diff --git a/.github/ISSUE_TEMPLATE/metadata-request.yml b/.github/ISSUE_TEMPLATE/metadata-request.yml deleted file mode 100644 index 7535501..0000000 --- a/.github/ISSUE_TEMPLATE/metadata-request.yml +++ /dev/null @@ -1,463 +0,0 @@ -name: Resource Metadata Request -description: Request a resource metadata to be made available to FAIRiCUBE use cases. -body: - - type: markdown - attributes: - value: | - Please provide the information requested below for the resource. Mandatory elements are marked with `*`. - Resource can be the algorithm, the model, or the pre-processing pipeline. - - type: markdown - attributes: - value: |- - # Resource Metadata Request - # inizio - - type: dropdown - id: uc - attributes: - label: Use case - description: Use case to which the resource belongs - multiple: false - options: - - UC1 - - UC2 - - UC3 - - UC4 - - common - validations: - required: true - - type: textarea - id: Name_of_resource - attributes: - label: Name of resource - description: Textual name identifying the resource. Resource can be the algorithm, the model, or the pre-processing pipeline. - validations: - required: true - # id - - type: input - id: id - attributes: - label: ID - description: Globally unique and persistent identifier of the resource. - validations: - required: true - # description - - type: textarea - id: description - attributes: - label: Description - description: Description of the resource. - validations: - required: true - # main category - - type: dropdown - id: main-category - attributes: - label: Main category - description: |- - Main category of the resource, e.g. ML, ingestion, pre-processing. - Notice that the codelist contains also some specific elements to further specify the resource (e.g., "DL" as a special case of "ML"). - multiple: false - options: - - Machine Learning - - Deep Learning - - Pre-processing - - Ingestion - - Analytics - validations: - required: true - # other category - - type: dropdown - id: other-category - attributes: - label: Other category - description: This element should be used to categorise the resource according to possible vocabularie. The codelist is not yet available. - multiple: true - options: - - A - - B - validations: - required: False - # pubblication date - - type: textarea - id: publication-date - attributes: - label: Publication date - description: Date of publication - i.e., first sharing with the scientific community - of the resource. - validations: - required: true - # objective - - type: dropdown - id: objective - attributes: - label: Objective - description: This element should be used to provide info about “What does the resource perform”, i.e., the purpose of the resource. - multiple: false - options: - - object-detection - - classification - - segmentation - - regression - - outliers-removing - - gap-filling - - feature-selection - - dimensionality-reduction - - feature-scaling - - dataset-balancing - - data-transformation - - analytics - - clustering - - anomaly-detection - validations: - required: true - # platform - - type: dropdown - id: platform - attributes: - label: Platform - description: Platform hosting the resource - multiple: false - options: - - EOX - - Rasdaman - - Google Colab - - Kaggle - - Microsoft Azure - - Amazon AWS - - Local Jupyter Notebook - validations: - required: true - # framework - - type: dropdown - id: framework - attributes: - label: Framework - description: |- - This field is generally intended as collection of reusable code written by others. - In this respect, it includes both frameworks, intended as program scaffolds that supply the blueprint of a product, and libraries, intended as collections of pre-defined methods and classes. - multiple: false - options: - - PyTorch - - Tensorflow - - Scikit-learn - - Keras - - Pandas - - Numpy - - OpenCV - - XGBoost - - Theano - - imblearn - - pillow - - Rasdaman - - MXNet - - Apache-Spark - validations: - required: true - # architecture - - type: dropdown - id: architecture - attributes: - label: Architecture - description: |- - This is a conditional element, applied only to ML algorithms. - This field has a different meaning than "algorithm" because there are often multiple "implementations" of an architecture. - multiple: false - options: - - random-forest - - dnn - - decision-tree - - ensemble - - gradient-based - - density-based - - datacubes - - RNN - Recurrent-Neural-Network - - CNN - Convolutional-Neural-Network - - Feed-Forward-Neural-Network - - DBN - Deep-Belief-Network - - DSN - Deep-Stacking-Network - - SVM - Support-Vector-Machine - - probabilistic-model - - Perceptron - - Multilayer-Perceptron - - Gaussian-Mixture - validations: - required: false - # approach - - type: dropdown - id: approach - attributes: - label: Approach - description: This is a conditional element, applied only to ML algorithms, identifying the learning modality. - multiple: false - options: - - supervised - - unsupervised - - semi-supervised - - reinforcement-learning - validations: - required: false - # algorithm - - type: dropdown - id: algorithm - attributes: - label: Algorithm - description: |- - This field contains the name of the algorithm, i.e., an implementation of an architecture. - This field has a different meaning than ‘architecture’ because there are often multiple "implementations" of an architecture. - multiple: false - options: - - Random-Forest-Classifier - - CNN - Convolutional-Neural-Network - - K-means - - Min-max-normalization - - DBSCAN - Density-Based-Spatial-Clustering-of-Applications-with-Noise - - Decision-Tree-Classifier - - Random-Forest-Regression - - SGD-Classifier - Stochastic-Gradient-Descent - - KNN-Classifier - K-nearest-neighbors - - SegNet - - LeNet - - Decision-Tree-Regression - - Voting-Classifier - - AdaBoost-Classifier - - AdaBoost-Regression - - SMOTE - Synthetic-Minority-Oversampling-TEchnique - - custom-method - - WCPS - - Logistic-Regression - - Naive-Bayes - validations: - required: true - # processor - - type: dropdown - id: processor - attributes: - label: Processor - description: |- - Type of processor used for training or inference in learning tasks, but also for running a pre-processing notebook. - If more than one processor is used to perform the entire task, indicate the one with the highest computing power. - multiple: false - options: - - cpu - - gpu - - tpu - validations: - required: true - # OS - - type: dropdown - id: os - attributes: - label: OS - description: Operating System System used. - multiple: false - options: - - aix - - linux - - win32 - - cygwin - - darwin - - macOS - - windows - validations: - required: true - # keyword - - type: textarea - id: keyword - attributes: - label: Keyword - description: |- - A series of keywords which can facilitate the resource discoverability. - These keywords can be used for a quick search of resources and should highlight foremost characteristics of the resource. - For the categorization of the resources via keywords to be effective, we recommend that a maximum of 5 keywords is provided. - value: Keyword-1, Keyword-2, ... , Keyword-5 - validations: - required: true - # reference-link - - type: input - id: reference-link - attributes: - label: Reference link - description: Link(s) to resource web page and/or download page - validations: - required: false - # example - - type: input - id: example - attributes: - label: Example - description: Link(s) to websites (including publications) providing examples of how the resource has been used. - validations: - required: false - # input-data - - type: textarea - id: input-data - attributes: - label: Input data used - description: |- - Link to data (or related metadata) to which the a/p resource has been applied. This information is required for a better understanding of context and domain of the a/p resource. - To insert the values, delete 'Link-1', 'Link-2' etc. and, keeping the number of the bulleted list, insert the various links. - value: |- - 1. Link-1 - 2. Link-2 - ... - n. Link-n - validations: - required: true - # Characteristics of input data - - type: textarea - id: characteristics-of-input-data - attributes: - label: Characteristics of input data - description: |- - The field contains a textual description of the main characteristics of each input data to the resource. - This field will also include e.g., description of sampling techniques, version of the data (if multiple versions are available), and, in the case of ML resources, also - the percentages of training, validation and testing sets. This field may contain details on the suitability of the resource for the chosen geographic area and thematic context. - To insert values, delete 'Characteristics of the Link-1', 'Characteristics of the Link-2' etc. and, keeping the number in the bulleted list, enter the characteristics of the input data. Each characteristic refers to the respective link, in the same position, in the input data field. E.g. the characteristic in 1. refers to the link in 1. - value: |- - 1. Characteristics of the Link-1 - 2. Characteristics of the Link-2 - ... - n. Characteristics of the Link-n - validations: - required: true - # Biases and ethical aspects - - type: textarea - id: biases-and-ethical-aspects - attributes: - label: Biases and ethical aspects - description: |- - This field may contain observations on the data and/or any biases found. - This field is optional but strongly recommended for pre-processing resources. - In the case of ML resources, this field is closely related to the performance field. - To insert values, delete 'Biases and ethical aspects of the Link-1', 'Biases and ethical aspects of the Link-2' etc. and, keeping the number in the bulleted list, enter the biases and ethical aspects of the input data. Each biases and ethical aspects refers to the respective link, in the same position, in the input data field. E.g. the biases and ethical aspects in 1. refers to the link in 1. - If not all links have biases and ethical aspects keep the same structure by leaving the corresponding line empty. - For example - 1. - 2. biases and ethical aspects of the Link-2 - If no link contains biases and ethical aspects, leave the entire cell empty by deleting all its contents. - value: |- - 1. Biases and ethical aspects of the Link-1 - 2. Biases and ethical aspects of the Link-2 - ... - n. Biases and ethical aspects of the Link-n - validations: - required: false - # Model configuration - - type: textarea - id: model-configuration - attributes: - label: Model configuration - description: |- - This field contains the configuration/initialisation data and how the model has been parameterized - validations: - required: false - # output-data - - type: textarea - id: output-data - attributes: - label: Output data obtained - description: |- - Link to output data (or related metadata) produced by execution of the a/p resource. This information is required for a better understanding of the a/p resource. - To insert the values, delete 'Link-1', 'Link-2' etc. and, keeping the number of the bulleted list, insert the various links. - value: |- - 1. Link-1 - 2. Link-2 - ... - n. Link-n - validations: - required: true - # Characteristics of output data - - type: textarea - id: characteristics-of-output-data - attributes: - label: Characteristics of output data - description: |- - Textual description of the output data from the resource. - To insert values, delete 'Characteristics of the Link-1', 'Characteristics of the Link-2' etc. and, keeping the number in the bulleted list, enter the characteristics of the input data. Each characteristic refers to the respective link, in the same position, in the input data field. E.g. the characteristic in 1. refers to the link in 1. - value: |- - 1. Characteristics of the Link-1 - 2. Characteristics of the Link-2 - ... - n. Characteristics of the Link-n - validations: - required: true - # performance - - type: textarea - id: perfmance - attributes: - label: Performance - description: This field contains result description and explanation, including a detailed description of the hyperparameters used, the run times, the metrics used for evaluation, and the respective scores and performance. - validations: - required: false - # Conditions for access and use - - type: dropdown - id: conditions-for-access-and-use - attributes: - label: Conditions for access and use - description: Type of distribution license for the resource. - multiple: false - options: - - MIT - - afl-3.0 - - agpl-3.0 - - artistic-2.0 - - bigscience-bloom-rail-1.0 - - bigscience-openrail-m - - bsd - - bsd-2-clause - - bsd-3-clause - - bsd-3-clause-clear - - bsl-1.0 - - cc - - cc0-1.0 - - cc-by-2.0 - - cc-by-2.5 - - cc-by-3.0 - - cc-by-4.0 - - cc-by-nc-2.0 - - cc-by-nc-3.0 - - cc-by-nc-4.0 - - cc-by-nc-nd-3.0 - - 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 - validations: - required: true - # Constraints - - type: textarea - id: constraints - attributes: - label: Constraints - description: This element can be used to provide information about possible constraints related to the use of the resource. - validations: - required: false