cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/tjdxjv8cz3
title: Germinated Winter Wheat (Triticum aestivum L) and Buckwheat (Fagopyrum esculentum) Extract for Chitin Isolation from White Shrimp Shells

description: Germinated grains can be a source of low-cost protease for chitin extraction.  Using crude protease from germinated grains in combination with chemicals can reduce the use of the chemicals in isolating chitin.  This study aimed to determine proteolytic and chitinolytic activities of crude enzymes extracted from germinated winter wheat (Triticum aestivum) and buckwheat (Fagopyrum esculentum).  The crude enzyme was applied in the deproteinization step during chitin isolation from shrimp shells.  The deproteinization rate was assessed, the crude chitin yield was compared and the isolated chitins were characterized through x-ray diffraction analysis. Proteolytic activities and chitinolytic activities were detected in the crude enzymes extracted from germinated winter wheat and buckwheat as shown by clear zones in agars containing azocasein, casein, gelatin and colloidal chitin through radial diffusion study (A Diameter of clear zones). The germinated winter wheat and buckwheat extracts exhibited proteolytic activity of 0.49 U/mL and 0.46 U/mL (B Proteolytic activities of germinated winter wheat and germinated buckwheat), and chitinolytic activity of 0.074 and 0.053 U/mL, respectively (C Chitinolytic activity of germinated winter wheat and germinated buckwheat).  Using winter wheat extract and buckwheat extract acted on demineralized shrimp shells exerted a deproteinization rate of 53.6±1.0% and 58.0±2.3% (D Deproteinization rate) and yielded 30.3±5.31% and 29.2±3.99% (E Crude chitin yield) crude chitin, respectively.  The crystallinity index for commercial chitin, chemical-isolated chitin, wheat extract- and buckwheat extract-isolated chitin were 86.49%, 88.74%, 88.82% and 75.87% (F1 to F4 X-ray diffraction data), respectively.  Buckwheat extract-isolated chitin, which was deacetylated with lower crystallinity, warrants further investigation.  Crude enzymes from winter wheat and buckwheat to remove protein from shrimp shells can be a more environment-friendly method to valorize shrimp shells as a chitin source.
DOI: 10.17632/wj8njsnwz4
title: SEC-SAXS data reduced AIF

description: SEC-SAXS data on reduced AIF
DOI: 10.17632/yj5cbjv2nm
title: Sustainability and Performance

description: SLR
DOI: 10.6084/m9.figshare.12894536.v2
title: Discerning Lunar Pyroclastic and Impact Glasses via Raman Spectroscopy

description: <b>The measured geochemical data and Raman spectra data of lunar glasses</b><br>
DOI: 10.17632/96bwxvb83m
title: Dataset neural signatures of risk-taking

description: Dataset for the paper "Neural Signatures of Heterogeneity in Risk-Taking and Strategic Behavior" by Josh Leota, Tobias Kleinert, Alex Tran and Kyle Nash.
DOI: 10.17632/xvphsxjm5m
title: Volumetric Quantifications of Areas Undergoing Retrogressive thaw slumping from ArcticDEM

description: Polygons of retrogressive thaw slumps mapped from ArcticDEM time series. These data are the results in a paper entitled "Volumetric Quantifications and Dynamics of
Areas Undergoing Retrogressive Thaw Slumping in the Northern Hemisphere
Between 2012 and 2022 ".

Format: ESRI Shapefile (Polygons)
Projection: EPSG:4326 (Lat/Lon WGS84)

VolLatlon.shp data format: longitude, latitude, total volume of each RTS (m^3, 'volume'), median volume loss per year of each RTS (m^3/year, 'med_volperyr'), the correlation of volume loss per year with the temperature (July August, 'cor_T'), the correlation of volume loss per year with the precipitation (July August, 'cor_P07'), the correlation of volume loss per year with the precipitation (April May June, 'cor_P04'), volume loss acceleration (m^3/year^2, 'vol_acc'), uncertainty of volume loss acceleration (m^3/year^2,'vol_acc_std').

