{ "dmp" : { "contact" : { "contact_id" : { "identifier" : "e60876ed-87f8-4a8e-8081-e5620ec839cf", "type" : "other", "additional_properties" : { } }, "mbox" : "elli.p@imis.athena-innovation.gr", "name" : "Elli Papadopoulou", "additional_properties" : { } }, "contributor" : [ ], "cost" : [ ], "created" : "2022-10-07T07:30:06Z", "dataset" : [ { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "14e36583-8a76-48b7-ae27-c8030c63e2e7", "type" : "other", "additional_properties" : { } }, "description" : "This dataset consists of a series of identifiers for publications in OpenAIRE, publications in Semantic Scholar, patents from PATSTAT and projects from CORDIS which are considered relevant for the AI domain, and were used to calculate the AI-related indicators. The criterion for selecting these items has been agreed with members of the SEDIA team, the lead partner for IntelComp’s AI living lab, and consists on appearance of at least three AI related keywords in the publications abstract, patent summary, or project description.", "language" : "eng", "metadata" : [ ], "title" : "AI publications, patents and EU projects", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "31c3e566-599f-4ea8-b1ab-695713992f32", "type" : "other", "additional_properties" : { } }, "description" : "
This dataset aggregates ESG (Environmental, Social, and Governance) data specifically from the energy and agrifood sectors within the EU and Greece. Derived from companies' sustainability reports, advanced Language Model techniques (LLMs) were employed to pinpoint and extract specific metric values. The collection provides a comprehensive overview of ESG practices and metrics for companies operating in these sectors in the mentioned regions.
", "distribution" : [ ], "keyword" : [ ], "language" : "eng", "metadata" : [ ], "personal_data" : "unknown", "security_and_privacy" : [ ], "sensitive_data" : "unknown", "technical_resource" : [ ], "title" : "EU-GR Energy & Agrifood ESG Analytics", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d", "metadata" : "true", "commentFieldValue8f9132fc-5b4a-65f6-9d63-6b049ab03faf" : "31.530 records", "commentFieldValue4bea3a8a-d9d2-b7de-cc04-ab698a624641" : "Dataset for internal use in the project.
", "qualitydataQ219" : "false", "other216a" : "true", "name212" : "In the IntelComp project, we employed basic naming conventions to ensure clarity and consistency in data management. These conventions were straightforward, focusing on facilitating easy identification and organization of the datasets. An example of our naming convention is: [DatasetName]_[VersionNumber]_[Date]. ", "commonlyusedformat216b" : "false", "c20efdf5-a4b3-82f3-524a-b2a692c7b593" : "CERIF (Common European Research Information Format)", "c4f19f13-55d6-379c-a359-349625dd74a5" : "false", "commentFieldValuedf877363-aefc-5847-fc55-5c2e33762391" : "No personal data involved.
", "633046ef-1e6f-dbe0-8c5c-43599554943b" : "true", "searchablemetadataQ215" : "false", "openlyaccessibleQ222" : "none", "persistentIdentifiersQ214" : "false", "DatasetSecuritySectionField1" : "Kept on secure, managed storage for limited time", "f57c4211-aab1-9bd7-65eb-249e79a1e13f" : "{\"label\":null,\"name\":\"Dimitris Pappas (orcid:0000-0001-5784-0658)\",\"id\":null,\"reference\":\"0000-0001-5784-0658\",\"status\":0,\"tag\":\"ORCID\",\"key\":\"orcid\",\"hint\":null}", "27555ff8-7d4f-0d90-a925-a9686467ba21" : "true", "1bda2fcb-d63e-16e8-5925-fa2d2df0b923" : "true", "commentFieldValuefieldSet-cf1P1" : "In the case of closed datasets, such as this one, created in the project their metadata will be open and available through the IntelComp Data Catalogue. However, access to this catalogue may be limited.
