan Institute (JSI) TRIGA Mark II reactor hall.<br>The JSI_radiation_data.txt consists of comma separated values, with a header row indicating the data types. Timestamp is with respect to Unix Epoch time (as is standard in ROS). Spatial coordinates of the robot x, y, z are in metres. As the robot is a ground level 2D robot, the SLAM estimate restricts the Z height to be roughly constant. Only x and y data are necessary. The counts data are in counts per second, indicating the number of events collected over a ~1 second time window by a CeBr<sub>3 </sub>scintillator detector and mixed field analyser.<br>The map file (.pgm) is a trinary (three value) map generated by SLAM via ROS (Robot Operating System). The three values related to occupied (obstacles and physical features, such as walls), unoccupied (free-space), and unknown.<br>The map metadata file (.yaml) provides values to convert pixel position of the map file into coordinates (in metres).<br>Data was originally collected 31/01/2020 (in ROS bag format), which was interrogated to provide the data in the .txt file.<br>This dataset is then post-processed to provide interpolated maps of gamma radiation, based on the point observations and maps in these files.
DOI: 10.48420/14152163.v1
title: JSI Gamma Radiation Dataset and SLAM Map

description: Three files consisting of a txt file dataset of gamma radiation count observations, with SLAM estimated position, and a 2D SLAM map of the Jožef Stefan Institute (JSI) TRIGA Mark II reactor hall.<br>The JSI_radiation_data.txt consists of comma separated values, with a header row indicating the data types. Timestamp is with respect to Unix Epoch time (as is standard in ROS). Spatial coordinates of the robot x, y, z are in metres. As the robot is a ground level 2D robot, the SLAM estimate restricts the Z height to be roughly constant. Only x and y data are necessary. The counts data are in counts per second, indicating the number of events collected over a ~1 second time window by a CeBr<sub>3 </sub>scintillator detector and mixed field analyser.<br>The map file (.pgm) is a trinary (three value) map generated by SLAM via ROS (Robot Operating System). The three values related to occupied (obstacles and physical features, such as walls), unoccupied (free-space), and unknown.<br>The map metadata file (.yaml) provides values to convert pixel position of the map file into coordinates (in metres).<br>Data was originally collected 31/01/2020 (in ROS bag format), which was interrogated to provide the data in the .txt file.<br>This dataset is then post-processed to provide interpolated maps of gamma radiation, based on the point observations and maps in these files.
DOI: 10.5281/zenodo.4742148
title: Replication Package

description: Replication Package
DOI: 10.48420/14152268
title: LUNL Gamma Radiation Dataset and SLAM Map

description: Three files consisting of a txt file dataset of gamma radiation count observations, with SLAM estimated position, and a 2D SLAM map of the Lancaster University Neutron Laboratory (LUNL).<br>The LUNL_radiation_data.txt consists of comma separated values, with a header row indicating the data types. Timestamp is with respect to Unix Epoch time (as is standard in ROS). Spatial coordinates of the robot x, y, z are in metres. As the robot is a ground level 2D robot, the SLAM estimate restricts the Z height to be roughly constant. Only x and y data are necessary. The counts data are in counts per second, indicating the number of events collected over a ~1 second time window by a CeBr<sub>3 </sub>scintillator detector and mixed field analyser.<br>The map file (.pgm) is a trinary (three value) map generated by SLAM via ROS (Robot Operating System). The three values related to occupied (obstacles and physical features, such as walls), unoccupied (free-space), and unknown.<br>The map metadata file (.yaml) provides values to convert pixel position of the map file into coordinates (in metres).<br>Data was originally collected 05/12/2019 (in ROS bag format), which was interrogated to provide the data in the .txt file.<br>This dataset is then post-processed to provide interpolated maps of gamma radiation, based on the point observations and maps in these files.
DOI: 10.48420/14152268.v1
title: LUNL Gamma Radiation Dataset and SLAM Map

