Understanding which trees farmers prefer, what determines their survival and enhancing farmer knowledge of tree management is key to increasing tree cover in agricultural landscapes. This article presents data on tree seedling survival under different tree planting and management practices in Kenya and Ethiopia. Data were collected from 1600 households across three Counties in Kenya and 173 households across four Woredas in Ethiopia, using a structured questionnaire which was administered through the Open Data Kit. Data on seedling survival were collected at least six months after tree seedlings were planted. To understand how planting and management practices influence tree planting across the different socioeconomic and biophysical contexts, both household level and individual tree level data were collected. Household level data included socio-economic and biophysical characteristics of the households while tree specific data included when the tree seedling was planted, where it was planted, the management practices employed and whether surviving. The datasets described in this article help understand which options confer the best chance survival for the planted seedlings and in which socio-economic and biophysical contexts they are most successful.

Understanding which trees farmers prefer, what determines their survival and enhancing farmer knowledge of tree management is key to increasing tree cover in agricultural landscapes. This article presents data on tree seedling survival under different tree planting and management practices in Kenya and Ethiopia. Data were collected from 1600 households across three Counties in Kenya and 173 households across four Woredas in Ethiopia, using a structured questionnaire which was administered through the Open Data Kit. Data on seedling survival were collected at least six months after tree seedlings were planted. To understand how planting and management practices influence tree planting across the different socioeconomic and biophysical contexts, both household level and individual tree level data were collected. Household level data included socio-economic and biophysical characteristics of the households while tree specific data included when the tree seedling was planted, where it was planted, the management practices employed and whether surviving. The datasets described in this article help understand which options confer the best chance survival for the planted seedlings and in which socio-economic and biophysical contexts they are most successful.

Understanding which trees farmers prefer, what determines their survival and enhancing farmer knowledge of tree management is key to increasing tree cover in agricultural landscapes. This article presents data on tree seedling survival under different tree planting and management practices in Kenya and Ethiopia. Data were collected from 1600 households across three Counties in Kenya and 173 households across four Woredas in Ethiopia, using a structured questionnaire which was administered through the Open Data Kit. Data on seedling survival were collected at least six months after tree seedlings were planted. To understand how planting and management practices influence tree planting across the different socioeconomic and biophysical contexts, both household level and individual tree level data were collected. Household level data included socio-economic and biophysical characteristics of the households while tree specific data included when the tree seedling was planted, where it was planted, the management practices employed and whether surviving. The datasets described in this article help understand which options confer the best chance survival for the planted seedlings and in which socio-economic and biophysical contexts they are most successful.

The dataset shows the relationship and valuation of the coastal dunes of the Araucanía region in Chile. The valuation of the local population was surveyed using a questionnaire applied to 49 subjects belonging to Mapuche communities and local government. The data consists of eight tables that show a list of questions, the number of times per year that visit the dunes, cultural practices carried out in the dunes, valuation of ecosystem services provided by the coastal dunes, and knowledge about flora and fauna. Lastly, the original questionnaire and its responses in Spanish and English are included in supplemantary material. This dataset was generated within the framework of the manuscript Ecosystem services and uses of dune systems of the coast of the Araucanía Region, Chile: a perception study where 23 leaders of Mapuche communities and 26 representatives of the local government were interviewed. The dataset can be used to compare the valuation of ecosystems by local communities, especially when quantitative data are scarce or do not exist. © 2021 Elsevier B.V., All rights reserved.

The dataset shows the relationship and valuation of the coastal dunes of the Araucanía region in Chile. The valuation of the local population was surveyed using a questionnaire applied to 49 subjects belonging to Mapuche communities and local government. The data consists of eight tables that show a list of questions, the number of times per year that visit the dunes, cultural practices carried out in the dunes, valuation of ecosystem services provided by the coastal dunes, and knowledge about flora and fauna. Lastly, the original questionnaire and its responses in Spanish and English are included in supplemantary material. This dataset was generated within the framework of the manuscript “Ecosystem services and uses of dune systems of the coast of the Araucanía Region, Chile: a perception study” where 23 leaders of Mapuche communities and 26 representatives of the local government were interviewed. The dataset can be used to compare the valuation of ecosystems by local communities, especially when quantitative data are scarce or do not exist.

Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm. The dataset presented in this article consists of 332 manure samples from poultry and cattle, sampled from farms located in major regions of livestock production in mainland France and Reunion Island. The samples were analysed for seven chemical properties following conventional laboratory methods. NIR spectra were acquired with three spectrometers from fresh homogenized and dried ground samples and then standardized using the PDS algorithm. This important dataset can be used to train and test chemometric models and is of particular interest to NIR spectroscopists and agronomists who assess the agronomic value of animal waste.

Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm.The dataset presented in this article consists of 332 manure samples from poultry and cattle, sampled from farms located in major regions of livestock production in mainland France and Reunion Island. The samples were analysed for seven chemical properties following conventional laboratory methods. NIR spectra were acquired with three spectrometers from fresh homogenized and dried ground samples and then standardized using the PDS algorithm. This important dataset can be used to train and test chemometric models and is of particular interest to NIR spectroscopists and agronomists who assess the agronomic value of animal waste.

International audience | Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm.The dataset presented in this article consists of 332 manure samples from poultry and cattle, sampled from farms located in major regions of livestock production in mainland France and Reunion Island. The samples were analysed for seven chemical properties following conventional laboratory methods. NIR spectra were acquired with three spectrometers from fresh homogenized and dried ground samples and then standardized using the PDS algorithm. This important dataset can be used to train and test chemometric models and is of particular interest to NIR spectroscopists and agronomists who assess the agronomic value of animal waste. (C) 2020 The Authors. Published by Elsevier Inc.

This dataset is composed of symbolic and quantitative entities concerning food packaging composition and gas permeability. It was created from 50 scientific articles in English registered in html format from several international journals on the ScienceDirect website. The files were annotated independently by three experts on a WebAnno server. The aim of the annotation task was to recognize all entities related to packaging permeability measures and packaging composition. This annotation task is driven by an Ontological and Terminological Resource (OTR). An annotation guideline was designed in a collective and iterative approach involving the annotators. This dataset can be used to train or evaluate natural language processing (NLP) approaches in experimental fields, such as specialized entity recognition (e.g. terms and variations, units of measure, complex numerical values) or sentence level binary relation (e.g. value to unit, term to acronym).

International audience | This dataset is composed of symbolic and quantitative entities concerning food packaging composition and gas permeability. It was created from 50 scientific articles in English registered in html format from several international journals on the ScienceDirect website. The files were annotated independently by three experts on a WebAnno server. The aim of the annotation task was to recognize all entities related to packaging permeability measures and packaging composition. This annotation task is driven by an Ontological and Terminological Resource (OTR). An annotation guideline was designed in a collective and iterative approach involving the annotators. This dataset can be used to train or evaluate natural language processing (NLP) approaches in experimental fields, such as specialized entity recognition (e.g. terms and variations, units of measure, complex numerical values) or sentence level binary relation (e.g. value to unit, term to acronym).

Crop monitoring is essential for ensuring food security in a global context of population growth and climate change. Satellite images are commonly used to estimate crop pa- rameters over large areas, and the freely available Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel- 2 (S-2) images are relevant for that purpose combining high temporal resolution and high spatial resolution. For this data article, field surveys were conducted from January to July 2017 in France to sample wheat and rapeseed crop pa- rameters during the entire crops cycle. Phenological stages were identified in 83 wheat fields and 32 rapeseed fields in Brittany and Picardy regions. Moreover, Leaf Area Index (LAI), wet biomass, dry biomass and water content were sampled in three wheat fields and three rapeseed fields in Brittany. We assigned to each field sample 10 spectral bands and 12 vegetation indices from S-2 images and two backscattering coefficients, one backscattering ratio and four polarimetric indicators from S-1 images. This dataset can be used for crop monitoring in other regions, as well as for modelling development.