", "harvestable211a" : "true", "e44e952f-5249-c495-5e84-fa696695849e" : "The project used COAR controlled vocabularies (https://www.coar-repositories.org/) as well as ontologies, following the OpenAIRE example for Resource Description Framework (RDF) compatibility. That was important for the project to allow for successful interlinking of resources across all domains.", "commentFieldValue6bb1034a-11f3-985e-9101-563a4643827e" : "An ESG (Environmental, Social, and Governance) analytics dataset plays a crucial role in agenda setting for STI (Science, Technology, and Innovation) policymakers for several reasons. First, it provides comprehensive insights into sustainability practices, social responsibility, and governance structures, which are vital for formulating policies that align with contemporary global challenges and ethical standards. Second, such a dataset aids in identifying emerging trends and priorities in the ESG domain, enabling policymakers to focus on key areas that drive innovation and sustainable development in the science and technology sectors.
", "197c2a3e-c92e-beca-acdf-0ecca8bcef94" : "[\"Data Center Archive Storage\"]", "notlistedA211a" : "false", "bdb3ee48-1862-d1ce-d90c-94fbe8c4daab" : "Per company ESG metrics coverage and performance data.", "documentedProcedures243" : "false", "field-HVLNZ" : "never", "otherField1" : "false", "boolean-will-metadata-use" : "true", "toolsaccess225" : "false", "6e47ae6f-a728-94bf-27df-bb7f76872248" : "[\"Other\"]", "to-whom-might-it" : "[\"Decision makers\"]", "commentFieldValue29b71ab9-1dd2-156d-dd70-1ee428029a5a" : "project", "commentFieldValuefieldSet-jYyLG" : "While the IntelComp project does not have formally documented procedures for data quality assurance, extensive quality checks and curations are inherently part of the process for all Tier 2 and Tier 3 datasets, which are created within the project. These procedures include various methods of ensuring data accuracy, consistency, and reliability. However, the specific processes and checks implemented are not formally documented but are integral to the workflow of data handling and management in the project.
", "field-UmI2l" : "true", "commentFieldValuefieldSet-WIQGN" : "JSON", "4fef6fbe-f539-e8a8-4e34-0844229b9398" : "true", "other211" : "false", "commentFieldValuefieldSet-dr427" : "For internal use in the project
", "notlisted215a" : "false", "reason242a" : "For internal use in the project", "metadataAvailableFreeofcharge" : "true", "backup224" : "securewithbackupandrecovery", "ethicallegalissuesQ221" : "false", "clearVersionNumbers" : "false", "what-is-the-origin" : "[\"Secondary data\"]", "notlisted223" : "false" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "838f0d61-3f9b-486c-bd36-4f746c9419ac", "type" : "other", "additional_properties" : { } }, "description" : "Funding Round Data downloaded from the Crunchbase database using an access key provided by Technopolis-Group.", "language" : "eng", "metadata" : [ ], "title" : "Funding Rounds ", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "ec05d7a3-7a4a-4f96-a546-472f2e40c107", "type" : "other", "additional_properties" : { } }, "description" : "ExPORTER provides bulk RePORTER data that includes: projects, project abstracts, publications citing support, link tables for project to publication associations, patents, and clinical studies. ExPORTER contains research projects funded by National Institues of Health (NIH), Administration for Children and Families ( ACF), Agency for Healthcare Research and Quality (AHRQ), Centers for Disease Control and Prevention (CDC), Health Resources and Services Administration (HRSA), Food & Drug Administration (FDA), and Department of Veterans Affairs ( VA). More information: https://exporter.nih.gov/about.aspx"", "language" : "eng", "metadata" : [ ], "title" : "NIH Research Portfolio", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "26e4b7e1-6b08-47ba-b147-f771e7207dba", "type" : "other", "additional_properties" : { } }, "description" : "
The subsequent raw datasets (Tier 1) were utilized in the IntelComp project. These datasets retain their original metadata as provided by their respective sources and are not shared by the project. They serve as the foundational elements for constructing Tier 2 (analytics) datasets, which are kept internal to the project (and described in detail in the DMP) and remain closed. Additionally, these contribute to the development of Tier 3 (indicators) datasets, which we make publicly accessible and are also described herein.

