cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/mktd4vn3d7.1
title: GENETIC VARIATION IN HAEMOGLOBIN ALTERS CONTROL OF BREATHING IN HIGH-ALTITUDE DEER MICE

description: Supplemental data sets for the publication "GENETIC VARIATION IN HAEMOGLOBIN ALTERS CONTROL OF BREATHING IN HIGH-ALTITUDE DEER MICE"
DOI: 10.17632/gyzx9js5p5.1
title: NIGERIA’S SECURITY AND INTELLIGENCE APPARATUS: GENERAL OWOYE ANDREW AZAZI’S MILITARY APPROACH IN COMBATING BOKO HARAM MILITIA, 2010 – 2012

description: In Nigeria insecurity in the context of violence has reached generally, frightening levels since 2010 with the activities of the dreaded Islamist Jihadist terrorist group known as Boko Haram, rated the second deadliest terror organization in the world in 2012 for killing a total of 1,132 persons in 364 attacks, exceeding Afghanistan’s Taliban, which killed 1,842 persons in 525 attacks. Despite the Federal Government and policy maker’s strategic response and efforts to establish peace in Nigeria, state repression has to be on the increase rather than reduced violence in many parts of the country. Accordingly, this paper examines one of Nigeria’s 20th Chief of Army Staff since 1966, Late General Owoye Andrew Azazi, who was a professional Nigerian Military Intelligence, an expert in Military and Policy Strategic Studies, a multidisciplinary scholar and as well an expert in Terrorism Management. Azazi was concern about national unity, peace and security in Nigeria, extensive rehabilitation projects of Nigeria’s Military barracks, and Military Reforms, thus his concern for all of these prompted him to theorize on the extremist threat doctrine in his shaping of a national response to the Jihadist extremist group. Finally, the paper attempts to contribute towards resolving the insecurity challenges caused by Boko Haram through government-military strategy and making the people be self-reliant rather than being indebted to the "warped goodwill of others.
DOI: 10.17632/7tyw5d3ccm.2
title: Artificial Intelligence to Model the COVID-19 Country Infection Trends

description: The Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) organized an online repository (available at https://github.com/CSSEGISandData/COVID-19) with world-wide information on the absolute number of new confirmed, recovered, and death cases related to the COVID-19 disease (Coronavirus Disease 2019) caused by the Sars-CoV-2 virus (coronavirus). From the whole dataset, we have focused our analysis on the daily time series summaries, which contain the accumulated numbers of confirmed, death, and recovered cases for each country. Given some countries (e.g., Australia, Canada, and China) were reported at the province/state level, we have aggregated all those into a single time series. Another important modification in this dataset was performed to reorganize the daily records. Instead of using accumulated cases, we calculated the lagged differences between consecutive days. Besides the dataset, we also share our source code designed to cluster time series from different countries with similar behavior. Aiming at reproducing our results, run the source code "tree-clustering.R" Our main contribution is the function "calc.dend.dists" available in "distances-dendrogram.R" . For more information, visit our project https://tsviz.icmc.usp.br/covid19
DOI: 10.17632/xh7c8c6485.1
title: Mendeley supplemental tables 1 and 2

description: Supplementary data related to the manuscript entitled 'Diagnostic value of genotypic analysis in primary cutaneous lymphomas using the standardized BIOMED-2 polymerase chain reaction protocols: experience in daily clinical practice ' Mendeley supplemental table 1: Specific diagnoses of patients with primary cutaneous lymphomas. Mendeley supplemental table 2: Specific diagnoses of patients with benign cutaneous lymphoid infiltrates.
DOI: 10.17632/jvvvgpdy48.4
title: Thermal, optical and chemcial dataset of flat premixed Laminar CH4/air standard flames