description: Three files consisting of a txt file dataset of gamma radiation count observations, with SLAM estimated position, and a 2D SLAM map of the Lancaster University Neutron Laboratory (LUNL).<br>The LUNL_radiation_data.txt consists of comma separated values, with a header row indicating the data types. Timestamp is with respect to Unix Epoch time (as is standard in ROS). Spatial coordinates of the robot x, y, z are in metres. As the robot is a ground level 2D robot, the SLAM estimate restricts the Z height to be roughly constant. Only x and y data are necessary. The counts data are in counts per second, indicating the number of events collected over a ~1 second time window by a CeBr<sub>3 </sub>scintillator detector and mixed field analyser.<br>The map file (.pgm) is a trinary (three value) map generated by SLAM via ROS (Robot Operating System). The three values related to occupied (obstacles and physical features, such as walls), unoccupied (free-space), and unknown.<br>The map metadata file (.yaml) provides values to convert pixel position of the map file into coordinates (in metres).<br>Data was originally collected 05/12/2019 (in ROS bag format), which was interrogated to provide the data in the .txt file.<br>This dataset is then post-processed to provide interpolated maps of gamma radiation, based on the point observations and maps in these files.
DOI: 10.48420/14152418
title: JSI Gaussian Process Regression Interpolation Results

description: Output from Gaussian Process Regression (GPR) on gamma radiation data observations from the reactor hall at the Jožef Stefan Institute (JSI) TRIGA Mark II facility.<br>The file is comma separated, with header row. Data columns are grid positions x and y (in metres), the estimated gamma counts (mean), and the absolute variance estimates, including upper and lower values. Variance can be used as an indication of confidence in a given estimate.<br>Grid positions correspond to locations of "free-space" identified in the SLAM map created by the robot during the survey mission.
DOI: 10.48420/14152418.v1
title: JSI Gaussian Process Regression Interpolation Results

description: Output from Gaussian Process Regression (GPR) on gamma radiation data observations from the reactor hall at the Jožef Stefan Institute (JSI) TRIGA Mark II facility.<br>The file is comma separated, with header row. Data columns are grid positions x and y (in metres), the estimated gamma counts (mean), and the absolute variance estimates, including upper and lower values. Variance can be used as an indication of confidence in a given estimate.<br>Grid positions correspond to locations of "free-space" identified in the SLAM map created by the robot during the survey mission.
DOI: 10.48420/14152550
title: LUNL Gaussian Process Regression Interpolation Results

description: Output from Gaussian Process Regression (GPR) on gamma radiation data observations from the reactor hall at the Lancaster University Neutron Laboratory (LUNL).<br>The file is comma separated, with header row. Data columns are grid positions x and y (in metres), the estimated gamma counts (mean), and the absolute variance estimates, including upper and lower values. The variance can be used as an indicator of confidence in the estimate.<br>Grid positions correspond to locations of "free-space" identified in the SLAM map created by the robot during the survey mission.
DOI: 10.48420/14152550.v1
title: LUNL Gaussian Process Regression Interpolation Results

description: Output from Gaussian Process Regression (GPR) on gamma radiation data observations from the reactor hall at the Lancaster University Neutron Laboratory (LUNL).<br>The file is comma separated, with header row. Data columns are grid positions x and y (in metres), the estimated gamma counts (mean), and the absolute variance estimates, including upper and lower values. The variance can be used as an indicator of confidence in the estimate.<br>Grid positions correspond to locations of "free-space" identified in the SLAM map created by the robot during the survey mission.
DOI: 10.48420/14152559.v1
title: MCNP Simulated Gamma Flux from LUNL Source

description: Simulation results of gamma flux from the Californium source at the Lancaster University Neutron Source (LUNL), using MCNP 6. This data was used to benchmark the GPR approach used to interpolate radiation observations collected by an unmanned ground vehicle.<br>Data is comma separated, with a header row. Columns are x, y, and z grid positions in centimetres, followed by the recorded flux and error estimate.<br>Data was recorded for an xy plane, mimicking the height where a sensor affixed to a ground robot would sample. The flux is energy integrated over 200 keV to 2500 keV, similar to the CeBr<sub>3</sub> detector used in robot deployment.<br>The simulation environment consists of an isotropic <sup>252</sup>Cf source surrounded by water, an air gap, followed by a concrete outer layer. Gamma flux contributions come from the source directly (spontaneous fission spectrum), as well as activation of nearby materials.
DOI: 10.48420/14152559
title: MCNP Simulated Gamma Flux from LUNL Source