", "distribution" : [ ], "keyword" : [ ], "language" : "eng", "metadata" : [ ], "personal_data" : "unknown", "security_and_privacy" : [ ], "sensitive_data" : "unknown", "technical_resource" : [ ], "title" : "Input (Reused) Datasets", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d", "notlistedA211a" : "false", "otherField1" : "false", "other211" : "false", "notlisted215a" : "false", "other216a" : "false", "toolsaccess225" : "false", "commonlyusedformat216b" : "false", "notlisted223" : "false" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "370e71a8-c9fc-4c29-b4ae-1f780e66d55f", "type" : "other", "additional_properties" : { } }, "description" : "The Green Skills dataset, with data analytics on the demand (EU and Greece) and supply (Greece only) of green skills across various sectors, is for the production of the KPIs produced by interlcomp. This dataset provides a comprehensive view of the current landscape and trends in green skills within the workforce. It highlights areas where skills are in high demand but supply is lacking, guiding policymakers in developing targeted educational and training programs. Additionally, by comparing the EU-wide demand with the specific supply context in Greece, this dataset aids in aligning national policies with broader European sustainability goals, ensuring that the workforce is equipped for the emerging green economy.", "distribution" : [ ], "keyword" : [ "green skills" ], "language" : "eng", "metadata" : [ ], "personal_data" : "unknown", "security_and_privacy" : [ ], "sensitive_data" : "unknown", "technical_resource" : [ ], "title" : "EU-GR Green Skills Dataset", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d", "field-NpLT2" : "true", "metadata" : "true", "commentFieldValue4bea3a8a-d9d2-b7de-cc04-ab698a624641" : "Data for internal project use only", "qualitydataQ219" : "false", "other216a" : "true", "supportForDataReuse" : "false", "name212" : "In the IntelComp project, we employed basic naming conventions to ensure clarity and consistency in data management. These conventions were straightforward, focusing on facilitating easy identification and organization of the datasets. An example of our naming convention is: [DatasetName]_[VersionNumber]_[Date]. ", "commonlyusedformat216b" : "false", "c20efdf5-a4b3-82f3-524a-b2a692c7b593" : "CERIF (Common European Research Information Format)", "c4f19f13-55d6-379c-a359-349625dd74a5" : "false", "commentFieldValuedf877363-aefc-5847-fc55-5c2e33762391" : "No personal data used", "633046ef-1e6f-dbe0-8c5c-43599554943b" : "true", "searchablemetadataQ215" : "false", "openlyaccessibleQ222" : "none", "persistentIdentifiersQ214" : "false", "DatasetSecuritySectionField1" : "Kept on secure, managed storage for limited time", "f57c4211-aab1-9bd7-65eb-249e79a1e13f" : "{\"label\":null,\"name\":\"Dimitris Pappas (orcid:0000-0001-5784-0658)\",\"id\":null,\"reference\":\"0000-0001-5784-0658\",\"status\":0,\"tag\":\"ORCID\",\"key\":\"orcid\",\"hint\":null}", "27555ff8-7d4f-0d90-a925-a9686467ba21" : "true", "1bda2fcb-d63e-16e8-5925-fa2d2df0b923" : "true", "commentFieldValuefieldSet-cf1P1" : "In the case of closed datasets, such as this one, created in the project their metadata will be open and available through the IntelComp Data Catalogue. However, access to this catalogue may be limited.