description: This dataset contains measurements of the thermal and chemical properties of eleven laminar premixed methane-air standard flames previously defined by the DLR Institute of Combustion [1] using a flat flame burner. All properties refer to a height of 15 mm above the sintered porous metal plate. We complement the existing dataset with infrared thermal imaging, photographs, heat flux density measurements and OH*-chemiluminescence for each of the conditions. In addition, the thermochemical properties for the region of interest are calculated using the open-source chemical kinetics software cantera [2]. The code is made available here: https://doi.org/10.5281/zenodo.4306644. The thermal imaging data is acquired with a multispectral camera equipped with eight filters for different spectral bands. The filter specifications, as well as the laboratory conditions (temperature, humidity and barometric pressure), are given for each test. The heat flux density is measured with a water-cooled gage. The gage sensitivity, cooling water temperature and raw data are given below. The OH* chemiluminescence data References: [1] P. Weigand, R. Lückerath, and W. Meier, “Documentation of flat premixed laminar CH4/air standard flames: temperatures and species concentrations,” http//www.dlr. de/VT/Datenarchiv, no. March, 2003, [Online]. Available: http://www.dlr.de/vt/Portaldata/29/Resources/dokumente/ch4_air_flames.pdf. [2] David G. Goodwin, Raymond L. Speth, Harry K. Moffat, and Bryan W. Weber. Cantera: An object-oriented software toolkit for chemical kinetics, thermodynamics, and transport processes. https://www.cantera.org, 2018. Version 2.4.0. doi:10.5281/zenodo.1174508
DOI: 10.17632/bnt6vvwcr7.1
title: Pirosanto et al - 2020

description: Raw geotyping data (BAF and LRR) from ECA10 and ECAX chromosomes
DOI: 10.17632/xghch684sn.2
title: Data for TCP_LR Clinical Application Examples

description: Input and Output data for CHART, CRT & Lung
DOI: 10.17632/ttcskpjgzd.1
title: Alg performance data on benchmark variants

description: This data contains algorithm performance data on variant problems for our paper: ASOC-D-20-02502_R1 'On the Diversity and Robustness of Multi-Objective Test Suites'. There are several new instances (versions) of the ZDT, DTLZ and WFG benchmark problems with changes to their initialised construction parameters, augmenting their characteristics with the domain presented in our paper.
DOI: 10.17632/zkt5km82r2.2
title: Self-Assembly of a Layered 2D Molecularly Woven Fabric - Data Set
DOI: 10.17632/4xxmncfkzr.1
title: Students’ Assessment of an Automatic Educational Change: Opportunities for Online Learning in Crisis Regions in Cameroon

description: This is the raw data from a survey that employed a questionnaire with close and open ended questions. It was aimed at identifying and examining the experiences and challenges of online learning to provide opportunities for online learning for students in the North West and South West regions plagued by a socio-political armed conflict in Cameroon. The data was collected through an online survey at the wake of COVID-19 and validated with a score of 1 and Cronbach alpha reliability index of 0.752. The data was interpreted using percentages, frequency counts, chi-square and thematic analysis which revealed that students view online learning as an opportunity for continuing education in crisis regions, however younger and undergraduate students particularly have limited experiences (P&lt;0.01) with online learning. Similarly, students perceive enormous challenges with online learning to which decision makers can address to make online learning effective in these areas. This data is limited to the case study of the University of Buea in one of the crisis regions in Cameroon.
DOI: 10.17632/tf57gxmxpd.1
title: COVID-19 Diagnostic Testing For All - Using Non-Dilutive Saliva Sample Collection, Stabilization and Ambient Transport Devices

description: GenTegra LLC has introduced two products for saliva collection and viral RNA stabilization: GTR-STM (GenTegra Saliva Transport Medium) and GTR-STMdk (GenTegra Saliva Transport Medium Direct to PCR). GenTegra’s proprietary Active Chemical Protection chemistry is particularly applicable to SARs-CoV-2 PCR assays where prevention of RNA degradation and preservation of stability during transport from sample collection to lab test sites is essential to ensuring high diagnostic test accuracy and sensitivity. GTR-STM stabilizes SARS-CoV-2 virus and viral RNA for 60 days at ambient temperature (Manuscript Data STM). ANCOVA analysis of GTR-STM stability studies at 25C and 56C show that the GTR-STM medium is significantly better at retaining SARS CoV-2 RNA integrity than the control saliva samples (Supplemental Data). GTR-STMdk stabilizes SARS-CoV-2 virus and viral RNA for 3 days at ambient temperature (Manuscript Data STMdk). Both GTR-STM and GTR-STMdk can be disinfection at 95C for 15 minutes (Manuscript Data STM and Manuscript Data STMdk - STMdk 3 Days Stability Study at 25C).
DOI: 10.17632/628x39mmnm.2
title: Why Do People Sacrifice Themselves For The Country? The Role Of Identity Fusion and Empathic Concern

