cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5d054
title: MassIVE MSV000083551 - Proteomic analysis of synovial fluid identifies periostin as a biomarker for anterior cruciate ligament injury
title: Identification of novel synovial fluid biomarkers that can differentiate early changes following ACL injury from OA using tandem-mass-spectrometry
DOI: 10.17632/9fcbj4y2rp.1
title: Do Market Shocks Generate Gender Differentiated Impacts? Policy Implications from a Quasi-Natural Experiment in a Developing Country

description: This dataset is constructed from two successive Household Income and Expenditure Surveys (HIES) conducted in 2005 and 2010 by the Bangladesh Bureau of Statistics (BBS). After independence in 1971, the BBS conducted the first round of HIES in 1973-74. Since then until the HIES 2010 rounds, the BBS has successfully conducted 15 rounds of HIES, but information on consumption was not included until 2000 round. Since the HIES 2000, BBS started including detailed information on household incomes and particularly expenditures on food and non-food items. The expenditure on food and nonfood items includes both in-kind expenditures in the form of the monetary value of the self-produced item and non-farm food bought from the market. With the financial and technical support of the World Bank, the BBS uses a two-stage stratified random sampling process in which, in the first stage, the BBS selects primary sampling units (PSUs) consisting of specific geographical areas both in the rural and urban areas, and in the second stage, randomly selects 20 households from each PSU. The HIES 2005 survey included 504 PSUs and 10,080 households randomly selected from 8 divisions, 64 districts and 364 sub-districts. Finally, the HIES 2010 survey randomly selected 1000 PSUs and 12,240 households from all nine administrative divisions, 64 districts and 372 sub-districts. However, out of the aggregate 22,320 sampled households across two waves, nine households had to drop for the lack of information on food and non-food expenditures. The present study is thus based on information collected from 22,311 households in 2005 and 2010, of which 9,036 were from urban areas, and 13,275 were from rural areas; females headed 7,789 households and males headed 19,522.
DOI: 10.17632/f8nvjrz5mx.1
title: Raw data

description: Raw data files for "Mechanisms of TiB2 and graphite nucleation during TiC-B4C high temperature interaction" by O. Popov1, V. Vishnyakov2, S. Chornobuk3, I. Totsky1, I. Plyushchay 1, 1 Faculty of Physics, Taras Shevchenko National University of Kiev, Hlushkov Ave. 4, Kiev 03187, Ukraine 2 Institute for Materials Research, University of Huddersfield, HD1 3DH, UK 3Science and Research Centre “Synthesis”, Kiev 02139, Ukraine
DOI: 10.17632/fv632kj9r5.2
title: direct current transmssion and reflection spectra of organic photovoltaic materials in the 110-160 GHz range

description: These are voltage datasets that also have standard deviation during acquisition. data structured as sweeper probe frequency, voltage (no sample), standard deviation, voltage (with sample), standard deviation of voltages through or reflected off sample
DOI: 10.17632/mcnfncf25t.1
title: Matlab scripts for Lane tracking and band detection on the Pulsed-Field Gel Electrophoresis images

description: Here a matlab script was presented for lane tracking and band detection on the pulsed field gel electrophoresis (PFGE) images. it can also be used as a software tool for automatic analysis of PFGE images.
DOI: 10.17632/6b8vbvmsjr.1
title: Processed data

description: Processed data for Mechanisms of TiB2 and graphite nucleation during TiC-B4C high temperature interaction O. Popov1, V. Vishnyakov2, S. Chornobuk3, I. Totsky1, I. Plyushchay 1, 1 Faculty of Physics, Taras Shevchenko National University of Kiev, Hlushkov Ave. 4, Kiev 03187, Ukraine 2 Institute for Materials Research, University of Huddersfield, HD1 3DH, UK 3Science and Research Centre “Synthesis”, Kiev 02139, Ukraine
DOI: 10.17632/6v6pdcc3xz.1
title: Biogas Adoption and Elucidating Its Impacts in India: Implications for Policy