", "harvestable211a" : "true", "e44e952f-5249-c495-5e84-fa696695849e" : "The project used COAR controlled vocabularies (https://www.coar-repositories.org/) as well as ontologies, following the OpenAIRE example for Resource Description Framework (RDF) compatibility. That was important for the project to allow for successful interlinking of resources across all domains.", "197c2a3e-c92e-beca-acdf-0ecca8bcef94" : "[\"Data Center Archive Storage\"]", "notlistedA211a" : "false", "bdb3ee48-1862-d1ce-d90c-94fbe8c4daab" : "CSV", "documentedProcedures243" : "false", "field-HVLNZ" : "never", "otherField1" : "false", "boolean-will-metadata-use" : "true", "toolsaccess225" : "true", "6e47ae6f-a728-94bf-27df-bb7f76872248" : "[\"Numerical\"]", "to-whom-might-it" : "[\"Decision makers\",\"Economy\",\"The public\",\"Industry\"]", "commentFieldValuefieldSet-Ybwru" : "ESCO Green Skills
", "commentFieldValuefieldSet-jYyLG" : "While the IntelComp project does not have formally documented procedures for data quality assurance, extensive quality checks and curations are inherently part of the process for all Tier 2 and Tier 3 datasets, which are created within the project. These procedures include various methods of ensuring data accuracy, consistency, and reliability. However, the specific processes and checks implemented are not formally documented but are integral to the workflow of data handling and management in the project.
", "field-UmI2l" : "true", "4fef6fbe-f539-e8a8-4e34-0844229b9398" : "true", "other211" : "false", "commentFieldValuefieldSet-dr427" : "Data for internal project use only", "notlisted215a" : "false", "reason242a" : "Data for internal project use only", "metadataAvailableFreeofcharge" : "true", "backup224" : "securewithbackupandrecovery", "ethicallegalissuesQ221" : "true", "name213a" : "CSV", "clearVersionNumbers" : "false", "what-is-the-origin" : "[\"Secondary data\"]", "notlisted223" : "false" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "416c741b-5f18-4c57-97eb-cb8e12f4af0c", "type" : "other", "additional_properties" : { } }, "description" : "This dataset is a curated collection of patents related to the energy and agrifood sectors. Patents are selected based on specific IPC/CPC codes and, in some cases, relevant keywords. Each patent is assigned to one topic according to standard sets. Further refining ensures inclusion of patents with at least one inventor or applicant from an EU member country. The collection provides insights into energy and agrifood inventions within the EU context.
", "language" : "eng", "metadata" : [ ], "title" : "EU Energy & Agrifood Enriched Patent Set", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "e6c5bd92-0f75-45bb-b936-032bd2ab97c0", "type" : "other", "additional_properties" : { } }, "description" : "Semantic Scholar is a search engine for research articles powered by the Allen Institute for Artificial Intelligence. These datasets provide a variety of information about research papers taken from a snapshot in time of the Semantic Scholar corpus. This site is provided by The Allen Institute for Artificial Intelligence as a service to the research community. The site is covered by AI2 Terms of Use and Privacy Policy.
This dataset contains parquet tables for papers, authors, paper_author correspondence, and citations, which are built by merging the following datasets from Semantic Scholar: papers, authors, abstracts, citations.
These are publications from all fields of science available at https://www.semanticscholar.org/. Includes authors, journals and conferences for different publication types since 1931.
", "language" : "eng", "metadata" : [ ], "title" : "Semantic Scholar", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "4c75b91c-a557-43c9-b3bc-e812f77ae290", "type" : "other", "additional_properties" : { } }, "description" : "This dataset provides key indicators about AI scientific production in Europe. It is derived from three main sources: expert-curated topic models using the Interactive Model Trainer (IMT) on OpenAIRE abstracts with AI-focused keywords, OpenAIRE publications with at least one European-affiliated author, and relevant patents from PATSTAT. These indicators have been curated specifically for in-depth analyses within the STI Viewer's Artificial Intelligence dashboards.", "language" : "eng", "metadata" : [ ], "title" : "EU AI Science Indicators", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "f2fb1263-ef2e-461c-9e4b-8c8df6288cd1", "type" : "other", "additional_properties" : { } }, "description" : "Sourced from Eurlex and the Greek National Printing Office, this dataset collates regulations pertinent to the energy and agrifood sectors. Advanced techniques, including crawling and string matching, were utilized to identify and extract relevant regulations along with their metadata. The collection offers a comprehensive insight into the regulatory framework of these sectors across European and Greek jurisdictions.
