cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7152598
title: Soil Organic Carbon Content estimations over the Lithuanian pilot area (2022)

description: In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_LT_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.
DOI: 10.5281/zenodo.7152597
title: Soil Organic Carbon Content estimations over the Lithuanian pilot area (2022)

description: In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_LT_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.
DOI: 10.6084/m9.figshare.21286761.v1
title: Supplementary Table 1. Primers for qPCR

description: Primers for qPCR.
DOI: 10.5281/zenodo.7139268
title: EO-based area monitoring markers computed over the Lithuanian pilot region (2022)

description: In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-01-2022 until 04-07-2021 and provides tailored information for the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers. <strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI) <strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation <strong>Homogeneity marker:</strong> used to determine if a parcel geometry consists of a single crop or if multiple things are growing on the parcel <strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest) <strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims <strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI <strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI This dataset is comprised of one geopackage file, the <em>"markers_summary.gpkg", which</em> was computed for the Lithuanian pilot region<em>. </em>Descriptions are given below. <strong>Markers summary dataset: </strong>There is a total of **1074460** FOIs for the complete country GSAA dataset. Out of these, markers are computed on **996028** FOIs that contain more than 1 Sentinel-2 pixel. <strong>Description of the information contained in the corresponding "markers summary" dataset</strong> Attribute name Description POLY_ID Reference ID of the polygon CROP_LABEL Declared crop group S2_all_observations_count Count of all observations S2_valid_observations_count Count of all valid observations S2_pixel_count Number of S2 pixels within FOI declared_as Declared crop group (v1) classification_score The pseudoprobability of the crop-group (v1) prediction. A score close to 1 indicates that the model is very confident in the prediction classification The FOI label as predicted by the crop group (v1) model crop_declared_as_2 Declared crop group (v2) crop_classification_score_2 The pseudoprobability of the crop-group (v2) prediction. A score close to 1 indicates that the model is very confident in the prediction crop_classification_2 The FOI label as predicted by the crop group (v2) model mowing_event_count Number of mowing events detected
DOI: 10.5281/zenodo.7139267
title: EO-based area monitoring markers computed over the Lithuanian pilot region (2022)

description: In the context of the EU-funded project DIONE (No. 870378), the following EO-based monitoring marker maps were released over the two pilot regions, containing the results produced from a set of image analysis and machine learning techniques. The latest explores the benefits of Copernicus's multispectral high-resolution Sentinel-2 data acquired from 01-01-2022 until 04-07-2021 and provides tailored information for the needs of European paying agencies (e.g. CAPO and NPA), expressed with the following markers. <strong>Mowing marker:</strong> used to detect mowing events on meadow/grass like Features Of Interest (FOI) <strong>Mean-NDVI marker:</strong> used to detect erroneous claims with no vegetation <strong>Homogeneity marker:</strong> used to determine if a parcel geometry consists of a single crop or if multiple things are growing on the parcel <strong>Bare soil marker:</strong> used to detect observation where bare soil is present on the feature of interest. This indicates agricultural activity on the FOI (plowing, harvest) <strong>Similarity and distance markers:</strong> used to give additional context to the crop classification and to detect erroneous claims <strong>Land marker:</strong> used to detect the land type and non-productive EFAs of the FOI <strong>Crop-type marker:</strong> used to detect the specific crop growing on the FOI This dataset is comprised of one geopackage file, the <em>"markers_summary.gpkg", which</em> was computed for the Lithuanian pilot region<em>. </em>Descriptions are given below. <strong>Markers summary dataset: </strong>There is a total of **1074460** FOIs for the complete country GSAA dataset. Out of these, markers are computed on **996028** FOIs that contain more than 1 Sentinel-2 pixel. <strong>Description of the information contained in the corresponding "markers summary" dataset</strong> Attribute name Description POLY_ID Reference ID of the polygon CROP_LABEL Declared crop group S2_all_observations_count Count of all observations S2_valid_observations_count Count of all valid observations S2_pixel_count Number of S2 pixels within FOI declared_as Declared crop group (v1) classification_score The pseudoprobability of the crop-group (v1) prediction. A score close to 1 indicates that the model is very confident in the prediction classification The FOI label as predicted by the crop group (v1) model crop_declared_as_2 Declared crop group (v2) crop_classification_score_2 The pseudoprobability of the crop-group (v2) prediction. A score close to 1 indicates that the model is very confident in the prediction crop_classification_2 The FOI label as predicted by the crop group (v2) model mowing_event_count Number of mowing events detected
DOI: 10.6084/m9.figshare.21286674
title: Siberian wildfire dataset

description: This dataset provides burned area of wildfires and observed 2-meter air temperature trend during 2002–2021, near-surface air temperature and soil moisture changes under the SSP1–2.6/SSP2-4.5 scenarios based on ten CMIP6 experiments.
DOI: 10.48366/r220885
title: Spanish corpora for Named Entity Recognition (NER)

