cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21223/p3/e0cx7j
title: Dataset for: Sweetpotato and Other Commodities Market Survey in Southern Ethiopia

description: The market survey was undertaken as part of the market monitoring by the Quality Diet for Better Health (QDBH) project in Southern Ethiopia during Oct 2017 - August 2018. Market data collection was undertaken every month at four markets (Hawassa, Dilla, Chuko and Wonago) in southern Ethiopia. This data refers to the market survey that was undertaken only Hawassa market. Hence, commodity data (quantity, number of sellers, origin of products, price per unit quantity, etc) was collected from both the open markets (where fresh roots, cereals, legumes, fresh vegetables, fruits, etc. could be sold) and retail shops (where processed food products like flours, oils, pastes, baby-lacks, etc were sold). Hence, the data included origin of commodities, quantity sold (where available), number of people selling and price per unit of commodity. For sweetpotato, there is a look up file that helps to convert the unstandardized unit (different sizes of heap) in to standard units (kg or gm). In the look up file, there is also some data on the quantity and price of sweetpotato as assemblers, wholesalers and retailers levels thus enabling to estimate the marketing margins. The data could be used to examine the seasonal variability of sweetpotato and other food commodities in terms of quantity, origin and price. The outcome of this study could help to better understand the implications of introduction and dissemination of biortified sweetpotato varieties that are rich in Vitamin in the market.
DOI: 10.21223/p3/gebr5y
title: Dataset for: Orange-Fleshed Sweetpotato Products and Sales By Urwibutso Enterprise(SINA), Rwanda

description: Ese URWIBUTSO is a privately owned and managed company that is oriented towards agro-processing industry sector, supporting more than 3,000 local farmers to grow crops and fruits. The company is located at Nyirangarama in Rulindo District, Northern Province about 45 Km north from Kigali City. Urwibutso Enterprise (SINA) therefore partnered with CIP's SUSTAIN project to connect OFSP farmers to the company is order to produce products made from OFSP for sale. The Project has provided technical support to the Enterprise. The data collected include the quantity of products sold by month and the sales values made out of those sales. Data has been collected together with the Processing unit focal person using the form provided for monitoring. Attached dataset are data from July 2014 up to December 2017
DOI: 10.21223/p3/idbfbt
title: Dataset for: Rice-Sweetpotato Rotation: Agronomic and Financial Indicators

description: To evaluate the cost benefit of the different rotation options (sweetpotato-rice, rice-rice, sweetpotato-sweetpotato) to provide basic "seed" (cuttings) in a timely manner to decentralized vine multipliers in the sweetpotato seed value chain and introduce seed of newly released rice varieties to farmers in and around the scheme. Financial data on cost of inputs, labour wages, outputs and market prices were also collected to assess the revenue to cost ratios of the sweetpotato-rice rotation compared to mono-cropping each crop.
DOI: 10.21223/p3/kovabf
title: Dataset for: Accelerated Value Chain Development (AVCD) Program - The Baseline Survey for Orange-Fleshed Sweet Potato in Bungoma, Busia Homa-Bay, and Migori Counties of Kenya

description: The baseline survey for the Accelerated Value Chain Development (AVCD) program was carried out in some randomly selected wards of Bungoma, Busia, Homa-Bay and Migori counties in March 2016. Average number of members per household was 4.5 with majority being female (56.2). Most households (78.3%) were male headed with average age of household head at 44.9 years.
DOI: 10.21223/p3/zdaahk
title: Dataset for: Feed the Future's Viable Sweetpotato Technology in Africa (VISTA) - Mozambique Nutrition Monitoring Survey 2017.

description: Nutrition activity monitoring survey was conducted to understand how the nutrition messaging is conveyed to beneficiary families who have been targeted by VISTA’s nutrition awareness building through a training, and the impact of traditional believes in improving the nutritional knowledge. Data was collected from 426 women, randomly selected women from the list of families under the supervision of community health workers in 10 districts namely: Meconta, Monapo, Rapale, Malema, Murrupula, and Mogovolas districts from Nampula province; and Alto Molocue, Gurue, Mocuba, and Gile, from Zambezia province. Four major sections were included in the survey the these are: Demographic composition of participant’s family; Nutrition and health knowledge of the caregiver; Child feeding practices, and Caregiver (defined as a woman taking care of children less than 5-years of age) and children and women food consumption (vitamin A rich food intake frequencies, and dietary diversity). Women-minimum dietary diversity (W-MDD), and Child dietary diversity (CDD) scores were computed using FANTA/WHO standard approach with recall period of 24-hours. Vitamin A rich food consumption frequency is based on 7-days recall period, using Helen Keller International (HKI) approach. VISTA project reported as the main source of information for families to introduce the types of foods rich in vitamin; followed by the health centers. This is mainly as result of cascaded nutrition education by community health worker supported by the project. In 2017 alone, the project has supported more than 1,200 community health workers (CHWs) who in turn each trained 12-15 families in their community. Access to information source, such as Radios is very low in the study area; generally, sweetpotato knowledge, is still low; however, most of the respondents heard about vitamin A. Traditional believes and perceptions still remain major bottle necks in the effort to promote consumption of nutritious food items during pregnancy and the first-1000 days of child life, more than one-quarter of the surveyed women believes that giving the first milk (colostrum) when the baby was born is bad for the child’s health; and about 10% believes eating egg during pregnancy causes child blindness.
DOI: 10.5066/f7kh0mk4
title: Federal Lands Greenhouse Gas Emissions and Sequestration in the United States: Estimates 2005-14 - Data Release