description: Datasets consists of the data collected by the National Sample Survey (NSS) Organization, India, NSS 46th round collected in 1990-91; NSS 56th round collected in 2000-01; NSS 63rd round collected in 2006-07; and NSS 68th round collected in 2011-12. Since the inception of the National Sample Survey Organization (NSSO) in 1950, the Institute collects information on consumer expenditure, employment, and unemployment on a regular basis. To enhance precision in data collection, NSSO implements a stratified multi-stage sampling designing approach. In the NSS 46th round of data (1990-91), out of the 28,533 surveyed households, only 57 reportedly used biogas for cooking. The number of households that supposedly used biogas for cooking in the NSS 56th round of data (2000-01) was 160, out of the total number of households surveyed which was 81,500. Similarly, in the NSS 63rd round of data (2006-07), 63,729 households were surveyed, but only 129 reportedly used biogas, and in the NSS 68th round of data (2011-12), 101,662 households were surveyed, and 148 households reportedly used biogas (Table 2). It shows that the sub-sample of the households that reportedly used biogas are only a few among the total sampled households in the sampled NSS rounds. The econometric estimation process in characterizing the households that use biogas may not converge due to the non-proportional number of users and non-users of biogas among the households sampled. To solve the problem, we have randomly grouped the sampled households in each NSS round into 100 sub-groups, and chose only the sub-sample in the first group, and merged them with the sub-sample of the households who reportedly used biogas. In the process, for example in the case of the NSS 46th round of data, we have divided 28,476 households into 100 random groups. Each group consists of 284 households. We merged the 57 biogas-user households with 284 randomly chosen households from 28,476 total sampled households of NSS 46th round. Thus, the process provides us with 341 sub-sample households from the NSS 46th round of data. In the same process, we have considered 965 sub-samples from 81,500 sampled households from the NSS 56th round data; 762 sub-samples from 63,729 sampled households from the NSS 63rd round of data and 1,016 sub-samples from 101,662 sampled households from the NSS 68th round of data (Table 2). This study, thus, relies on information collected from sub-samples of 3,232 households from India, of which 1,764 (54.6%) were from rural areas, and 1,468 (45.4%) were from the urban areas
DOI: 10.17632/b56r5xyb4y.1
title: Changing Food Consumption of the Households in Developing Countries: A Bangladesh Case

description: The dataset primarily consists of Bangladesh’s Household Income and Expenditure Survey (HIES) data collected in 2000, 2005 and 2010 by the Bangladesh Bureau of Statistics (BBS). The BBS uses a two-stage stratified random sampling to ensure maximum precision in the data collection process in each survey round. In the first stage, the BBS selects primary sampling units (PSUs) consisting of specific geographical areas, and in the second stage, it randomly selects 20 households from each PSU that represent rural, urban, and statistical metropolitan areas (SMAs). The present study is based on information collected from a total of 29,676 households surveyed in 2000, 2005 or 2010, of which 9,183 were from urban areas and the remaining 20,493 were from rural areas. The HIES 2000, 2005 and 2010 data on household-level consumption is quite detailed. The consumption of food items in quantity and expenditure was divided into 17 major food categories and collected all consumption information for two-week period. The major categories were cereals, pulses, fish, eggs, meat, vegetables, milk and dairy products, sweets, oil and fats, fruits, drinks, sugar and molasses, tobacco and related items, spices, betel leaves and betel nuts. In the cereal category, there were sub-categories of rice, wheat, and processed rice and wheat products. As the major focus of this study is the examination of the trends of cereal consumption patterns over time by households in a rapidly-growing developing country, this study analyzed the consumption of five basic and commonly-consumed food items: rice and wheat (both including grain and processed products), pulses, fish and vegetables.
DOI: 10.17632/hd4dd65762.1
title: The Water Footprint in Bioenergy—A Comparison of Four Biomass Sources to Produce Biofuels in Argentina: Energy and Building Sectors