", "language" : "eng", "metadata" : [ ], "title" : "EU-GR Energy & Agrifood Regulations Set", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "66795914-11c1-4f8f-928b-b0253baaa605", "type" : "other", "additional_properties" : { } }, "description" : "This section outlines the handling and management of input and output data within the project. The datasets are categorized as either new or reused, distinguishing the practices applied by the project team from those applied by others to datasets we received. For datasets created by project partners, detailed information on their FAIRness, the tools and methods used, and data managers is provided. For reused datasets, we describe their components and sources.", "language" : "eng", "metadata" : [ ], "title" : "All data (input and output)", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "313c0bac-7360-42a1-b442-06d167329913", "type" : "other", "additional_properties" : { } }, "description" : "Organizations Data downloaded from the Crunchbase database using an access key provided by Technopolis-Group.", "language" : "eng", "metadata" : [ ], "title" : "Organizations", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "a811b719-efb0-4212-9967-76fc286d6650", "type" : "other", "additional_properties" : { } }, "description" : "This dataset consists of a subset of Semantic Scholar documents in the AI domain created with the Domain Classifier (https://github.com/IntelCompH2020/domain_classification) and Topic Modeler (https://github.com/IntelCompH2020/topicmodeler) toolboxes. First, we used the Domain Classifier to identify AI-related documents and selected those documents for further analysis. Then, we used the TMT with Mallet to create a topic model of 10 topics and extracted the document-topic distribution for each dataset's document. The resulting dataset includes the Semantic Scholar IDs of the documents used for creating the topic model, their associated lemmas, and a string representing the document-topic distribution in the format 'Topic_i|proportion_i Topic_i+1|proportion_i+1 ...'.", "language" : "eng", "metadata" : [ ], "title" : "Topic model of Semantic Scholar documents in the domain of Artificial Intelligence", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "1822fdad-608e-4cfd-8cc7-4df097baca6c", "type" : "other", "additional_properties" : { } }, "description" : "The EU-GR Energy & Agrifood Indicators dataset provides essential indicators for STI policymakers in the energy and agrifood sectors of Europe and Greece. It covers a broad spectrum of societal sectors including Science, Technology, Industry, ESG (Environmental, Social, and Governance), Human Resources, and Policy. Each indicator set is derived from specific analytics: Science indicators are created from publication analytics, Technology from patent analytics, Industry from company/industry analytics, ESGs from ESG analytics, Human Resources from Green Skills analytics, and Policy from regulation analytics.

This dataset, a product of multiple AI-driven pipelines, is derived by processes on raw datasets (Tier 1) and analytics datasets (Tier 2, as described in this DMP) to produce a comprehensive collection of Tier 3 indicators. These indicators provide a detailed view of the current state and trends within the energy and agrifood sectors, assisting policymakers in agenda setting and policy formulation. The data bridges various aspects of societal development and offers a framework for understanding the intersection of science, technology, industry, and human resource development in these critical domains.
", "language" : "eng", "metadata" : [ ], "title" : "EU-GR Energy & Agrifood Indicators", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "bacc49e3-673f-4b9a-a219-9f44d9aa85ed", "type" : "other", "additional_properties" : { } }, "description" : "This dataset aggregates critical indicators pertinent to AI technology within the European Union. It is specifically curated for in-depth analysis within the STI Viewer's Artificial Intelligence dashboards. These indicators emerge from a multifaceted analysis involving an expert-curated topic model using the Interactive Model Trainer (IMT) on PATSTAT patent summaries with AI-specific keywords, AI-related patents granted by the European Patent Office (EPO) as found in PATSTAT, and calculated metrics revolving around AI patents processed and granted by the EPO.", "language" : "eng", "metadata" : [ ], "title" : "EU AI Technology Indicators", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "9ced7ba9-d65d-4881-b463-80e43e509c7c", "type" : "other", "additional_properties" : { } }, "description" : "To create this dataset the SciNoBo classifier was used to classify scientific publications in the domains of Artificial Intelligence (AI) & Energy. 