ArcticRTS.shp data format: Bounding box of the polygon, longitude, latitude, total area of each RTS (m^2), zone type, latitude and longitude of the center of the polygon, region id names (ArcticDEM convention), total volume of each RTS (m^3), total volume uncertainty (m^3), median volume loss per year (m^3/year), the uncertainty of median volume loss per year (m^3/year), mean elevation decrease over the scar area (m), peak elevation decrease (m), corresponding file names for elevation change maps.
DOI: 10.17632/5ygfynng4z
title: Data for the regression of synthetic load profiles for heat demand forecasting

description: This repository contains datasets and results related to a heat demand forecasting method based on synthetic load profiles generated from regression analysis. These load profiles represent heat demand as a function of the so called allocation temperature. These datasets include the training and validation data, that was used to generate and verify the synthetic heat demand profile, as well as calculated hourly correction factors for temporal allocation.
DOI: 10.17632/pm6t9hz23d
title: Geolocation IP Datasets G7, G15, and G40 for Hornet 7, Hornet 15, and Hornet 40 Datasets

description: Geolocation IP Datasets G7, G15, and G40 for Hornet 7, Hornet 15, and Hornet 40 Datasets

The G7.csv, G15.csv, and G40.csv files contain geolocation Information for all resolvable Distinct Source IP Addresses for Hornet 7 [1], Hornet 15 [2], and Hornet 40 [3] datasets created by Valeros, Veronica in 2021.

The influence of geo-economics on cyberattacks is being investigated.

The datasets consists of eight fields:
status,continent,continentCode,country,countryCode,region,regionName,city,lat,lon,timezone,offset,currency,isp,org,as,asname,mobile,proxy,hosting,query,SrcAdd,Count

(a) SrcAdd - distinct attacking source IPs, and 
(2) Count - corresponding attack counts (the number of net-flow records per distinct source IP).

The meaning of other fields can be checked at IP-API.com (IP geolocation API web service) 


Ref:
[1] Valeros, Veronica (2021), “Hornet 7: Network Dataset of Geographically Placed Honeypots”, Mendeley Data, V3, doi: 10.17632/w6yskg3ffy.3
[2] Valeros, Veronica (2021), “Hornet 15: Network Dataset of Geographically Placed Honeypots”, Mendeley Data, V2, doi: 10.17632/rry7bhc2f2.2
[3] Valeros, Veronica (2021), “Hornet 15: Network Dataset of Geographically Placed Honeypots”, Mendeley Data, V2, doi: 10.17632/rry7bhc2f2.2
DOI: 10.17632/vcbh2gt8wg
title: Calculation of 1D and 2D densities in VMD: A flexible and easy-to-use code

description: Characterization of structural information at the atomistic level in molecular dynamics (MD) simulations is a necessary task for researchers in the fields of materials modeling and simulation. Visualization of the density distribution is typically one of the most important properties in structural characterization. Visual Molecular Dynamics (VMD) is a widely used molecular visualization package that can not only visualize complex molecular systems but also perform analysis by integrating special plugins or by running in-house generated TCL scripts. However, a density analysis is still not an in-built feature of VMD. This work presents a flexible and easy-to-use TCL code to be used in VMD, that can perform both 1D and 2D density calculations over any specified local areas of a given system. By using the built-in commands of VMD, the code can access and process trajectory files in any formats that are supported by VMD, as produced by mainstream simulation packages, i.e., LAMMPS, GROMACS, NAMD, and CHARMM, etc. This work introduces the calculation method, code, and usages in detail to provide a quick start for users in their density analysis work.
DOI: 10.17632/bdkhkmy4xm
title: Gagnon et al. Muscle temperature on VO2_Qm relationship 2025

description: Data file containing Muscle blood flow (Qm), muscle oxygen consumption (mVO2), muscle tissue saturation (%TSI), total peripheral resistance (TPR), arterio-venous difference (A-VO2diff), mean arterial pressure (MAP), heart rate (HR), stroke volume (SV), cardiac output (CO), pulmonary VO2 (VO2pul), minute ventilation (VE), respiratory quotient (RQ), muscle temperature (Tm) and skin temperature (Tskin).
DOI: 10.17632/fswwgp98gj
title: Data-Rural Water Supply- AHP-TOPSIS-FUZZY

description: Data-Rural Water Supply- AHP-TOPSIS-FUZZY
DOI: 10.17632/7z6cg6kgdy
title: Data for: Synthesis of the Trisaccharide Repeating Unit of Capsular Polysaccharide from Klebsiella Pneumoniae
DOI: 10.6084/m9.figshare.18339416.v2
title: COVID-CT-Rate.zip