description: This comparison takes a comprehensive look at various corpora in Spanish language that are available for the NLP/IE Named Entity Recognition (NER) task. The corpora are surveyed over all the various genre and domains of the underlying text as well as the encoded NER extraction objectives. Spanish belongs to the Romance languages group together with other major languages of the family including French, Italian, Portuguese, and Romanian, all national languages. Today, it is a global language with nearly 500 million native speakers, mainly in the Americas and Spain. Spanish is the official language of 20 countries. It is the world's second-most spoken native language after Mandarin Chinese; the world's fourth-most spoken language overall after English, Mandarin Chinese, and Hindustani (Hindi-Urdu); and the world's most widely spoken Romance language.
DOI: 10.5281/zenodo.7152640
title: Soil Organic Carbon Content estimations over the Cypriot pilot area (2022)

description: In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_CY_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.<br>
DOI: 10.5281/zenodo.7152641
title: Soil Organic Carbon Content estimations over the Cypriot pilot area (2022)

description: In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_CY_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.<br>
DOI: 10.5281/zenodo.4722443
title: Data for "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction"

description: This dataset contains the simulation results in "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction".
DOI: 10.25318/9810020101-eng
title: Language spoken at home by single and multiple responses of language spoken at home and mother tongue: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts

description: Data on language spoken at home by single and multiple responses of language spoken at home, mother tongue and age for the population excluding institutional residents for Canada, provinces and territories, census metropolitan areas and census agglomerations.
DOI: 10.21227/fyzv-w953
title: Italian validation of the situational Brief Cope Scale (I-Brief Cope)

description: Italian validation of the situational Brief Cope Scale (I-Brief Cope)
DOI: 10.34740/kaggle/dsv/4290248
title: Kurdish (Kurmanji) Proverbs
DOI: 10.25318/9810020101-fra
title: Langue parlée à la maison selon les réponses uniques et multiples portant sur la langue parlée à la maison et la langue maternelle : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement y compris les parties

description: Données sur la langue parlée à la maison selon les réponses uniques et multiples portant sur la langue parlée à la maison, la langue maternelle et l’âge pour la population, à l’exclusion des résidents institutionnels pour Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement.
DOI: 10.7910/dvn/jajvuk
title: Replication Data for: Moving toward rice self-sufficiency in sub-Saharan Africa by 2030: Lessons learned from 10 years of the Coalition for African Rice Development

description: The Coalition for African Rice Development (CARD) was initiated as a policy framework with the aim of doubling rice production in sub-Saharan Africa (SSA) over the period from 2008 to 2018. This paper assesses the contribution of the CARD policy to rice production and forecasts the local rice supply and demand to provide a better understanding of the policies needed to attain rice self-sufficiency by 2030. A combination of the autoregressive integrated moving average method and counterfactual approach was adopted using rice statistical data from 23 countries in SSA. The results showed that the contribution of CARD to paddy rice production in 2018 was 10.2 million tons, equivalent to 74% of the target. This contribution resulted from increases in area and yield of 23% and 19%, respectively. However, the yield growth rate was not sustainable in almost two-thirds of countries. Investments in supply-push factors such as fertilizer and irrigation development, which were the focus in the past, have limited effects on rice production. We conclude that sustainable investments in demand-pull factors such as the private-led modern milling sector and contract farming development should be prioritized to achieve rice self-sufficiency in SSA.
DOI: 10.34740/kaggle/dsv/4290348
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5287/bodleian:8rdk8j2p9
title: Impact of Natural Environment, Regional Integration, and Policies on FDI
DOI: 10.5281/zenodo.7152717
title: Soil properties as point estimations over the Lithuanian pilot area (2022)

description: In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties: Sand % Clay % Silt % Electrical Conductivity (mS/m) pH Calcium carbonate % Soil Organic Carbon % The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields: <strong>Description of the information contained in the corresponding "LT points estimations" dataset</strong> Field Type Description Sample ID String Unique ID Lat Real Latitude Lon Real Longitude Sand Real Sand fraction Clay Real Clay fraction Silt Real Silt fraction EC Real Electrical Conductivity ph_H<sub>2</sub>0 Real ph CaCO<sub>3</sub> Real Calcium Carbonate SOC Real Soil Organic Carbon
DOI: 10.5281/zenodo.7152716
title: Soil properties as point estimations over the Lithuanian pilot area (2022)

description: In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties: Sand % Clay % Silt % Electrical Conductivity (mS/m) pH Calcium carbonate % Soil Organic Carbon % The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields: <strong>Description of the information contained in the corresponding "LT points estimations" dataset</strong> Field Type Description Sample ID String Unique ID Lat Real Latitude Lon Real Longitude Sand Real Sand fraction Clay Real Clay fraction Silt Real Silt fraction EC Real Electrical Conductivity ph_H<sub>2</sub>0 Real ph CaCO<sub>3</sub> Real Calcium Carbonate SOC Real Soil Organic Carbon
DOI: 10.21227/sr59-vy20
title: CRAWDAD dartmouth/bluetooth-hci

description: This dataset consists of a collection of Bluetooth HCI traces captured on a smartphone while a smartphone and smart device communicated.
DOI: 10.5281/zenodo.7152653
title: Soil properties as point estimations over the Cypriot pilot area (2022)