description: Simulation results of gamma flux from the Californium source at the Lancaster University Neutron Source (LUNL), using MCNP 6. This data was used to benchmark the GPR approach used to interpolate radiation observations collected by an unmanned ground vehicle.<br>Data is comma separated, with a header row. Columns are x, y, and z grid positions in centimetres, followed by the recorded flux and error estimate.<br>Data was recorded for an xy plane, mimicking the height where a sensor affixed to a ground robot would sample. The flux is energy integrated over 200 keV to 2500 keV, similar to the CeBr<sub>3</sub> detector used in robot deployment.<br>The simulation environment consists of an isotropic <sup>252</sup>Cf source surrounded by water, an air gap, followed by a concrete outer layer. Gamma flux contributions come from the source directly (spontaneous fission spectrum), as well as activation of nearby materials.
DOI: 10.6084/m9.figshare.12850358
title: Data analytics interrogates robotic surgical performance using a microsurgery-specific haptic device

description: With the increase in robot-assisted cases, recording the quantifiable dexterity of surgeons is essential for proficiency evaluations. The present study employs sensor-based kinematics and recorded surgeon experience for evaluating a new haptic device. Thirty surgeons performed a task simulating micromanipulation with neuroArmPLUS<sup>HD</sup> and two commercially available hand-controllers. The surgical performance was evaluated based on subjective measures obtained from survey and objective features derived from the sensors. Statistical analyses were performed to assess the hand-controllers and regression analysis was used to identify the key features and develop a machine learning model for surgical skill assessment. MANCOVA tests on objective features demonstrated significance (α = 0.05) for time (p = 0.02), errors (p = 0.01), distance (p = 0.03), clutch incidents (p = 0.03), and forces (p = 0.00). The majority of metrics were in favor of neuroArmPLUS<sup>HD</sup>. The surgeons found it smoother, more comfortable, less tiring, and easier to maneuver with more realistic force feedback. The ensemble machine learning model trained with 5-fold cross-validation showed an accuracy (SD) of 0.78 (0.15) in surgeon skill classification. This study validates the importance of incorporating a superior haptic device in telerobotic surgery for standardization of surgical education and patient care.
DOI: 10.6084/m9.figshare.12850358.v1
title: Data analytics interrogates robotic surgical performance using a microsurgery-specific haptic device

description: With the increase in robot-assisted cases, recording the quantifiable dexterity of surgeons is essential for proficiency evaluations. The present study employs sensor-based kinematics and recorded surgeon experience for evaluating a new haptic device. Thirty surgeons performed a task simulating micromanipulation with neuroArmPLUS<sup>HD</sup> and two commercially available hand-controllers. The surgical performance was evaluated based on subjective measures obtained from survey and objective features derived from the sensors. Statistical analyses were performed to assess the hand-controllers and regression analysis was used to identify the key features and develop a machine learning model for surgical skill assessment. MANCOVA tests on objective features demonstrated significance (α = 0.05) for time (p = 0.02), errors (p = 0.01), distance (p = 0.03), clutch incidents (p = 0.03), and forces (p = 0.00). The majority of metrics were in favor of neuroArmPLUS<sup>HD</sup>. The surgeons found it smoother, more comfortable, less tiring, and easier to maneuver with more realistic force feedback. The ensemble machine learning model trained with 5-fold cross-validation showed an accuracy (SD) of 0.78 (0.15) in surgeon skill classification. This study validates the importance of incorporating a superior haptic device in telerobotic surgery for standardization of surgical education and patient care.
DOI: 10.6084/m9.figshare.14554644
title: SourceData_ED2.xlsx

description: Source Data ED2<br>
DOI: 10.6084/m9.figshare.14554644.v1
title: SourceData_ED2.xlsx

description: Source Data ED2<br>
DOI: 10.5281/zenodo.4741932
title: MaxCor Labs COVID-19 in Israel Datasets on COVID-19 from 2020 to April 2021

description: MaxCor labs has produced several .csv files of datasets related to the COVID-19 pandemic in Israel. The data is based on data from several datasets produced by the Israeli Ministry of Health with the addition of data from Oxford University, Blavatnik School of Government COVID-19 Government Response Tracker. Full details on the dataset and the code used to produce it are provided.
DOI: 10.5281/zenodo.4742244
title: EMUE-D2-5-TraceabilityFromConformity