description: COVID-CT-Rate is a dataset including 433 CT images from 82 COVID-19 patients with their associated infection masks. It can be used for training AI models to segment COVID-19 lesions from chest CT images.For the annotation process, first, infection masks were generated using a standard U-Net pre-trained on a public COVID-19 dataset. Then, a thoracic radiologist with 20 years of experience in lung imaging carefully modified and verified the generated infection masks. All CT images have been obtained without contrast enhancement and saved in the Digital Imaging and Communications in Medicine (DICOM) format and the Hounsfield Unit.CT images have been selected from diffident parts of the lung (top, middle, and bottom) with different infection rates to help the AI model better predict the infection regions on unseen CT images from the whole lung volume.DICOM files for each patient have been saved in individual folders within the COVID-Rate-CT directory, and corresponding infection masks, labeled with patient and image numbers, have been stored in the COVID-Rate-Masks directory.For ease of use, you can also download CT images and infection masks from the <code>CTs.npy and </code>InfMasks<code>.npy</code> files. <br>If you found this dataset helpful to your research, please consider citing:<br>Enshaei N, Oikonomou A, Rafiee MJ, Afshar P, Heidarian S, Mohammadi A, Plataniotis KN, Naderkhani F. COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images. Scientific Reports. 2022 Feb 25;12(1):3212.
DOI: 10.17632/3brbjpt39s
title: Peak Energy Demand in the Electricity Energy Dataset BanE-16

description: The Peak Energy Demand in the Electricity Grid Dataset includes various data types such as: Numeric Data: This comprises numerical measurements related to energy consumption, temperature, wind speed, preceptor. Time Series Data: Information recorded over time intervals, showcasing patterns and trends in energy consumption or demand. The dataset likely contains a mix of these data types to facilitate comprehensive analysis and prediction of peak energy demand within the electricity grid with some environmental variable.
DOI: 10.17632/544jj4vn69
title: Sustainability data

description: Sustainability in sport dataset.
DOI: 10.17632/ckpz6brdj6
title: A reliable and fast method for the solution of Fredhol integral equations of the first kind based on Tikhonov regularization

description: Abstract In many physical and chemical applications one is faced with the problem of calculating the solution of a Fredholm integral equation of the first kind from noisy experimental data. For such problems a solution method based on Tikhonov regularization together with the computer program FTIKREG is presented. In contrast to other solution methods which are based on Tikhonov regularization especially the determination of the regularization parameter has been improved. This improvement leads to muc... Title of program: FTIKREG Catalogue Id: ACGH_v1_0 Nature of problem Experimental not accessible functions and distributions are often related to an experimental accessible quantity by a Fredholm integral equation of the first kind. Therefore the solution of the integral equation must be calculated from noisy experimental data, in order to determine such a function or distribution. Versions of this program held in the CPC repository in Mendeley Data ACGH_v1_0; FTIKREG; 10.1016/0010-4655(92)90132-I ACGH_v2_0; NLREG; 10.1016/0010-4655(93)90187-H ACGH_v3_0; GENEREG; 10.1016/S0010-4655(01)00217-X This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/kk6hktdydg
title: Physical simulation of obstructive respiratory dysfunction 

description: To simulate chronic obstructive pulmonary disorder (COPD) the device designed is to be used with an existing venturi-based flow sensor with a y-split tube to separate inhalation and exhalation. The device is to be attached to the exhalatory side of the y-split to leave inhalation unchanged. The device is comprised of both low and high resistance outlets. During exhalation air initially fills the low resistance free volume, as this fills and pressure increases the flow is diverted out of the fixed high resistance outlet. This diversion creates an increase in the resistance to end-exhalation to simulate to non-linearity in resistance to exhalation found in people who have COPD. 

The customisable sizing of the free volume allows multiple levels of COPD severity to be simulated. This can additionally be changed to suit the application as each part is 3D printed. This device would be used for respiratory research to validate models using healthy people and non-invasive methods.

-	COPD simulation 
-	Low cost validation of respiratory models 
-	Non-invasive data collection
DOI: 10.17632/rgfhzd5mzw
title: Potato Viral Disease Dataset on both Foliar and Tuber