description: This dataset includes ten years of emissions and sequestration estimates (2005-2014) in two separate tables, 1) the combustion and extraction of fossil fuels on Federal lands and 2) processes from the ecosystems on those Federal lands. The fossil fuel related estimates include the greenhouse gases carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), and the ecosystems estimates include only CO2. The results are presented by State and year, including the Pacific and Gulf offshore areas in the fossil fuel associated estimates. In addition to total emissions, the estimates are broken into categories by the sector of the economy where the combustion or extraction related emissions occurred or the biologic process being quantified. Net emissions calculations, a measure of the difference between emissions and sequestration are also included. All estimates are based on public and proprietary data provided from various Federal agencies.
DOI: 10.14469/hpc/4815
title: peracid epoxidation of aminoethene, IRC

description: Gaussian Calculation
DOI: 10.14469/hpc/4816
title: Data set for PONE-D-17-33558R2

description: This is the data set for the PloS One paper: Giuseppe Cotugno, Jelizaveta Konstantinova, Kaspar Althoefer, Thrishantha Nanayakkara, âModelling the Structure of Object-Independent Human Affordances of Approaching to Grasp for Robotic Handsâ, accepted in Plos One, PONE-D-17-33558R2.
DOI: 10.5281/zenodo.1503774
title: Systematic Review on the Research Topic "Learning Ecologies" - Dataset and Analysis

description: The concept of learning ecologies (Barron, 2004, 2006; Williams, 2011) emerged in a context of educational change. In such context, there was emphasis on searching models and applications facing the new forms of learning as continuum from structured, formal spaces to fluid, informal spaces, with the support of digital technologies (Sangra, Gonzalez-Sanmamed, & Guitert, 2013; Tabuenca, Ternier, & Specht, 2013). While the construct “learning ecologies” has offered a broad semantic space to characterize innovative ways of learning, it is also true that its potential to promote innovative educational interventions could have been hindered by its same broadness. According to this assumption, the authors have carried out a systematic review of the literature with the aim of analyzing a) the several definitions given to the concept, encompassing the underlying ontological perspective on the phenomena studied; b) the methodological approaches adopted to study the phenomenon; c) the applications of the research on this topic. Throughout this analysis, the authors attempt to represent the criticalities of the existing research, as well as the potential areas of development with fruitful alignment of the theoretical/ontological issues, the methodological approaches and the educational applications. The current dataset introduces all the stages of data analysis: from the 337 articles identified (covering the period 1991-2018, containing the key term “learning ecolog*” and selected from 5 relevant scientific databases in the area of education: SCOPUS, ISI WOS, DOAJ, EDITLIB, ERIC) to the final group of 84 papers which were analyzed. Each article was coded according to a set of 20 categories developed by the authors on theoretical basis. The dataset presents: A) The Codebook with the categories used to code each article B) The first, raw selection of articles from each database C) The second integrated dataset will the articles merged, without overlaps D) The third final database where the articles were coded; each row reprents an article. Data filters have been applied to facilitate dynamic searches and explore the categories. E) The PRISMA workflow, with the data extracted from A,B,C F) The tables adopted to make the Kappa calculations G) Some pivot tables with data analysis extracted D
description: Grant: Spanish Ministry of Economy and Competitiveness I+D (EDU2012-37334); the project "Cómo aprenden los mejores docentes universitarios en la era digital: Impacto de las ecologías de aprendizaje en la calidad de la enseñanza (ECO4LEARN-HE)
DOI: 10.5281/zenodo.1503775
title: Systematic Review on the Research Topic "Learning Ecologies" - Dataset and Analysis