description: Data and supplementary material from article Why Do People Sacrifice Themselves For The Country? The Role Of Identity Fusion and Empathic Concern
DOI: 10.17632/s7c74xw673.1
title: Electronic noses synthetic database

description: Four work scenarios are generated artificially with the chemosensors package to address the deviations present in artificial odor systems. This research leads to a total of ten databases generated from the four scenarios, in which, noise and drift concentration present are parameterized in the generated data. Prior knowledge of these values allows analysis of results using raw data instead of the traditional method for artificial odor systems, which employs feature selection.
DOI: 10.6084/m9.figshare.13345019
title: GoogleLocationHistoryData.csv

description: In order to compare GLH data to travel diary data, GLH data were captured passively across five smartphone devices<b> </b>while two researchers followed prescribed itineraries. While the same five smartphone devices were not all carried on each prescribed itinerary, resulting in an uneven number of samples between devices, two researchers traveled together along prescribed itineraries following identical characteristics of travel. Arrival and departure times from all prescribed itinerary locations were measured empirically by self-report, thus completing the prescribed itineraries as travel diaries. This method afforded <i>a priori </i>knowledge of what the GLH data should be reporting as expected from the prescribed itineraries. In all, 12 unique itineraries were prepared, accounting information that GLH data provide, and to which can be compared: positions of locations, arrival and departure times, location dwell times, trip duration times, trip distances, travel modes, and trip geometries throughout different urban forms. <br> <br> Once GLH data were captured, the downloadable Keyhole Markup Language (KML) files were retrieved from the desktop Google Maps “Your Timeline” feature. The attribute information associated to the point and line shapefiles (SHP) derived from the KML files were then matched to the itinerary data by arrival and departure times within an error of ten minutes. We accepted an error of ten minutes as a reasonable limit for which we could unambiguously assume that the GLH data were sensing our prescribed itinerary travel and assign a match.<br> <br> The following analysis were done for each variable interaction of interest between smartphone device, travel mode and urban form. Mean location position errors were calculated by taking the mean Euclidean distance between the expected locations and the matched GLH data locations. Arrival and departure times were assessed using the root mean square error (RMSE). Similarly, trip duration times, trip distances, and location dwell times were assessed by the normalized RMSE to account for the differences in range for each interaction subset. Finally, GLH data trip geometries were assessed using the mean Hausdorff distance. The Hausdorff distance is a measure of the maximum distance from a point in set A to its nearest point in set B. In other words, of all the shortest distances from each point in set A to points in set B, it is the maximum of those distances. Since GLH data trip geometries were given as linestrings, we converted the linestrings to point sets for computing Hausdorff distances. This was done by generating points along linestring geometries. An interval of one meter between points was chosen as a distance small enough to account for the geometric variation in trip geometry for all scales of travel. The mean Hausdorff distances were then calculated for each interaction subset of travel mode and urban form.
DOI: 10.17632/n8fyb32r9b.1
title: Targeting the Opioid Pathway for the Treatment of Uremic Pruritus: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

description: Supplemental Table 1
DOI: 10.17632/c52km273vv.3
title: Rubisco proton production can drive the elevation of CO2 within condensates and carboxysomes

description: Supplementary data and COPASI modelling file for the analysis of Rubisco compartments. To access the manuscript follow this link: https://doi.org/10.1101/2020.07.08.125609
DOI: 10.17632/3typz6j2hk.1
title: A role for the Mre11-Rad50-Xrs2 complex in gene expression and chromosome organization