description: Measurement traceability is commonly obtained from calibration measurements that provide a result in terms of a single value and its associated uncertainty. However, there are circumstances where instead, the result may consist of a range of possible values. Such circumstances might arise when a result is provided in the form of the output from a conformity decision process, for example as a conformity statement in which a range of acceptable values rather than a specific value is reported. In terms of metrological traceability this style of result provides less information than a specific value, but it may be sufficient to obtain an acceptable target measurement uncertainty for a given application. The standard ISO/IEC 17025 acknowledges this in informative annex A. This example describes how such information might be used to propagate traceability. Files contained in the dataset are: “EMUE 1.2.5.zip”: Zip file containing tex, bib and image files “A125.bib”: Bibliography file “EMUE_A1.2.5.tex”: LaTeX file “EMUE_1_2_5.pdf”: Report - GUM-LPU uncertainty evaluation - importing measurement traceability from a conformity statement “Figure_1_2.m”, “Figure_3.m”: MATLAB scripts used to calculate data for section 3.7
DOI: 10.18112/openneuro.ds002738.v1.0.2
title: rewardBeast
DOI: 10.6084/m9.figshare.14554647
title: SourceData_ED5_a.pdf

description: Source Data ED5<br>
DOI: 10.6084/m9.figshare.14554647.v1
title: SourceData_ED5_a.pdf

description: Source Data ED5<br>
DOI: 10.6084/m9.figshare.14555169.v1
title: Code for Fig S1 from Interventions targeting non-symptomatic cases can be important to prevent local outbreaks: SARS-CoV-2 as a case study

description: The MATLAB code used to generate Supplementary Figure S1
DOI: 10.6084/m9.figshare.14555169
title: Code for Fig S1 from Interventions targeting non-symptomatic cases can be important to prevent local outbreaks: SARS-CoV-2 as a case study

description: The MATLAB code used to generate Supplementary Figure S1
DOI: 10.6084/m9.figshare.14555172
title: Code for Fig 2A from Interventions targeting non-symptomatic cases can be important to prevent local outbreaks: SARS-CoV-2 as a case study

description: The MATLAB code used to generate Fig 2A in the main text
DOI: 10.6084/m9.figshare.14555178
title: Code for Figs 2B,C,D, Fig 3, Figs S3-S12 from Interventions targeting non-symptomatic cases can be important to prevent local outbreaks: SARS-CoV-2 as a case study

description: The MATLAB code used to generate Figs 2B,C,D and Fig 3 in the main text, and Figs S3-S12 in the supplementary material
DOI: 10.6084/m9.figshare.14555172.v1
title: Code for Fig 2A from Interventions targeting non-symptomatic cases can be important to prevent local outbreaks: SARS-CoV-2 as a case study

description: The MATLAB code used to generate Fig 2A in the main text
DOI: 10.6084/m9.figshare.14555175
title: Code for Fig 1B from Interventions targeting non-symptomatic cases can be important to prevent local outbreaks: SARS-CoV-2 as a case study

description: The MATLAB code used to generate Fig 1B in the main text
DOI: 10.6084/m9.figshare.14555178.v1
title: Code for Figs 2B,C,D, Fig 3, Figs S3-S12 from Interventions targeting non-symptomatic cases can be important to prevent local outbreaks: SARS-CoV-2 as a case study

description: The MATLAB code used to generate Figs 2B,C,D and Fig 3 in the main text, and Figs S3-S12 in the supplementary material
DOI: 10.6084/m9.figshare.14554353.v1
title: SourceData_ED4_f_left.tsv.gz

description: Source Data ED4<br>
DOI: 10.6084/m9.figshare.14554353
title: SourceData_ED4_f_left.tsv.gz

description: Source Data ED4<br>
DOI: 10.6084/m9.figshare.14465367
title: Orthogroup data for inferring protist evolutionary relationships