description: In Argentina, the use of biomass for the production of biofuels capable of replacing fossil fuels has aroused great expectation. In the last decade, the production of biodiesel has tripled, and the rising trend continues supported by national legislation that soon will increase the blend mandates up to B20 and E25 (being now B10 and E12). However, there could be some environmental concerns associated with land use and especially with water use. In this chapter, we calculate the volume of water used and consumed in the production of rapeseed (Brassica napus) and soybean (Glycine max) for biodiesel, and the production of sugarcane (Saccharum officinarum) and cordgrass (Spartina argentinensis) for bioethanol in different regions of the country. The water footprint, as defined by the Water Footprint Network, is used as an indicator of water resources appropriation, and the ISO approach is followed to assess the impacts associated with the use of water. The volume of water associated with the production of cordgrass is lower than figures obtained by traditional sources of biomass in Argentina (soybean and sugarcane). Soybean is produced in the Pampean Region and it is recommended to optimize the management of water resources in that region to minimize competition with food products while on the opposite, there is the case of Spartina that is a native grass growing naturally in the Chaco Region and it uses water that does not compete with food or livestock feed. On the other hand, rapeseed has a high water footprint mainly as a consequence of the site where it is produced. For instance, considering the environmental fragility, it is recommended to avoid the production of biomass destined to bioenergy in the arid zones of the country. Therefore, our findings show that results are more dependent on the region where each biomass is grown, than on management practices or the amount and type of chemical inputs (fertilizers, pesticides). Further work, such as the accounting of water along the industrial phase of the biofuels, is needed to have the full picture of the water consumption issue in bioenergy production.
DOI: 10.17632/zvmt38vy3n.1
title: Evolving food consumption patterns of rural and urban households in developing countries: A Bangladesh case

description: This dataset is consists of Bangladesh’s Household Income and Expenditure Survey (HIES) data collected in 2000, 2005 and 2010 by the Bangladesh Bureau of Statistics (BBS). In the first stage, the BBS selects primary sampling units (PSUs) consisting of specific geographical locations, and in the second stage, it randomly selects 20 households from each PSU that represent rural and urban areas. In HIES 2000, BBS selected 442 primary sampling units and surveyed 7,440 households of which 5,063 were from the rural areas and the rest were from urban areas. In HIES 2005, BBS selected 504 primary sampling units and surveyed 10,080 households of which 6,040 were from the rural areas, and the rest were from urban areas. In HIES 2010, 1,000 PSUs were selected and they surveyed 12,240 households of which 7,840 were from the rural areas and the rest were from urban areas. The HIES 2000, 2005 and 2010 data on household-level consumption is quite detailed. The consumption of food items both in quantity and expenditure was divided into 17 major food categories. The households were asked to describe their consumption habits during a two-week period. The major food categories were cereals, pulses, fish, eggs, meat, vegetables, milk and dairy products, sweets, oil and fats, fruits, drinks, sugar and molasses, tobacco and related items, spices and betel leaves, including betel nuts. An analysis of a complete demand system consisting of separate equations for all items in 17 major food categories would require a substantial amount of computer memory and time. For simplicity, the present study analyzed the demand systems considering a subset of food items commonly consumed by the sampled households: cereals, pulses, fish and vegetables. Among the cereals, rice (grain), and wheat (flour) and other rice and wheat products, such as rice cakes, biscuits and miscellaneous rice and wheat products are separately treated. As the five food categories are the most commonly-consumed items, and as the others are sporadically-consumed food items, policy implications based on the present study can provide deep insights into the evolution of the basic food consumption pattern in Bangladesh by the rural-urban affiliation of the households. Note that in HIES 2000 the expenditure information on vegetables for 84 households was missing; in HIES 2005 information on vegetable expenditure for 783 households was missing, and such information was missing for 1,229 households in HIES 2010. Thus, the dataset includes 28,384 households from HIES 2000, 2005 and 2010, of which 19,471 are rural households and 8,913 are urban households.
DOI: 10.5281/zenodo.2590589
title: A dataset of regional operational programmes (ROP) and rural development program (PROW) expenditures and socio-economic features in 2007-2013, Poland (source: Bank of Local Data)

description: Dataset prepared on the bases of Polish Central Statistical Office (Statistics Poland) Bank of Local Data system https://bdl.stat.gov.pl/BDL/dane/podgrup/tablica [access: 1.07.2018]. The data set the expenditure of funds for individual priority axes in the programmes of both policies in the 2007-2013 programming period and the change in socio-economic features at the local (<em>poviat</em>, NUTS4) level. The Pearson correlation coefficients are used to assess the relationship between the level of expenditure for RDP and ROP <em>per capita</em> and selected indicators describing the level of economic, social and demographic development of local government units. The results of the analysis (the article <strong>Regional approach to rural development? A case of regional and rural programs 2007-2015 in Poland) </strong>will consist of tables, texts and of numerical data. Article with data is available here: OI: 10.5604/01.3001.0012.2934 GICID: 01.3001.0012.2934 Available language versions: en. <strong>Issue: </strong>Annals PAAAE 2018; XX (4): 22-28, https://rnseria.com/resources/html/article/details?id=176837
DOI: 10.5281/zenodo.2590590
title: A dataset of regional operational programmes (ROP) and rural development program (PROW) expenditures and socio-economic features in 2007-2013, Poland (source: Bank of Local Data)