For more information regarding the algorithm of SciNoBo, please refer to: https://dl.acm.org/doi/abs/10.1145/3487553.3524677 
", "language" : "eng", "metadata" : [ ], "title" : "SciNoBo classified scientific publications (AI & Energy domains)", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "1ad9d988-9e08-4b06-aa88-a45274295640", "type" : "other", "additional_properties" : { } }, "description" : "This dataset compiles key indicators surrounding AI scientific production in Spain. Derived from several primary sources, it includes expert-curated topic models via the Interactive Model Trainer (IMT) on OpenAIRE abstracts with AI-specific keywords, OpenAIRE publications featuring at least one author tied to a Spanish institution, and relevant patents from PATSTAT. These indicators are meticulously crafted for in-depth analyses within the STI Viewer's Artificial Intelligence dashboards.", "language" : "eng", "metadata" : [ ], "title" : "ES AI Science Indicators", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "3159d0a8-93ce-4a93-815a-9ef9cd481a9e", "type" : "other", "additional_properties" : { } }, "description" : "This dataset consists of a series of identifiers for publications in OpenAIRE, publications in Semantic Scholar, patents from PATSTAT and projects from CORDIS which are considered relevant for the cancer domain, and were used to calculate the CA-related indicators. The criterion for selecting these items has been agreed with members of the HCERES team, the lead partner for IntelComp’s cancer living lab. Publications were selected manually by experts in the field, PATENTS were selected using CPC and keywords criteria, and projects were selected based on their links to pre-selected publications. Additionally, machine learning was used to create domain classifiers for this field and these data sets.", "language" : "eng", "metadata" : [ ], "title" : "Cancer publications, patents and EU projects", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d" } }, { "data_quality_assurance" : [ ], "dataset_id" : { "identifier" : "d2a61604-ed95-43bc-88db-210b94edf543", "type" : "other", "additional_properties" : { } }, "description" : "The Climate Change Industry Analytics Dataset for Energy & Agrifood (EU/GR) encompasses per company innovativeness indicators and other metadata crucial for the KPIs produced by Intelcomp for agenda-setting in the energy and agrifood sectors, offering insights for both the European Union and Greece specifically.", "distribution" : [ ], "keyword" : [ "industry", "energy", "energy", "agrifood", "energy", "agrifood", "industry", "innovation" ], "language" : "eng", "metadata" : [ ], "personal_data" : "unknown", "security_and_privacy" : [ ], "sensitive_data" : "unknown", "technical_resource" : [ ], "title" : "EU-GR Energy & Agrifood Industry Analytics", "type" : "DMP Dataset", "additional_properties" : { "template" : "7ecc93d0-e660-46da-9d46-2798a0afde9d", "field-NpLT2" : "true", "metadata" : "true", "commentFieldValue4bea3a8a-d9d2-b7de-cc04-ab698a624641" : "Data for internal project use only", "qualitydataQ219" : "false", "other216a" : "true", "supportForDataReuse" : "false", "name212" : "In the IntelComp project, we employed basic naming conventions to ensure clarity and consistency in data management. These conventions were straightforward, focusing on facilitating easy identification and organization of the datasets. An example of our naming convention is: [DatasetName]_[VersionNumber]_[Date]. ", "commonlyusedformat216b" : "false", "c20efdf5-a4b3-82f3-524a-b2a692c7b593" : "CERIF (Common European Research Information Format)", "c4f19f13-55d6-379c-a359-349625dd74a5" : "false", "commentFieldValuedf877363-aefc-5847-fc55-5c2e33762391" : "No personal data used", "633046ef-1e6f-dbe0-8c5c-43599554943b" : "true", "searchablemetadataQ215" : "false", "openlyaccessibleQ222" : "none", "persistentIdentifiersQ214" : "false", "DatasetSecuritySectionField1" : "Kept on secure, managed storage for limited time", "f57c4211-aab1-9bd7-65eb-249e79a1e13f" : "{\"label\":null,\"name\":\"Dimitris Pappas (orcid:0000-0001-5784-0658)\",\"id\":null,\"reference\":\"0000-0001-5784-0658\",\"status\":0,\"tag\":\"ORCID\",\"key\":\"orcid\",\"hint\":null}", "27555ff8-7d4f-0d90-a925-a9686467ba21" : "true", "1bda2fcb-d63e-16e8-5925-fa2d2df0b923" : "true", "commentFieldValuefieldSet-cf1P1" : "In the case of closed datasets, such as this one, created in the project their metadata will be open and available through the IntelComp Data Catalogue. However, access to this catalogue may be limited.
