cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/w6jxx2n5kd.1
title: Identification and ranking of supply chain risks in Digital Libraries State universities of Tehran based on ISO 31000 standard.

description: The present research has been done with a view to identifying and ranking the supply chain risks in digital libraries of state universities in Tehran based on the 2018 version of ISO 31000. This research is an applied one and to implement it, survey and analytic methods have been applied with Shannon entropy to weight risks and fuzzy TOPSIS to evaluate them. Primarily the most important risks were identified using the relevant resources and the results of previous researches; a process which led to the formulation of a questionnaire with 5 components and 109 items. The stability of the questionnaire was calculated based on the internal consistency coefficient & Cronbach alpha coefficient using SPSS software. The resulted value in each aspect of the questionnaire amounted to more than %98 and then the analysis and ranking of the risks were carried out using the above-mentioned techniques. In this research the risks of supply chain have been evaluated in 5 sections: suppliers, production & digital resources management, distribution of digital resources, services to users and common risks among the components of supply chain. In the suppliers section the risk index of "the fluctuation of exchange rates" has the highest rank (=%73), in production and digital resources management, the risk index of "the strategy of collection development of digital resources" has the highest rank (=%73), in distribution of digital resources section, the risk index of "possibility of rendering web-based loan services in digital library" has the highest rank (=%84), in services to users section, the risk index of "The possibility of searching by means of any browsers in digital library" has the highest rank (=%88) and in the common risks among the components of supply chain section, the index risk of "the increase of hardware prices of the components of supply chain due to the fluctuation of exchange rate" has the highest rank (=%82). The risks based on their ranking and by applying the TOPSIS technique in the supply chain of digital libraries in the universities of Tehran in order of importance that is from number 1 to number 5 include: "risks of suppliers", "common risks among the components of supply chain", "risks relating to services of users", "risks involving the production & digital resources management", and "risks involving the distribution of digital resources". Keywords digital libraries Evaluation risks of supply chain 31000 ISO Tehran`s state universities
DOI: 10.25582/data.2022-05.2509537/2124290
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2509012/2124291
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/h9kzt7ktm7.1
title: Dataset from a Systematic Review with Network Meta-Analysis on Pre-oxygenation Techniques

description: This is a dataset from a systematic review on techniques for pre-oxygenation.
DOI: 10.17632/h9kzt7ktm7.2
title: Dataset from a Systematic Review with Network Meta-Analysis on Pre-oxygenation Techniques

description: This is a dataset from a systematic review on techniques for pre-oxygenation.
DOI: 10.17632/6sg3x4bchj.1
title: Post-Traumatic Stress Disorder (PTSD) Development in the Aftermath of the Turkey Earthquake: Exploring the Role of Demographic Variables, Personality Traits, and Psychological Vulnerability

description: Data on the relationship between post-traumatic stress disorder level, personality traits and psychological vulnerability levels of the victims of the February 6 Great Turkey Earthquake. Background: The most common mental disorder after mass traumas such as earthquakes is Post-Traumatic Stress Disorder (PTSD). In our study, we aimed to investigate the development of PTSD in the third month after the earthquake and its association with some demographic variables, personality traits, and psychological vulnerability. Methods: 503 people who experienced the earthquake were included in the study. The Demographic Data Form, Ten-Item Personality Inventory (TIPI), Psychological Vulnerability Scale (PVS), and PTSD Checklist 5 (PCL-5) were applied to the individuals participating in the study. Results: PTSD scores were higher in women, those who witnessed someone else's injury and death, those with a previous psychiatric illness, singles, those with a low income level, and those whose homes were heavily damaged. Extraversion, emotional stability, and responsibility predict psychological vulnerability, and psychological vulnerability directly predicts PTSD. In addition, extraversion and emotional stability also indirectly predict PTSD. Conclusion: Being a woman, witnessing someone else's injury and death, having a previous psychiatric illness, being single, having a heavily damaged home, having a low income level, personality traits, and psychological vulnerability may pose a risk for the development of PTSD after an earthquake.
DOI: 10.25582/data.2022-05.2513102/2124292
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2513091/2124293
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2513109/2124294
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/k2jhgktzd7.1
title: Figure 1