description: The concept of learning ecologies (Barron, 2004, 2006; Williams, 2011) emerged in a context of educational change. In such context, there was emphasis on searching models and applications facing the new forms of learning as continuum from structured, formal spaces to fluid, informal spaces, with the support of digital technologies (Sangra, Gonzalez-Sanmamed, & Guitert, 2013; Tabuenca, Ternier, & Specht, 2013). While the construct “learning ecologies” has offered a broad semantic space to characterize innovative ways of learning, it is also true that its potential to promote innovative educational interventions could have been hindered by its same broadness. According to this assumption, the authors have carried out a systematic review of the literature with the aim of analyzing a) the several definitions given to the concept, encompassing the underlying ontological perspective on the phenomena studied; b) the methodological approaches adopted to study the phenomenon; c) the applications of the research on this topic. Throughout this analysis, the authors attempt to represent the criticalities of the existing research, as well as the potential areas of development with fruitful alignment of the theoretical/ontological issues, the methodological approaches and the educational applications. The current dataset introduces all the stages of data analysis: from the 337 articles identified (covering the period 1991-2018, containing the key term “learning ecolog*” and selected from 5 relevant scientific databases in the area of education: SCOPUS, ISI WOS, DOAJ, EDITLIB, ERIC) to the final group of 84 papers which were analyzed. Each article was coded according to a set of 20 categories developed by the authors on theoretical basis. The dataset presents: A) The Codebook with the categories used to code each article B) The first, raw selection of articles from each database C) The second integrated dataset will the articles merged, without overlaps D) The third final database where the articles were coded; each row reprents an article. Data filters have been applied to facilitate dynamic searches and explore the categories. E) The PRISMA workflow, with the data extracted from A,B,C F) The tables adopted to make the Kappa calculations G) Some pivot tables with data analysis extracted D
description: Grant: Spanish Ministry of Economy and Competitiveness I+D (EDU2012-37334); the project "Cómo aprenden los mejores docentes universitarios en la era digital: Impacto de las ecologías de aprendizaje en la calidad de la enseñanza (ECO4LEARN-HE)
DOI: 10.5281/zenodo.1506172
title: Co-citations Map: Literature review on Learning Ecologies, 1991-2018

description: The co-citation map was adopted as type of analysis over 85 papers sampled from five scientific databases (pls. cfr: Systematic Review on the Research Topic "Learning Ecologies" - Dataset and Analysis". This was adopted as method to further understand the relationships and advancement of work relating the concept of LE. The software tool to carry out this phase of the study was Citenet Explorer for the analysis and visualization of co-citations along the period 1999-2018. The total number of citations and the relationships between most cited authors and all authors were extracted from the corpus analyzed (Systematic Review on the Research Topic "Learning Ecologies" - Dataset and Analysis"; a specific dataset was created and the outputs were processed by the specialized software that delivers the bibliometric maps as output. All the instructions to use the two files that compose the dataset requested by the software, as well as the software specifications can be found at CiteNet Explore page: http://www.citnetexplorer.nl/ Finally, the Fig. 7 with the analysis we performed shows the co-citations map, where at a first sight it is possible to see two main groups of nodes or authors cited (X axis) across a timespan (Y axis), and sparse elements at the center and at the beginning of the period (1991). The main and more compact group (also in terms of clusterisation of nodes, which are showed in green) related the publications that cite the seminal work of Barron (2004). These publications are mostly classified as using socio-constructivist theories and are placed in the area of social sciences, while for other categories (such as the methodological approach, research applications and the alignment are more fragmented). There are four seminal works (Abd-El-Khalick & Akerson, 2004; Barron, 2004; T ; Okamoto, Kayama, Inoue, & Cristea, 2002; Toshio Okamoto & Kayama, 2004) to which other papers can be connected. Beyond the mentioned work of Barron, the other three works can be placed in the area of technology (development of eLearning environments) and STEM education, supporting the idea of disciplinary fragmentation.
description: Grant: Spanish Ministry of Economy and Competitiveness I+D (EDU2012-37334); the project "Cómo aprenden los mejores docentes universitarios en la era digital: Impacto de las ecologías de aprendizaje en la calidad de la enseñanza (ECO4LEARN-HE)
DOI: 10.5281/zenodo.1506173
title: Co-citations Map: Literature review on Learning Ecologies, 1991-2018

