Resource Metadata

The purpose of this repository is to govern the creation and maintenance of metadata of processing/analysis (a/p) resources. Aspects related to the governance of codelists apply also to datasets metadata.

To create the metadata file for a new a/p resource as well as to update an existing one use the dedicated web application.

The created metadata file will automatically be published in the FAIRiCUBE Catalogue and will be queryable through Knowledge Base Query Tool.

To ensure semantic harmonisation, some metadata elements are valorised by selecting values from corresponding codelists. Use the Codelist change proposal template for proposing updates to current codelists.

Analysis and processing resources metadata codelists

NB: Each column corresponds to a codelist. The column header (in italics) is the codelist name.

Main category	Objective	Platform	Framework	Architecture	Approach	Algorithm	Processor	OS	Use case	Conditions for access and use
Machine Learning	object-detection	EOX	pytorch	random forest	supervised	Random-Forest-Classifier	cpu	aix	UC1	afl-3.0
Deep Learning	classification	Rasdaman	tensorflow	DNN – Deep-Neural-Network	unsupervised	CNN - Convolutional-Neural-Network	gpu	linux	UC2	agpl-3.0
Pre-processing	segmentation	Google Colab	scikit-learn	decision-tree	semi-supervised	K-means	tpu	windows	UC3	artistic-2.0
Ingestion	regression	Kaggle	keras	ensemble	reinforcement-learning	Min-max-normalization		cygwin	UC4	bigscience-bloom-rail-1.0
Analytics	outliers-removing	Microsoft Azure	pandas	gradient-based		DBSCAN - Density-Based-Spatial-Clustering-of-Applications-with-Noise		darwin	UC5	bigscience-openrail-m
	gap-filling	Amazon AWS	numpy	density-based		Decision-Tree-Classifier		macOS	common	bsd
	feature-selection	Local Jupyter Notebook	openCV	datacubes		Random-Forest-Regression				bsd-2-clause
	dimensionality-reduction		XGBoost	RNN – Recurrent-Neural-Network		SGD - classifier – Stochastic-Gradient-Descent				bsd-3-clause
	feature-scaling		theano	CNN – Convolutional-Neural-Network		KNN – classifier – K-nearest-neighbors				bsd-3-clause-clear
	dataset-balancing		imblearn	Feed-Forward-Neural-Network		SegNet				bsl-1.0
	data-transformation		pillow	DBN – Deep-Belief-Network		LeNet				cc
	analytics		Rasdaman	DSN – Deep-Stacking-Network		Decision-Tree-Regression				cc0-1.0
	clustering		MXNet	SVM – Support-Vector-Machine		Voting-Classifier				cc-by-2.0
	anomaly-detection		Apache-Spark	probabilistic model		AdaBoost-Classifier				cc-by-2.5
			gdal	Perceptron		AdaBoost-Regression				cc-by-3.0
			no-framework	Multilayer-Perceptron		SMOTE – Synthetic-Minority-Oversampling-TEchnique				cc-by-4.0
						custom-method				cc-by-nc-2.0
						WCPS				cc-by-nc-3.0
						Naïve-Bayes				cc-by-nc-4.0
						Logistic-regression				cc-by-nc-nd-3.0
						Gaussian-Mixture				cc-by-nc-nd-4.0
										cc-by-nc-sa-2.0
										cc-by-nc-sa-3.0
										cc-by-nc-sa-4.0
										cc-by-nd-4.0
										cc-by-sa-3.0
										cc-by-sa-4.0
										creativeml-openrail-m
										c-uda
										ecl-2.0
										epl-1.0
										epl-2.0
										eupl-1.1
										gfdl
										gpl
										gpl-2.0
										gpl-3.0
										isc
										lgpl
										lgpl-2.1
										lgpl-3.0
										lgpl-lr
										mpl-2.0
										ms-pl
										ncsa
										odbl
										odc-by
										ofl-1.1
										openrail
										openrail++
										osl-3.0
										pddl
										postgresql
										wtfpl

Dataset metadata codelists

NB: Each column corresponds to a codelist. The column header (in italics) is the codelist name. For each codelist there are three fields on three different lines: the value, the definition and the link to a source providing more information.

	ObservableProperties
Value	Water and Wetness
Definition	The HRL Water and Wetness 2018 provides primary products in full spatial resolution of 10m x 10m (as compared to 20m x 20m resolution in 2015). The main product is a classified layer, differentiating the classes of permanent water, temporary water, permanent wet, temporary wet, and dry areas, derived from water and wetness occurrences in the period 2012-2018.
Source Link	Water and Wetness source link