description: Mre11–Rad50–Xrs2 (MRX) is a highly conserved complex with key roles in various aspects of DNA repair. Here, we report a new function for MRX in limiting transcription in budding yeast. We show that MRX interacts physically and colocalizes on chromatin with the transcriptional co-regulator Mediator. MRX restricts transcription of coding and noncoding DNA by a mechanism that does not require the nuclease activity of Mre11. MRX is required to tether transcriptionally active loci to the nuclear pore complex (NPC) and also promotes large-scale gene–NPC interactions. Moreover, MRX-mediated chromatin anchoring to the NPC contributes to chromosome folding and helps to control gene expression. Together, these findings indicate that MRX has a role in transcription and chromosome organization that is distinct from its known function in DNA repair. See : https://doi.org/10.1016/j.molcel.2020.11.010
DOI: 10.17632/xgx6hzw59w.1
title: Thermal Modeling of Heat Transfer within LOM Heat Roller used for Laminate Hot-pressing or Boding: Heat Accumulation Model

description: Laminated Object Manufacturing (LOM) Rapid Prototyping Technology (RPT) is an efficient and suitable technique with many applications. However, its application for wood product prototyping (WPP), especially in micro-thin wood objects, namely different types of wood veneers, the thermal heat effect emanating from LOM heat sources, challenge the quality of the final 3D wood products. Therefore, regarding the parts and processes, which thermally affect the workpiece during object lamination (part-building phase), namely laser cutting and laminate bonding or hot pressing, this present report is a part of the understanding of the heat transfer within the heat roller of LOM RPT.
DOI: 10.17632/c7yt2dp457.1
title: Thermal Modeling of Heat Transfer within LOM Heat Roller used for Laminate Hot-pressing or Boding: 2D Axisymmetric Stationary Heat Conduction Model

description: Laminated Object Manufacturing (LOM) Rapid Prototyping Technology (RPT) is an efficient and suitable technique with many applications. However, its application for wood product prototyping (WPP), especially in micro-thin wood objects, namely different types of wood veneers, the thermal heat effect emanating from LOM heat sources, challenge the quality of the final 3D wood products. Therefore, regarding the parts and processes, which thermally affect the workpiece during object lamination (part-building phase), namely laser cutting and laminate bonding or hot pressing, this present report is a part of the understanding of the heat transfer within the heat roller of LOM RPT.
DOI: 10.17632/kbx4c9zsbt.1
title: Thermal Modeling of Heat Transfer within LOM Heat Roller used for Laminate Hot-pressing or Boding: 2D Axisymmetric Transient Heat Conduction Model

description: Laminated Object Manufacturing (LOM) Rapid Prototyping Technology (RPT) is an efficient and suitable technique with many applications. However, its application for wood product prototyping (WPP), especially in micro-thin wood objects, namely different types of wood veneers, the thermal heat effect emanating from LOM heat sources, challenge the quality of the final 3D wood products. Therefore, regarding the parts and processes, which thermally affect the workpiece during object lamination (part-building phase), namely laser cutting and laminate bonding or hot pressing, this present report is a part of the understanding of the heat transfer within the heat roller of LOM RPT.
DOI: 10.17632/m7bv36r2xf.1
title: CB_Dataset_S1

description: The raw individual pupal measurement data for the Current Biology paper.
DOI: 10.18112/openneuro.ds003392.v1.0.2
title: NeuroSpin hMT+ Localizer DATA (MEG aMRI)
DOI: 10.6084/m9.figshare.13345148
title: GoogleLocationHistoryDataKey.csv