description: The co-citation map was adopted as type of analysis over 85 papers sampled from five scientific databases (pls. cfr: Systematic Review on the Research Topic "Learning Ecologies" - Dataset and Analysis". This was adopted as method to further understand the relationships and advancement of work relating the concept of LE. The software tool to carry out this phase of the study was Citenet Explorer for the analysis and visualization of co-citations along the period 1999-2018. The total number of citations and the relationships between most cited authors and all authors were extracted from the corpus analyzed (Systematic Review on the Research Topic "Learning Ecologies" - Dataset and Analysis"; a specific dataset was created and the outputs were processed by the specialized software that delivers the bibliometric maps as output. All the instructions to use the two files that compose the dataset requested by the software, as well as the software specifications can be found at CiteNet Explore page: http://www.citnetexplorer.nl/ Finally, the Fig. 7 with the analysis we performed shows the co-citations map, where at a first sight it is possible to see two main groups of nodes or authors cited (X axis) across a timespan (Y axis), and sparse elements at the center and at the beginning of the period (1991). The main and more compact group (also in terms of clusterisation of nodes, which are showed in green) related the publications that cite the seminal work of Barron (2004). These publications are mostly classified as using socio-constructivist theories and are placed in the area of social sciences, while for other categories (such as the methodological approach, research applications and the alignment are more fragmented). There are four seminal works (Abd-El-Khalick & Akerson, 2004; Barron, 2004; T ; Okamoto, Kayama, Inoue, & Cristea, 2002; Toshio Okamoto & Kayama, 2004) to which other papers can be connected. Beyond the mentioned work of Barron, the other three works can be placed in the area of technology (development of eLearning environments) and STEM education, supporting the idea of disciplinary fragmentation.
description: Grant: Spanish Ministry of Economy and Competitiveness I+D (EDU2012-37334); the project "Cómo aprenden los mejores docentes universitarios en la era digital: Impacto de las ecologías de aprendizaje en la calidad de la enseñanza (ECO4LEARN-HE)
DOI: 10.5281/zenodo.1495069
title: Synthetically Spoken STAIR

description: This dataset consists of synthetically spoken captions for the STAIR dataset. Following the same methodology as Chrupała <em>et al.</em> (see article | dataset | code) we generated speech for each caption of the STAIR dataset using Google's Text-to-Speech API. This dataset was used for visually grounded speech experiments (see article accepted at ICASSP2019). <pre><code>@INPROCEEDINGS{8683069, author={W. N. {Havard} and J. {Chevrot} and L. {Besacier}}, booktitle={ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Models of Visually Grounded Speech Signal Pay Attention to Nouns: A Bilingual Experiment on English and Japanese}, year={2019}, volume={}, number={}, pages={8618-8622}, keywords={information retrieval;natural language processing;neural nets;speech processing;word processing;artificial neural attention;human attention;monolingual models;part-of-speech tags;nouns;neural models;visually grounded speech signal;English language;Japanese language;word endings;cross-lingual speech-to-speech retrieval;grounded language learning;attention mechanism;cross-lingual speech retrieval;recurrent neural networks.}, doi={10.1109/ICASSP.2019.8683069}, ISSN={2379-190X}, month={May},}</code></pre> The dataset comprises the following files : <strong>mp3-stair.tar.gz</strong> :<strong> </strong>MP3 files of each caption in the STAIR dataset. Filenames have the following pattern <em>imageID_captionID</em>, where both <em>imageID</em> and <em>captionID </em>correspond to those provided in the original dataset (see annotation format here) <strong>dataset.mfcc.npy</strong> : Numpy array with MFCC vectors for each caption. MFCC were extracted using python_speech_features with default configuration. To know to which caption the MFCC vectors belong to, you can use the files dataset.words.txt and dataset.ids.txt. <strong>dataset.words.txt</strong> : Captions corresponding to each MFCC vector (line number = position in Numpy array, starting from 0) <strong>dataset.ids.txt </strong>: IDs of the captions (<em>imageID_captionID</em>) corresponding to each MFCC vector (line number = position in Numpy array, starting from 0) Splits test <strong>test.txt </strong>: captions comprising the test split <strong>test_ids.txt</strong>: IDs of the captions in the test split <strong>test_tagged.txt </strong>: tagged version of the test split <strong>test-alignments.json.zip </strong>: Forced alignments of all the captions in the test split. (dictionary where the key corresponds to the caption ID in the STAIR dataset). <em>Due to an unknown error during upload, the JSON file had to be zipped...</em> train <strong>train.txt </strong>: captions comprising the train split <strong>train_ids.txt </strong>: IDs of the captions in the train split <strong>train_tagged.txt </strong>: tagged version of the train split val <strong>val.txt </strong>: captions comprising the val split <strong>val_ids.txt </strong>: IDs of the captions in the val split <strong>val_tagged.txt </strong>: tagged version of the val split