Crop monitoring is essential for ensuring food security in a global context of population growth and climate change. Satellite images are commonly used to estimate crop parameters over large areas, and the freely available Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) images are relevant for that purpose combining high temporal resolution and high spatial resolution. For this data article, field surveys were conducted from January to July 2017 in France to sample wheat and rapeseed crop parameters during the entire crops cycle. Phenological stages were identified in 83 wheat fields and 32 rapeseed fields in Brittany and Picardy regions. Moreover, Leaf Area Index (LAI), wet biomass, dry biomass and water content were sampled in three wheat fields and three rapeseed fields in Brittany. We assigned to each field sample 10 spectral bands and 12 vegetation indices from S-2 images and two backscattering coefficients, one backscattering ratio and four polarimetric indicators from S-1 images. This dataset can be used for crop monitoring in other regions, as well as for modelling development.

International audience | Crop monitoring is essential for ensuring food security in a global context of population growth and climate change. Satellite images are commonly used to estimate crop parameters over large areas, and the freely available Synthetic Aperture Radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) images are relevant for that purpose combining high temporal resolution and high spatial resolution. For this data article, field surveys were conducted from January to July 2017 in France to sample wheat and rapeseed crop parameters during the entire crops cycle. Phenological stages were identified in 83 wheat fields and 32 rapeseed fields in Brittany and Picardy regions. Moreover, Leaf Area Index (LAI), wet biomass, dry biomass and water content were sampled in three wheat fields and three rapeseed fields in Brittany. We assigned to each field sample 10 spectral bands and 12 vegetation indices from S-2 images and two backscattering coefficients, one backscattering ratio and four polarimetric indicators from S-1 images. This dataset can be used for crop monitoring in other regions, as well as for modelling development.

International audience | Duck species are known to have different ability to fatty liver production in response to overfeeding and gene expression analyses can help to characterize mechanisms involved in these differences. This data article reports the sequencing of RNAs extracted from the liver of Pekin and Muscovy duck species and of their reciprocal hybrids, Mule and Hinny ducks fed ad libitum or overfed. Libraries were prepared by selecting polyadenylated mRNAs and RNA Sequencing (RNASeq) was performed using Illumina HiSeq2000 platform. RNASeq data presented in this article were deposited in the NCBI sequence read archive (SRA) under the accession number SRP144764 and links to these data were also indicated in the Data INRAE repository (https://doi.org/10.15454/JJZ3QQ). Transcriptome analyses of these data were published in Hérault et al. (2019) and Liu et al. (2020)

Duck species are known to have different ability to fatty liver production in response to overfeeding and gene expression analyses can help to characterize mechanisms involved in these differences. This data article reports the sequencing of RNAs extracted from the liver of Pekin and Muscovy duck species and of their reciprocal hybrids, Mule and Hinny ducks fed ad libitum or overfed. Libraries were prepared by selecting polyadenylated mRNAs and RNA Sequencing (RNASeq) was performed using Illumina HiSeq2000 platform. RNASeq data presented in this article were deposited in the NCBI sequence read archive (SRA) under the accession number SRP144764 and links to these data were also indicated in the Data INRAE repository (https://doi.org/10.15454/JJZ3QQ). Transcriptome analyses of these data were published in Hérault et al. (2019) and Liu et al. (2020).

International audience | This article provides experimental data describing the cell wall protein profiles of stems and leaves of Brachypodium distachyon at two different stages of development. The cell wall proteomics data have been obtained from (i) stem internodes at young and mature stages of development, and (ii) leaves at young and mature stages of development. The proteins have been extracted from purified cell walls using buffers containing calcium chloride (0.2 M) or lithium chloride (2 M). They have been identified by LC-MS/MS and bioinformatics. These data allow deepening our knowledge of these cell wall proteomes. They are a valuable resource for people interested in plant cell wall biology to understand the roles of cell wall proteins during the growth of vegetative organs.

This article provides experimental data describing the cell wall protein profiles of stems and leaves of Brachypodium distachyon at two different stages of development. The cell wall proteomics data have been obtained from (i) stem internodes at young and mature stages of development, and (ii) leaves at young and mature stages of development. The proteins have been extracted from purified cell walls using buffers containing calcium chloride (0.2 M) or lithium chloride (2 M). They have been identified by LC-MS/MS and bioinformatics. These data allow deepening our knowledge of these cell wall proteomes. They are a valuable resource for people interested in plant cell wall biology to understand the roles of cell wall proteins during the growth of vegetative organs.