FAIR4ML - Bewertungsergebnisse

project

Dieses Projekt beinhaltet alle Objekte, die mittelts FAIR4ML bewertet wurden.

Tags: Machine Learning Model

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Project Assessments (9)


Assessment Metrics Date
Target Rubric   Globally unique identifier Persistent identifier Machine-readable metadata Standardized metadata Resource identifier in metadata Resource discovery through web search Open, Free, Standardized Access protocol Protocol to access restricted content Persistence of resource and metadata Resource uses formal language FAIR vocabulary Linked Digital resource license Metadata license Provenance scheme Certificate of compliance to community standard Metadata is identified by a persistent identifier (Essential) Data is identified by a persistent identifier (Essential) Metadata is identified by a globally unique identifier (Essential) Data is identified by a globally unique identifier (Essential) Rich metadata is provided to allow discovery (Essential) Metadata includes the identifier for the data (Essential) Metadata is offered in such a way that it can be harvested and indexed (Essential) Metadata contains information to enable the user to get access to the data (Important) Metadata can be accessed manually (i.e. with human intervention) (Essential) Data can be accessed manually (i.e. with human intervention) (Essential) Metadata identifier resolves to a metadata record (Essential) Data identifier resolves to a digital object (Essential) Metadata is accessed through standardised protocol (Essential) Data is accessible through standardised protocol (Essential) Data can be accessed automatically (i.e. by a computer program) (Important) Metadata is accessible through a free access protocol (Essential) Data is accessible through a free access protocol (Important) Data is accessible through an access protocol that supports authentication and authorisation (Useful) Metadata is guaranteed to remain available after data is no longer available (Essential) Metadata uses knowledge representation expressed in standardised format (Important) Data uses knowledge representation expressed in standardised format (Important) Metadata uses machine-understandable knowledge representation (Important) Data uses machine-understandable knowledge representation (Important) Metadata uses FAIR-compliant vocabularies (Important) Data uses FAIR-compliant vocabularies (Useful) Metadata includes references to other metadata (Important) Data includes references to other data (Useful) Metadata includes references to other data (Useful) Data includes qualified references to other data (Useful) Metadata includes qualified references to other metadata (Important) Metadata include qualified references to other data (Useful) Plurality of accurate and relevant attributes are provided to allow reuse (Essential) Metadata includes information about the licence under which the data can be reused (Essential) Metadata refers to a standard reuse licence (Important) Metadata refers to a machine-understandable reuse licence (Important) Metadata includes provenance information according to community-specific standards (Important) Metadata includes provenance information according to a cross-community language (Useful) Metadata complies with a community standard (Essential) Data complies with a community standard (Essential) Metadata is expressed in compliance with a machine-understandable community standard (Essential) Data is expressed in compliance with a machine-understandable community standard (Important) Unterschiedliche Versionen Globaler Identifikator Persistenter Identifikator Beschreibende und zitierende Kernelemente FAIR Metadaten Identifikator in den Metadaten Zugriffsbeschränkung und -bedingung Standardprotokol für Metadaten Standardprotokoll für Model Authentifizierung- und Authentifikationsprozess für Metadaten Authentifizierung- und Authentifikationsprozess für Model formale Wissensrepräsentationssprache semantische Ressourcen ML Model Service Qualifizierte Referenzen genaue und relevante Attribute Lizenz Herkunftsinformationen Model Type/ Lernalgorithmus Informationen über das Datenset für Training, Testen und Validieren Herkunft des Datensets für Training, Testen und Validieren Quellcode des ML Models Hyperparameter des ML Models Lernprozess des ML Models Software als qualifizierte Referenz Metadatenstandard für ML Model File format Evaluierung des Models offenes Protokoll Metadaten offenes Protokoll Model Datenarchivierung
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding FAIR4ML by Laukar Tofik
                                                                                                                  yesbut (0.75) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yesbut (0.75) yesbut (0.75) yes (1.00) yesbut (0.75) yes (1.00) yes (1.00) yes (1.00) yesbut (0.75) yes (1.00) yes (1.00) yesbut (0.75) yesbut (0.75) yesbut (0.75) nobut (0.25) yesbut (0.75) yes (1.00) yes (1.00) no (0.00) yesbut (0.75) yesbut (0.75) yesbut (0.75) yes (1.00) no (0.00) yes (1.00) yes (1.00) no (0.00) Jan 16, 2023
ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation FAIR4ML by Laukar Tofik
                                                                                                                  yes (1.00) yes (1.00) yes (1.00) yesbut (0.75) yes (1.00) yesbut (0.75) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yesbut (0.75) yes (1.00) yes (1.00) yes (1.00) yesbut (0.75) yesbut (0.75) no (0.00) nobut (0.25) nobut (0.25) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yesbut (0.75) yes (1.00)         Jan 16, 2023
ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation FAIR metrics by fairmetrics.org
yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00)   yes (1.00) yes (1.00) no (0.00) no (0.00) no (0.00)                                                                                                                                                 Jan 16, 2023
A spinup acceleration tool for land surface model (LSM) family of ORCHIDEE. FAIR4ML by Laukar Tofik
                                                                                                                  yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yesbut (0.75) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) no (0.00) no (0.00) no (0.00) yesbut (0.75) yes (1.00) yesbut (0.75) yesbut (0.75) no (0.00) no (0.00) yesbut (0.75) yesbut (0.75) yesbut (0.75) no (0.00)   nobut (0.25)         Jan 16, 2023
A spinup acceleration tool for land surface model (LSM) family of ORCHIDEE. FAIR metrics by fairmetrics.org
yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) no (0.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) no (0.00)                                                                                                                                                 Jan 16, 2023
Model-based assessment of sampling protocols for infectious disease genomic surveillance FAIR4ML by Laukar Tofik
                                                                                                                                                                                Jan 22, 2023
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data FAIR4ML by Laukar Tofik
                                                                                                                  yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) no (0.00) yesbut (0.75) yes (1.00) no (0.00) yes (1.00) no (0.00) yes (1.00) yesbut (0.75) no (0.00) no (0.00) yesbut (0.75) yesbut (0.75) yesbut (0.75) no (0.00) no (0.00) no (0.00) nobut (0.25) no (0.00) no (0.00) no (0.00) yesbut (0.75) no (0.00) no (0.00) yes (1.00) no (0.00) yes (1.00) Jan 22, 2023
Model for Syriac text FAIR4ML by Laukar Tofik
                                                                                                                  yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) nobut (0.25) yes (1.00) yes (1.00) nobut (0.25) yes (1.00) yes (1.00) yes (1.00) yesbut (0.75) yesbut (0.75) yes (1.00) nobut (0.25) yes (1.00) yesbut (0.75) no (0.00) no (0.00) no (0.00) no (0.00) no (0.00) no (0.00) no (0.00) yes (1.00) yesbut (0.75) no (0.00) yes (1.00)   yes (1.00) Jan 24, 2023
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding FAIR Data Maturity Model: specification and guidelines
                                                                                                                                                                                Feb 5, 2023