description: Standardized differences before and after IPTW weighting in individuals with psoriasis (Fig 1a) and arthritic psoriasis (Fig 1b)
DOI: 10.17632/2m99hrsckk.1
title: Microstructure refinement and enhanced mechanical properties of wire arc additively manufactured RAFM steel via post heat treatments

description: Reduced activation ferritic/martensitic (RAFM) steel with high strength and neutron radiation damage resistance is deemed one of the promising structural materials for fusion blankets. In this study, RAFM steel tube was prepared through wire arc additive manufacturing, and the evolution of phase composition, microstructural features and mechanical properties during the as-built and heat treatment processes were investigated. The results show that the as-built state is predominantly composed of α-Fe, and the microstructure’s morphology from bottom to top consists of alternating columnar grain regions, equiaxed coarse grain regions, and fine grain regions. Normalizing and tempering treatment eliminates the heterogeneous microstructure, resulting in the presence of M23C6 carbides enriched with Cr and W, as well as MX phases enriched with Ta and V. Partitioning heat treatment results in the formation of ultrafine martensitic, quenched martensite and bainitic microstructures. The mechanical properties of wire arc additively manufactured RAFM steel are influenced by variations in microstructure. The coarse-grained region formed in the as-built state represent weak links. Normalizing and tempering treatment reduces the strength while improving ductility. Partitioning heat-treated RAFM steel exhibited a high strength of 1.1 GPa and an elongation of approximately 10.9%. This study provides a method of combining additive manufacturing with heat treatment to enhance the strength and ductility of RAFM steel simultaneously.
DOI: 10.25582/data.2022-05.2514153/2124295
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507815/2124296
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514310/2124297
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2509097/2124298
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514330/2124299
title: Data for EMSL Project 51287 from May 2022
DOI: 10.34740/kaggle/dsv/6780240
title: Our World in Data - COVID-19
DOI: 10.25582/data.2022-05.2511248/2124300
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511956/2124301
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511850/2124302
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511805/2124303
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511011/2124304
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510958/2124305
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507720/2124306
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507682/2124307
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514331/2124308
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2509549/2124309
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507835/2124310
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/pkt982nxtg.1
title: Data and code for: Complex spikes perturb movements and reveal the sensorimotor map of Purkinje cells

description: Data and code for the article titled: Complex spikes perturb movements, revealing the sensorimotor map of Purkinje cells (Current Biology 2023).
DOI: 10.25582/data.2022-05.2511068/2124311
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510932/2124312
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/35jhpjzvf9.1
title: Market Conditions During Emergencies: The Pasta Market at the Onset of the Russian Federation's Invasion of Ukraine

description: Data describes the volatility of food prices (pasta) in Ukraine since the beginning of the full-scale invasion of the Russian Federation in Ukraine.
DOI: 10.25582/data.2022-05.2508102/2124313
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510662/2124314
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507675/2124315
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2512355/2124316
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/k8m75gm4m4.1
title: banka_kredi_iskko_ekonomik_aktivite_etkilesim

description: Series of article: "Banka Kredileri İle İmalat Sanayi Kapasite Kullanım Oranı İlişkisi: Ekonomik Aktivite Etkileşimi", "Relation Of Bank Credits And Manufacturing Industry Capacity Usage Ratio: Economic Activity Interaction" Turkey. The period 2014:01 to 2020:12. Interaction of preliminary data(manufacturing capacity utilization rate and bank credits), which were chosen according to the high representation ability of economic activity in Turkey were investigated. Bank credits are represented by using the Banking Regulation and Supervision Agency of Turkey(BRSA) data in the study. From the Banking Sector Data of the weekly bulletin created by the BRSA through the BRSA Data Transfer System (BVTS), Loans, Turkish Lira (TRY), and Foreign Currency (FC) are included in the study as the sum of Turkish Lira. In the study, the Total item was found by adding TRY to FC. For manufacturing capacity utilization rate, CBRT, EVDS Manufacturing Industry Capacity Utilization Ratio (%) (Weighted-NACE REV.2) series are used.
DOI: 10.25582/data.2022-05.2510800/2124317
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/px87jz88s9.1
title: Bimetallic Nacre-like structures

description: The figure data of the manuscript.
DOI: 10.25582/data.2022-05.2513069/2124318
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/x9nxffm2cz.1
title: Need based assessment study of mothers with pre-menarche daughters.