description: Dataset prepared on the bases of Polish Central Statistical Office (Statistics Poland) Bank of Local Data system https://bdl.stat.gov.pl/BDL/dane/podgrup/tablica [access: 1.07.2018]. The data set the expenditure of funds for individual priority axes in the programmes of both policies in the 2007-2013 programming period and the change in socio-economic features at the local (<em>poviat</em>, NUTS4) level. The Pearson correlation coefficients are used to assess the relationship between the level of expenditure for RDP and ROP <em>per capita</em> and selected indicators describing the level of economic, social and demographic development of local government units. The results of the analysis (the article <strong>Regional approach to rural development? A case of regional and rural programs 2007-2015 in Poland) </strong>will consist of tables, texts and of numerical data. Article with data is available here: OI: 10.5604/01.3001.0012.2934 GICID: 01.3001.0012.2934 Available language versions: en. <strong>Issue: </strong>Annals PAAAE 2018; XX (4): 22-28, https://rnseria.com/resources/html/article/details?id=176837
DOI: 10.17632/4z4n7p5gxt.1
title: Data for thermal response measurement in C. atrata and H. fuscata (Hemiptera: Cicadidae)
DOI: 10.17632/jkmrdz3kn8.2
title: Data for Tactile angle discriminability improvement: roles of training time intervals and different types of training tasks

description: Supplemental data. This data is related to Wang et. al 2019.
DOI: 10.17632/ym798wy75g.3
title: Quantum-classical mechanics: Luminescence spectra in polymethine dyes and J-aggregates. Nature of the small Stokes shift_

description: Mathcad program files validating the results in figures 9-15.
DOI: 10.6073/pasta/b331efa1734c5793978dbe844b981349
title: Arbuscular mycorrhizal fungi and dark septate endophytes root colonization in Upper Green Lakes Valley, 2007-2016

description: Arbuscular mycorrhizal fungi (AMF) and dark septate endophytes (DSE)
  are two fungal groups that colonize plant roots and can benefit plant
  growth, but little is known about their landscape distributions. We
  performed sequencing and microscopy on a variety of plants across a
  high-elevation landscape featuring plant density, snowpack, and
  nutrient gradients. Percent colonization by both AMF and DSE varied
  significantly among plant species, and DSE colonized forbs and grasses
  more than sedges. AMF were more abundant in roots at lower elevation
  areas with lower snowpack and lower phosphorus and nitrogen levels,
  suggesting increased hyphal recruitment by plants to aid in nutrient
  uptake. DSE colonization was highest in areas with less snowpack and
  higher inorganic nitrogen levels, suggesting an important role for
  these fungi in mineralizing organic nitrogen. Both of these groups of
  fungi are likely to be important for plant fitness and establishment
  in areas limited by phosphorus and nitrogen.
DOI: 10.17632/4k6knxb996.3
title: Translational and HIF-1a-dependent metabolic reprograming underpin metabolic plasticity and determine responses to kinase inhibitors and biguanides. Hulea, Gravel, Morita et al.

description: Original data
DOI: 10.17632/7p6rky666j.1
title: Data for: Does energy price affect energy efficiency? Cross-country panel evidence

description: Dataset used for the article Does energy price affect energy efficiency? Cross-country panel evidence. Dataset: Full dataset. Data source: our elaboration from IEA; Inflationdata.com; Wold Bank. Variables names have been explained in the article.
DOI: 10.17632/bhz5ywntn4.1
title: Data for: Does energy price affect energy efficiency? Cross-country panel evidence

description: STATA 15 do file of the article: Does energy price affect energy efficiency? Cross-country panel evidence
DOI: 10.17632/dn8rj26kzm.1
title: Deep Green Diagnostics

description: The following dataset was created to cover the necessity of getting images of urban areas to estimate the health vegetation using a drone to 30 hight meters. The images were taken with a drone Phantom 4 of the DJI family and cut them to get a size of 200x200px. Into the pictures, there are different types of vegetation such as grass, trees of different size, and shrubs. Also, streets, buildings, cars, people and other types of objects commonly present in these areas. Contamination is an agent that is almost always found in all of the cities, so we consider as contamination as all the garbage that could be found in an urban environment, such as waste of food, empty containers of sweets or chips, plastic bottles, and other things.
DOI: 10.17632/f727rc78wp.1
title: Articulos