", "harvestable211a" : "true", "e44e952f-5249-c495-5e84-fa696695849e" : "The project used COAR controlled vocabularies (https://www.coar-repositories.org/) as well as ontologies, following the OpenAIRE example for Resource Description Framework (RDF) compatibility. That was important for the project to allow for successful interlinking of resources across all domains.", "197c2a3e-c92e-beca-acdf-0ecca8bcef94" : "[\"Data Center Archive Storage\"]", "notlistedA211a" : "false", "bdb3ee48-1862-d1ce-d90c-94fbe8c4daab" : "JSON", "documentedProcedures243" : "false", "field-HVLNZ" : "never", "otherField1" : "false", "boolean-will-metadata-use" : "true", "toolsaccess225" : "true", "6e47ae6f-a728-94bf-27df-bb7f76872248" : "[\"Numerical\"]", "to-whom-might-it" : "[\"Decision makers\",\"Economy\",\"The public\",\"Industry\"]", "commentFieldValuefieldSet-Ybwru" : "Fascati Fields of Study, Eurovoc, IPC, NACE
", "commentFieldValuefieldSet-jYyLG" : "While the IntelComp project does not have formally documented procedures for data quality assurance, extensive quality checks and curations are inherently part of the process for all Tier 2 and Tier 3 datasets, which are created within the project. These procedures include various methods of ensuring data accuracy, consistency, and reliability. However, the specific processes and checks implemented are not formally documented but are integral to the workflow of data handling and management in the project.
", "field-UmI2l" : "true", "4fef6fbe-f539-e8a8-4e34-0844229b9398" : "true", "other211" : "false", "commentFieldValuefieldSet-dr427" : "Data for internal project use only", "notlisted215a" : "false", "reason242a" : "Data for internal project use only", "metadataAvailableFreeofcharge" : "true", "backup224" : "securewithbackupandrecovery", "ethicallegalissuesQ221" : "true", "name213a" : "CSV", "clearVersionNumbers" : "false", "what-is-the-origin" : "[\"Secondary data\"]", "notlisted223" : "false" } } ], "description" : "This document serves as the updated Data Management Plan (DMP) for the IntelComp project that has been created using the OpenAIRE ARGOS DMP service (argos.openaire.eu). It presents an in-depth overview of the project's data management practices, in compliance with the Horizon 2020 policy and FAIR guidelines. The DMP details the types of datasets collected, generated, and used, with a particular focus on the datasets created in the project (output datasets), and describes the management methods implemented in the IntelComp STI Data Space.", "dmp_id" : { "identifier" : "6a65e36c-528b-4c8a-b368-e85552f1cda0", "type" : "other", "additional_properties" : { } }, "ethical_issues_exist" : "unknown", "language" : "eng", "modified" : "2023-12-21T15:03:16Z", "project" : [ { "funding" : [ { "funder_id" : { "identifier" : "ec__________::EC||European Commission||EC", "type" : "fundref", "additional_properties" : { } }, "grant_id" : { "identifier" : "corda__h2020::101004870", "type" : "other", "additional_properties" : { } }, "additional_properties" : { } } ], "title" : "A Competitive Intelligence Cloud/HPC Platform for AI-based STI Policy Making", "additional_properties" : { } } ], "title" : "Data Management Plan for IntelComp. D8.2.", "additional_properties" : { "templates" : [ "7ecc93d0-e660-46da-9d46-2798a0afde9d" ] } } }