description: In order to compare GLH data to travel diary data, GLH data were captured passively across five smartphone devices<b> </b>while two researchers followed prescribed itineraries. While the same five smartphone devices were not all carried on each prescribed itinerary, resulting in an uneven number of samples between devices, two researchers traveled together along prescribed itineraries following identical characteristics of travel. Arrival and departure times from all prescribed itinerary locations were measured empirically by self-report, thus completing the prescribed itineraries as travel diaries. This method afforded <i>a priori </i>knowledge of what the GLH data should be reporting as expected from the prescribed itineraries. In all, 12 unique itineraries were prepared, accounting information that GLH data provide, and to which can be compared: positions of locations, arrival and departure times, location dwell times, trip duration times, trip distances, travel modes, and trip geometries throughout different urban forms. <br> <br> Once GLH data were captured, the downloadable Keyhole Markup Language (KML) files were retrieved from the desktop Google Maps “Your Timeline” feature. The attribute information associated to the point and line shapefiles (SHP) derived from the KML files were then matched to the itinerary data by arrival and departure times within an error of ten minutes. We accepted an error of ten minutes as a reasonable limit for which we could unambiguously assume that the GLH data were sensing our prescribed itinerary travel and assign a match.<br> <br> The following analysis were done for each variable interaction of interest between smartphone device, travel mode and urban form. Mean location position errors were calculated by taking the mean Euclidean distance between the expected locations and the matched GLH data locations. Arrival and departure times were assessed using the root mean square error (RMSE). Similarly, trip duration times, trip distances, and location dwell times were assessed by the normalized RMSE to account for the differences in range for each interaction subset. Finally, GLH data trip geometries were assessed using the mean Hausdorff distance. The Hausdorff distance is a measure of the maximum distance from a point in set A to its nearest point in set B. In other words, of all the shortest distances from each point in set A to points in set B, it is the maximum of those distances. Since GLH data trip geometries were given as linestrings, we converted the linestrings to point sets for computing Hausdorff distances. This was done by generating points along linestring geometries. An interval of one meter between points was chosen as a distance small enough to account for the geometric variation in trip geometry for all scales of travel. The mean Hausdorff distances were then calculated for each interaction subset of travel mode and urban form.
DOI: 10.6084/m9.figshare.13345019.v1
title: GoogleLocationHistoryData.csv

description: In order to compare GLH data to travel diary data, GLH data were captured passively across five smartphone devices<b> </b>while two researchers followed prescribed itineraries. While the same five smartphone devices were not all carried on each prescribed itinerary, resulting in an uneven number of samples between devices, two researchers traveled together along prescribed itineraries following identical characteristics of travel. Arrival and departure times from all prescribed itinerary locations were measured empirically by self-report, thus completing the prescribed itineraries as travel diaries. This method afforded <i>a priori </i>knowledge of what the GLH data should be reporting as expected from the prescribed itineraries. In all, 12 unique itineraries were prepared, accounting information that GLH data provide, and to which can be compared: positions of locations, arrival and departure times, location dwell times, trip duration times, trip distances, travel modes, and trip geometries throughout different urban forms. <br> <br> Once GLH data were captured, the downloadable Keyhole Markup Language (KML) files were retrieved from the desktop Google Maps “Your Timeline” feature. The attribute information associated to the point and line shapefiles (SHP) derived from the KML files were then matched to the itinerary data by arrival and departure times within an error of ten minutes. We accepted an error of ten minutes as a reasonable limit for which we could unambiguously assume that the GLH data were sensing our prescribed itinerary travel and assign a match.<br> <br> The following analysis were done for each variable interaction of interest between smartphone device, travel mode and urban form. Mean location position errors were calculated by taking the mean Euclidean distance between the expected locations and the matched GLH data locations. Arrival and departure times were assessed using the root mean square error (RMSE). Similarly, trip duration times, trip distances, and location dwell times were assessed by the normalized RMSE to account for the differences in range for each interaction subset. Finally, GLH data trip geometries were assessed using the mean Hausdorff distance. The Hausdorff distance is a measure of the maximum distance from a point in set A to its nearest point in set B. In other words, of all the shortest distances from each point in set A to points in set B, it is the maximum of those distances. Since GLH data trip geometries were given as linestrings, we converted the linestrings to point sets for computing Hausdorff distances. This was done by generating points along linestring geometries. An interval of one meter between points was chosen as a distance small enough to account for the geometric variation in trip geometry for all scales of travel. The mean Hausdorff distances were then calculated for each interaction subset of travel mode and urban form.
DOI: 10.17632/vwkrtdg85b.3
title: Adsorption and photocatalytic scavenging of 2-chlorophenol using carbon nitride-titania nanotubes based nanocomposite: Experimental data, kinetics and mechanism

description: Experimental data on the adsorption behavior of 2-chlorophenol (2-CP) onto graphitic pure carbon nitride (C3N4), titania nanotubes (TiO2-NTs) and carbon nitride/titania nanotubes nanocomposite (C3N4/TiO2-NTs) from synthetic wastewater has been summarized