description: Articulos
DOI: 10.17632/jytxxbrrgt.1
title: Data for: Does energy price affect energy efficiency? Cross-country panel evidence

description: Dataset used for the article Does energy price affect energy efficiency? Cross-country panel evidence. Dataset: Restricted dataset for Difference GMM Data source: our elaboration from IEA; Inflationdata.com; Wold Bank. Variables names have been explained in the article.
DOI: 10.17632/t4852mbbp9.1
title: Data for: Does energy price affect energy efficiency? Cross-country panel evidence

description: Dataset used for the article Does energy price affect energy efficiency? Cross-country panel evidence. Dataset: dataset to perform the structural break test Data source: our elaboration from IEA; Inflationdata.com; Wold Bank. Variables names have been explained in the article.
DOI: 10.3886/e108566v1-10802
title: Multiplex Imbalance

description: We include code used to parse and analyze data, as well as the data used to reconstruct multiplex weighted and signed networks. Finally, we include features that are correlates of dyadic imbalance and useful for Heckman selection in sparse international relations networks.
DOI: 10.17632/2syjyt5d3b.2
title: Data for: Reactive spark plasma sintering and thermoelectric properties of Nd-substituted BiCuSeO oxyselenides doi: 10.1016/j.jallcom.2019.01.183

description: It contains some raw data obtained during the study "Reactive spark plasma sintering and thermoelectric properties of Nd-substituted BiCuSeO oxyselenides", https://doi.org/10.1016/j.jallcom.2019.01.183
DOI: 10.17632/dn3v95wcdr.1
title: Weights and biases of ANN trained by hybrid of EHO and LM-BP

description: Even though ANN trained by Levenberg-Marquardt Back-Propagation (LM-BP) provided good results, it was observed that the performance would fluctuate on each trail and also gives poor performance at times, due to getting stuck in local optima. Elastic Modulus being a very sensitive parameter of structural concrete, it would be risky to adopt such a model. Here, ANN is trained by swarm-based optimization technique namely, Elephant Herding Optimization (EHO). Further, the weights and biases are exported and fed as pre-defined weights and biases for ANN trained by LM-BP. This takes advantage of complimentary features of either technique and improvises results. It was also observed that the performance was consistent over various trails and provides a good alternative for predicting elastic modulus of recycled aggregate concrete. The various weights and biases of ANN trained by hybrid of EHO and LM-BP as they pass through the network with two Hidden layers are given in the dataset.
DOI: 10.17632/dn3v95wcdr.2
title: Weights and biases of ANN trained by hybrid of EHO and LM-BP

description: Even though ANN trained by Levenberg-Marquardt Back-Propagation (LM-BP) provided good results, it was observed that the performance would fluctuate on each trail and also gives poor performance at times, due to getting stuck in local optima. Elastic Modulus being a very sensitive parameter of structural concrete, it would be risky to adopt such a model. Here, ANN is trained by swarm-based optimization technique namely, Elephant Herding Optimization (EHO). Further, the weights and biases are exported and fed as pre-defined weights and biases for ANN trained by LM-BP. This takes advantage of complimentary features of either technique and improvises results. It was also observed that the performance was consistent over various trails and provides a good alternative for predicting elastic modulus of recycled aggregate concrete. The various weights and biases of ANN trained by hybrid of EHO and LM-BP as they pass through the network with two Hidden layers are given in the dataset.
DOI: 10.17632/p5gj69vrw3.1
title: Data on the the process of CFL learners solving Chinese logogriphs

description: The data presented here are related to the research paper entitled “The influence of cultural backgrounds and language proficiencies on Chinese logogriph solving”, showing the types and frequencies of logogriph-solving strategies used by Chinese as a foreign language (CFL) learners with different Chinese proficiencies and cultural backgrounds. Subjects answered the logogriphs through testing papers, and wrote down how they solved those puzzles during the process. The data were collected, calculated and checked by the authors and two researchers who did not involve in this experiment, and can be used to examine how CFL learners comprehend and process Chinese logogriphs.