description: Potato (Solanum tuberosum) is the main crop that is vegetatively propagated(cloning). It is the third most important food crop after wheat and rice in terms of human consumption. This crop is affected by various types of diseases like bacterial, fungal, and viral diseases. As, a traditional practice some viral disease are identified by visual symptoms and some by laboratory methods like Enzyme-linked immunosorbent assay (ELISA), real time reverse-transcription- polymerase chain reaction (RT-PCR) etc., the above methods are tedious, computational costs along with time will be high and it is requiring a more labor-intensive and controlled lab structure to carry out the experiment. So, there is an urgent need for developing an automated model which employs machine learning, deep learning methodology which are subset of Artificial Intelligence. This model helps agriculturist/ farmers for early detection of viral disease. and take effective measures for huge yield loss. The dataset shows different types of viral diseases that the potato crop foliar and tubers get effected with namley Mosaic Virus, Potato Leaf Roll Virus(PLRV), on foliar, and on tuber Potato Spindle Tuber Viriod (PSTVD), Potato Virus Y(PVY)-tuber cracking. The dataset of foliar and tuber comprises of Nineteen hundred and seventy two images Mosaic(666), PLRV(527), Healthy leaf(135), PSTVD(85), PVY cracking (559). Each image of size in pixels is 4288*2848 and camera employed is Nikon D90,its configuration details include f-stop-f/5.3, ISO speed -ISO-250, focal length-80mm, contrast -medium, No flash. The dataset is collected from potato fields of University of Agricultural Sciences, Dharwad where 3 acres of land is cultivated with certified seeds from modipuram regional Centre and 1 acre is cultivated with uncertified seeds. The scientist from the research institute shared the image data from Indian Council of Agricultural Research- Central Potato Research Institute (ICAR- CPRI) shimla. The data set is captured in the day light by placing the leaf or tuber on black background. The image data collected has different symptoms like plant affected with mosaic virus has yellow color spread throughout the leaf the leaf can be classified as mild, medium and severe mosaic. The color feature plays a major role in classifying the mosaic viral disease the symptoms include yellow color propagated throughout the leaf. The PLRV symptoms are rolling of leaf towards upward direction the texture of the leaf is little crunchy in nature. The main features here are shape and texture for accurately classifying the disease. In tuber PSTVD where the tubers are elongated that is based on shape features the classifier gives the accurate results. PVY cracking it is one of the strains of potato virus Y where the tubers have cracks on external part which is noninfectious. The cracks generally start at bud and extend lengthwise.
DOI: 10.17632/wws2cx74zw
title: TNF synergy with IL-17A & IL-17F stimulation of MSCs

description: The IL-17 pathway is implicated in axial SpA and targeted by multiple biologics in the clinic, we wanted to assess the downstream effects of IL-17A and IL-17F on entheseal stroma as these cytokines have been identified to drive inflammation and possibly new bone formation. Specifically we wanted to investigate the synergistic effect of IL-17A and IL-17F in combination with TNF.
Bulk-seq was performed on spinal MSCs isolated from human spinal entheseal bone and soft tissue, MSCs were stimulated with IL-17A, IL-17F and TNF alone and in combination to assess the synergistic effects of these cytokines on stromal function and activation.
DOI: 10.17632/nn8xj6zgst
title: Diagnosis of Mycosis Fungoides or Sézary Syndrome After Dupilumab Use: A Systematic Review. 

description: Supplemental materials
DOI: 10.17632/wwdwhrzvd8
title: Identification, Evolutionary Analysis, and Biological Characterization of Bovine Parvovirus in Northeastern China

description: BPV titer test results（TCID50）
DOI: 10.6084/m9.figshare.21687359.v1
title: Green Bond towards Sustainability

description: 11% of green bonds that have been issued so far are related to clean water and sanitation (SDG 6). Water security and climate change are intricately linked, as is becoming increasingly clear. Although the market for green bonds has broadened over the past few years, clean energy (SDG7) still accounts for 40% of the market. Low-carbon transportation accounts for 15% of the market for green bonds, with low-carbon buildings making for 24% of it.
DOI: 10.6084/m9.figshare.5661988.v1
title: Genome-wide microsatellite panel for Brycon orbignyanus

description: Supplementary Table S2 - A broad genomic panel with potentially amplifiable microsatellite loci for <i>Brycon orbignyanus</i> is publicly made available. Loci marked with an asterisk (*) have been empirically validated as polymorphic (Arias et al., 2016; Conservation Genetics Resources, Volume 8, issue 1, p 43-81). These results are part of the article "A broad genomic panel of microsatellite loci from Brycon orbignyanus (Characiformes: Bryconidae), an endangered migratory Neotropical fish". Scientific Reports, 8: 8511, 2918
DOI: 10.17632/79zv29fngj
title: Data for: A Comprehensive Test of the Fama-French Five-Factor Model in Emerging Markets

description: The spreadsheet contains the factor returns and portfolio excess returns for Asia, Eastern Europe and Latin America
DOI: 10.17632/g6mnc24f5b
title: Traffic Signals Detector using YOLOv8