description: In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties: Sand % Clay % Silt % Electrical Conductivity (mS/m) pH Calcium carbonate % Soil Organic Carbon % The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields: <strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong> Field Type Description Sample_ID String Unique ID Lat Real Latitude Lon Real Longitude Sand Real Sand fraction Clay Real Clay fraction Silt Real Silt fraction EC Real Electrical Conductivity ph_H<sub>2</sub>0 Real pH CaCO<sub>3</sub> Real Calcium Carbonate SOC Real Soil Organic Carbon
DOI: 10.5281/zenodo.7152652
title: Soil properties as point estimations over the Cypriot pilot area (2022)

description: In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties: Sand % Clay % Silt % Electrical Conductivity (mS/m) pH Calcium carbonate % Soil Organic Carbon % The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields: <strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong> Field Type Description Sample_ID String Unique ID Lat Real Latitude Lon Real Longitude Sand Real Sand fraction Clay Real Clay fraction Silt Real Silt fraction EC Real Electrical Conductivity ph_H<sub>2</sub>0 Real pH CaCO<sub>3</sub> Real Calcium Carbonate SOC Real Soil Organic Carbon
DOI: 10.48366/r220919
title: Chinese corpora for Named Entity Recognition (NER)

description: This comparison takes a comprehensive look at various corpora in Chinese language that are available for the NLP/IE Named Entity Recognition (NER) task. The corpora are surveyed over all the various genre and domains of the underlying text as well as the encoded NER extraction objectives.
DOI: 10.25318/98100202-eng
title: Knowledge of official languages by income statistics, age and gender: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts

description: Data on the knowledge of official languages by some income statistics, age and gender for the population ages 15 years and over excluding institutional residents.
DOI: 10.5281/zenodo.7152655
title: deGeco genomic compartments model fit results on whole genome at resolution of 50kb

description: These files contain the fitted parameters for the deGeco model for genomic compartments. Fits were done at 50kb on four cell lines: GM12878 (from Rao, et al., 2014), H1, HFF (both from Krietenstein, et al., 2020) and mESC (from Bonev, et al., 2017). Each Hi-C file was zoomified again using cooler, to prevent duplicate entries in the pixel table. The file format is NumPy's npz object that has two main keys: Metadata - an object containing various information on the run: command line parameters, duration of run, etc Parameters - an object containing the actual fitted parameters: state_probabilities - an NxS matrix of state probabilities, where N is the number of bins and S the number of states the model was run with cis_weights - an SxS matrix of cis state affinities trans_weights - an SxS matrix of trans state affinities cis_dd_power - the exponent of the power law decay of interaction intensity in cis (also denoted as alpha) trans_dd - the constant background level of trans interaction (also denoted as beta) cis_lengths - Number of bins for each chromosome. Sum of cis_lengths is N, the total number of bins. To read using numpy: <pre><code class="language-python">import numpy as np fit = np.load(filename, allow_pickle=True) metadata = fit['metadata'][()] parameters = fit['parameters'][()]</code></pre> or use the gc_datafile module from the deGeco repository: <pre><code class="language-python">import gc_datafile parameters = gc_datafile.load_params(filename)</code></pre>
DOI: 10.5281/zenodo.7152654
title: deGeco genomic compartments model fit results on whole genome at resolution of 50kb

description: These files contain the fitted parameters for the deGeco model for genomic compartments. Fits were done at 50kb on four cell lines: GM12878 (from Rao, et al., 2014), H1, HFF (both from Krietenstein, et al., 2020) and mESC (from Bonev, et al., 2017). Each Hi-C file was zoomified again using cooler, to prevent duplicate entries in the pixel table. The file format is NumPy's npz object that has two main keys: Metadata - an object containing various information on the run: command line parameters, duration of run, etc Parameters - an object containing the actual fitted parameters: state_probabilities - an NxS matrix of state probabilities, where N is the number of bins and S the number of states the model was run with cis_weights - an SxS matrix of cis state affinities trans_weights - an SxS matrix of trans state affinities cis_dd_power - the exponent of the power law decay of interaction intensity in cis (also denoted as alpha) trans_dd - the constant background level of trans interaction (also denoted as beta) cis_lengths - Number of bins for each chromosome. Sum of cis_lengths is N, the total number of bins. To read using numpy: <pre><code class="language-python">import numpy as np fit = np.load(filename, allow_pickle=True) metadata = fit['metadata'][()] parameters = fit['parameters'][()]</code></pre> or use the gc_datafile module from the deGeco repository: <pre><code class="language-python">import gc_datafile parameters = gc_datafile.load_params(filename)</code></pre>
DOI: 10.48366/r220920
title: Arabic corpora for Named Entity Recognition (NER)

description: This comparison takes a comprehensive look at various corpora in Arabic language that are available for the NLP/IE Named Entity Recognition (NER) task. The corpora are surveyed over all the various genre and domains of the underlying text as well as the encoded NER extraction objectives.
DOI: 10.34848/qsnikj/gmhlbc
title: Base de datos de pacientes.tab

description: :unav