description: The present study titled “Needs based assessment study of mothers with pre-menarche daughters” is aimed at understanding the requirements of mothers of pre-menarche girls in North Bengaluru urban constituency. As a qualitative research technique, the study employed focus group discussion technique to procure data. It was a mixed group of mothers of pre-menarche girls speaking Tamil and Urdu language and practising Islam, Christianity and Hinduism. The themes and subthemes have delineated predisposing, reinforcing and enabling factors. The study has unveiled thoughts and feelings of mothers anticipating their daughter’s onset of menstruation. The study has shown the lack of biological information, misconception and misinformation that mothers have about menstruation, and as a result the inability to communicate about menstruation. Their understanding of period is based on their lived experience of menstruation, which is related to body discomfort and restrictions imposed on them at menarche and thereafter. The study displays cultural practices that will be introduced to their daughters at the time of menarche as tradition. The current study has also revealed that mothers will introduce healthy menstrual hygiene practices at menarche.
DOI: 10.25582/data.2022-05.2509528/2124319
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507755/2124320
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/vvdcjtkg8k.1
title: Three types of company and technology datasets towards a unified graph model

description: We collect three types of publicly available datasets. 1) Company Data: Initially, we collect data on promising small and medium-sized enterprises located in Gyeonggi Province from 2019 to 2021, sourced from the Gyeonggi Open Data Portal, "Gyeonggi Data Dream."  2) Technology Keyword Data: Utilizing prompt engineering with the ChatGPT service [3], we define a set of 14 initial technology keywords. Since it is insufficient to construct a graph with only 14 initial technology keywords, we extend the keywords using a patent database. We extract core technology keyword sets through query expansion using the previously defined initial technology keyword set. 3) Patent Data: We acquire patent disclosure and registration datasets from the Patent Office's KIPRIS PLUS patent information utilization service. Finally, to construct graph data, For a total of 47,385 patent data with 727 SMEs collected as applicants, the relationship between each company and the technology keywords included in the patent data is modeled as a heterogeneous graph. It consists of two types of nodes, Company and Technology, 727 and 1957 respectively, which define three types of edges. There are Use, which indicates that a particular company uses a particular technology, Share, which means that two company share common technology in their main products respectively, and Relate, which indicates that the two technologies are used in a common patent document. We generate graph data as data objects provided by the pytorch-geometric library. For the reconstruction of this data, we converted it into a form of a Pytorch Tensor and stored it as a pickle file. This can be reproduced by "torch.load('Graph.pt')" module.
DOI: 10.17632/vvdcjtkg8k.2
title: Three types of company and technology datasets towards a unified graph model

description: We collect three types of publicly available datasets. 1) Company Data: Initially, we collect data on promising small and medium-sized enterprises located in Gyeonggi Province from 2019 to 2021, sourced from the Gyeonggi Open Data Portal, "Gyeonggi Data Dream."  2) Technology Keyword Data: Utilizing prompt engineering with the ChatGPT service [3], we define a set of 14 initial technology keywords. Since it is insufficient to construct a graph with only 14 initial technology keywords, we extend the keywords using a patent database. We extract core technology keyword sets through query expansion using the previously defined initial technology keyword set. 3) Patent Data: We acquire patent disclosure and registration datasets from the Patent Office's KIPRIS PLUS patent information utilization service. Finally, to construct graph data, For a total of 47,385 patent data with 727 SMEs collected as applicants, the relationship between each company and the technology keywords included in the patent data is modeled as a heterogeneous graph. It consists of two types of nodes, Company and Technology, 727 and 1957 respectively, which define three types of edges. There are Use, which indicates that a particular company uses a particular technology, Share, which means that two company share common technology in their main products respectively, and Relate, which indicates that the two technologies are used in a common patent document. We generate graph data as data objects provided by the pytorch-geometric library. For the reconstruction of this data, we converted it into a form of a Pytorch Tensor and stored it as a pickle file. This can be reproduced by "torch.load('Graph.pt')" module.