description: This repository presents the obtained products from the research article "Commissioning of the NVIDIA Jetson AGX Orin module for a traffic signals detector based on YOLO"; this article, among other things, presents a real-time Traffic Signals Detector model trained on the NVIDIA Jetson AGX Orin module using CUDA cores. The proposed TSD is based on a pre-trained YOLOv8m model. Some detections and metrics of the TSD are shown to evaluate its performance.
DOI: 10.17632/rznn6s5myw
title: TurboPy: A lightweight python framework for computational physics

description: Computational physics problems often have a common set of aspects to them that any particular numerical code will have to address. Because these aspects are common to many problems, having a framework already designed and ready to use will not only speed the development of new codes, but also enhance compatibility between codes. Some of the most common aspects of computational physics problems are: a grid, a clock which tracks the flow of the simulation, and a set of models describing the dynamics of various quantities on the grid. Having a framework that could deal with these basic aspects of the simulation in a common way could provide great value to computational scientists by solving various numerical and class design issues that routinely arise. This paper describes the newly developed computational framework that we have built for rapidly prototyping new physics codes. This framework, called turboPy, is a lightweight physics modeling framework based on the design of the particle-in-cell code turboWAVE. It implements a class (called Simulation) which drives the simulation and manages communication between physics modules, a class (called PhysicsModule) which handles the details of the dynamics of the various parts of the problem, and some additional classes such as a Grid class and a Diagnostic class to handle various ancillary issues that commonly arise.
DOI: 10.17632/r5t954xrvb
title: Data for: Human-wildlife conflicts and their correlates in Narok County, Kenya
DOI: 10.17632/rpck298r8b
title: Data for: Identification of rice plant diseases using lightweight attention networks

description: Experimental program & data
DOI: 10.17632/yfg9xf7nbs
title: Investigating the effects of crosslinker systems on the properties of waterborne epoxy-acrylate core-shell emulsion, film, and coating

description: Recently, waterborne coating has been a trend in research. The waterborne epoxy-acrylate (WEA) core-shell emulsion has been widely studied. Hence, in this research, we have investigated the impacts of the crosslinking systems on the properties of the WEA core-shell emulsion, film and coatings. The emulsions were synthesized through semi-continuous emulsion polymerization. Two kinds of crosslinking systems were achieved by adding the crosslinker (MAA) at the core layer (intralayer crosslinking) or the shell layer (interlayer crosslinking). The interlayer crosslinking provides a very high crosslinking density (7.01 × 10-4 mol cm-3) as it involves the crosslinking of polymer chains between the core and shell layers. Consequently, this film has a compact structure and high modulus (489.12 MPa) but low elongation at break (3.20 %). Conversely, the intralayer crosslinking has a low crosslinking density (1.59 × 10-4 mol cm-3). Hence, the film has a loose structure and low modulus (0.38 MPa) but high elongation at break (225.29 %). This is because it only involves crosslinking with the polymer chains in the same layer, leading to poor compatibility between the layers. The incorporation of crosslinker also allows two Tg to be observable. Lastly, the crosslinking system does not affect the stability (shelf-life) of the emulsion, but the oxirane ring of the Epoxy (EP) does. We believe the findings from this research could help future researchers better understand the properties of WEA core-shell coating.
DOI: 10.6084/m9.figshare.19200494.v1
title: Virb4 secretion protein phylogeny full and subtree files

description: #virb4 trimmed alignment subtree, nexus formatvirb4_trimmed_alignment_nexus_subtree_comchoanosNotara.9Nov20.aln.masked.nex<br>#virb4 trimmed alignment subtree, fasta formatvirb4_trimmed_alignment_subtree_comchoanosNoTara.9Nov20.aln.trimmed.fasta<br>#virb4 trimmed alignment subtree, fasta format not rimmedvirb4_alignment_subtree_comchoanosNoTara.9Nov20.aln.fasta<br>#virb4 subtree bayesian, tree filevirb4_bayes_subtree_comchoanos_notara_MrB_9nov.con.tre<br><br>#virb4 raxml subtree, tree file, newickvirb4_raxml_subtree_RAxML_bipartitions.comchoanosNoTara_9Nov.new<br>#virb4 raxml full tree, alignment filededupe.all.combined.t4ss.virb.list.notara alignment.fasta<br>#virb4 raxml full tree, fasttree newick filevirb4_fastree_fulltree_dedupe.all.combined.t4ss.virb.list.notara alignment FastTree Tree.newick