description: This dataset contains 45 821 orthogroup (.fal) files plus accompanying tree (.arb) files, clustered from the genomes/transcriptomes of 97 taxa spanning the eukaryote tree of life.<br>Accompanying this are two plaintext files, containing the ID codes for every taxon in the dataset and the eukaryote group to which each belongs. The files differ in their eukaryote group definitions - one contains 15 distinct groups, the other splits two of these (SAR and Haptista) into their respective subgroups, for a total of 18 groups.<br>Using the ID codes the orthogroup files can be scanned programmatically to determine the number of orthogroups that are shared between different groups of eukaryotes.
DOI: 10.6084/m9.figshare.14465367.v1
title: Orthogroup data for inferring protist evolutionary relationships

description: This dataset contains 45 821 orthogroup (.fal) files plus accompanying tree (.arb) files, clustered from the genomes/transcriptomes of 97 taxa spanning the eukaryote tree of life.<br>Accompanying this are two plaintext files, containing the ID codes for every taxon in the dataset and the eukaryote group to which each belongs. The files differ in their eukaryote group definitions - one contains 15 distinct groups, the other splits two of these (SAR and Haptista) into their respective subgroups, for a total of 18 groups.<br>Using the ID codes the orthogroup files can be scanned programmatically to determine the number of orthogroups that are shared between different groups of eukaryotes.
DOI: 10.5281/zenodo.4741214
title: Visualizing DNA folding and RNA in embryos at single-cell resolution

description: See: https://www.nature.com/articles/s41586-019-1035-4 for a description of the approach Dataset organization: The dnaData files contain 3D chromatin paths measured using Optical Reconstruction of Chromatin Architecture are recorded in nanometers, (x,y,z positions). Individual paths are also indexed by cell ID number, embryo ID number, embryo segment type ID number, brightness, and genomic position. The expression level of 29 RNA targets in the corresponding cells are indexed in the accompanying rnaData document. The cell ID number can be used to link the RNA expression and DNA structure from the same cells. RNA expression is provided in molecule counts per cell for cytoplasmic mRNA targets, and in arbitrary brightness units for intronic RNA targets.
DOI: 10.5281/zenodo.4741213
title: Visualizing DNA folding and RNA in embryos at single-cell resolution

description: See: https://www.nature.com/articles/s41586-019-1035-4 for a description of the approach Dataset organization: The dnaData files contain 3D chromatin paths measured using Optical Reconstruction of Chromatin Architecture are recorded in nanometers, (x,y,z positions). Individual paths are also indexed by cell ID number, embryo ID number, embryo segment type ID number, brightness, and genomic position. The expression level of 29 RNA targets in the corresponding cells are indexed in the accompanying rnaData document. The cell ID number can be used to link the RNA expression and DNA structure from the same cells. RNA expression is provided in molecule counts per cell for cytoplasmic mRNA targets, and in arbitrary brightness units for intronic RNA targets.
DOI: 10.6084/m9.figshare.14554359
title: SourceData_ED3_d_DMR.tsv.gz

description: Source Data ED3<br>
DOI: 10.6084/m9.figshare.14554359.v1
title: SourceData_ED3_d_DMR.tsv.gz

description: Source Data ED3<br>
DOI: 10.6084/m9.figshare.7680938.v1
title: Daunorubicin and cytarabine for certain types of poor-prognosis acute myeloid leukemia: a systematic literature review

description: Induction chemotherapy based on anthracyclines and cytarabine (Ara-C) combination remains the standard of care for acute myeloid leukemia (AML) patients who are considered candidate for intensive and curative approaches. However, the toxicity of this regimen is high, with disappointing clinical outcomes among the so-called poor-prognosis AML subsets, which generally refer to patients with adverse cytogenetic risk, secondary AML including therapy-related AML, poor-prognosis mutations, especially FLT3-ITD, and relapse/refractory AML. To the best of our knowledge, the role and efficacy of 7 + 3 schedules containing daunorubicin (DNR) and Ara-C for certain types of poor-prognosis AML has not been systematically assessed. A critical approach to the role of DNR and Ara-C induction could be relevant to establish which patients should be enrolled in clinical trials using novel therapies. In this regard, a recent randomized clinical trial (RCT) showed improved results in older patients with sAML or high-risk cytogenetics who received CPX-351 compared with standard 7 + 3 combination. We perform a systematic literature review to analyze the clinical outcomes reported with DNR plus Ara-C regimens in adult patients with poor-prognosis AML, the use of liposomal formulations of DNR and Ara-C and the RCTs which compared standard 7 + 3 with the addition of a third drug.