Note that the original datasets were written in Korean. We converted them to English for reference: (eng) init_keywords.txt, (eng) expanded_keywords.txt, (eng) company_data.csv, and (eng) patents.csv.
DOI: 10.25582/data.2022-05.2514264/2124321
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2509001/2124322
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510736/2124323
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507861/2124324
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507733/2124325
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/mb4b7b92vs.1
title: RESEARCH DATA IN PHYSICAL OCEANOGRAPHY: ADCP mooring on the north coast of Rio Grande do Sul, Brazil

description: This dataset consists of wave, current, and water level data from five Nortek AWAC 1 MHz profiler moorings. In the first two moorings, the equipment was installed at a depth of 12 m, and in the last three, at 14 m. All the moorings were made on the north coast of Rio Grande do Sul, near the beach of Tramandaí, Brazil. Data were processed using Nortek Storm software. Five oceanographic moorings were made and distributed between December 2013 and September 2017. The complete methodology for collecting this research data is described in the doctoral thesis Coastal Circulation Patterns of the North Coast of Rio Grande do Sul, by Andrade (2019) [https://lume.ufrgs.br/handle/10183/188435]. ADCP data mooring can help calibrate and validate numerical models, determine dispersion patterns of pollutants, oil, and fish larvae, and calculate the potential for power generation in the northern portion of the RS coast.
DOI: 10.25582/data.2022-05.2511852/2124326
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/g3kyfkrx9y.1
title: Quercetin targets and inhibits G6PD

description: Raw data of western blotting and Mass spectrometry
DOI: 10.25582/data.2022-05.2511177/2124327
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/byrsp7tpdb.1
title: Schenberg et al. 2023

description: Data set corresponding to the preprint and eLife article: "Subchronic alteration of vestibular hair cells in mice: implications for multisensory gaze stabilization" ; doi: https://doi.org/10.1101/2023.04.19.535725 doi: https://doi.org/10.7554/eLife.88819.1
DOI: 10.17632/byrsp7tpdb.2
title: Schenberg et al. 2023

description: Data set corresponding to the eLife article: "Multisensory gaze stabilization in response to subchronic alteration of vestibular type I hair cells" ; doi: https://doi.org/10.1101/2023.04.19.535725 doi: https://doi.org/10.7554/eLife.88819.1
DOI: 10.25582/data.2022-05.2510392/2124328
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/z4y3wcpyrn.1
title: Metallic Pb in lunar zirconolite

description: BSE images of zirconolite in the lunar meteorite NWA 8182 and related characterization results
DOI: 10.25582/data.2022-05.2511658/2124329
title: Data for EMSL Project 51287 from May 2022
DOI: 10.17632/gcrknwv4zj.1
title: NMR and ETS-NOCV calculations on [Py - Ag/I - Py]+ complexes

description: ZORA and SO-ZORA DFT computations of 15N NMR chemical shift and interaction energies - input and output files for ADF2022
DOI: 10.25582/data.2022-05.2507634/2124330
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507749/2124331
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511112/2124332
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511082/2124333
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514282/2124334
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514315/2124335
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507814/2124336
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511031/2124337
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2509713/2124338
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507724/2124339
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510864/2124340
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510253/2124341
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514162/2124342
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511671/2124343
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2512417/2124344
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511654/2124345
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510830/2124346
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507824/2124347
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507758/2124348
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507995/2124349
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514091/2124350
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510924/2124351
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2513103/2124352
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510937/2124353
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507672/2124354
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511657/2124355
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511246/2124356
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510987/2124357
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514262/2124358
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514068/2124359
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511905/2124360
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514220/2124361
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511639/2124362
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2507729/2124363
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2514160/2124364
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511268/2124365
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510683/2124366
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511840/2124367
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510786/2124368
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511175/2124369
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2512383/2124370
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2512373/2124371
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510703/2124372
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511212/2124373
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511631/2124374
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2510967/2124375
title: Data for EMSL Project 51287 from May 2022
DOI: 10.25582/data.2022-05.2511651/2124376
title: Data for EMSL Project 51287 from May 2022