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2022
Dataset
Unknown
Recommender systems for science: a basic taxonomy
Arezoumandan M., Ghannadrad A., Candela L., Castelli D.
This dataset is accompanying the "Recommender system for science: A basic taxonomy" paper published at IRCDL 2022 conference. This study had a Systematic Mapping Approach on the Recommender system for science. In particular, the study aims at responding to four questions on recommender systems in science cases: users and their interests representation, item typologies and their representation, recommendation algorithms, and evaluation, and then providing a taxonomy. This dataset contains 209 papers of interest that have been published between 2015 and 2022. The dataset has 11 columns which organised as follows: Column Title: This column contains the title of the papers. Column DOI: This column contains the DOI of the papers. Column Publication_year: This column contains the year that the paper is published. Column DB: This column contains the repository that the paper is retrieved. Column Keywords: This column contains the keywords provided for the paper. Column Content_type: This column contains the paper type which can be: Article, Conference or Review. Column Citing_paper_count: This column contains the citation number of the paper. Column Recommended_artefact: This column contains the scientific product that is recommended to users which can be paper, workflow, collaborator, dataset or others. Column User_type: This column contains the type of user who receives the recommendation, which can be an Individual user or a Group of users. Column Algorithm: This column contains the recommendation algorithm that the paper proposed, which can be: HB (Hybrid-based), CB (Content-based), CFB (Collaborative-filtering-based), or GB (Graph-based). Column Evaluation_method: This column contains the method of the algorithm evaluation which can be OFFLINE, ONLINE, BOTH, or NO_EVALUATION.DOI: 10.5281/zenodo.6006905
Project(s): Blue Cloud
, EOSC-Pillar , SoBigData-PlusPlus
Metrics:
Arezoumandan M., Ghannadrad A., Candela L., Castelli D.
This dataset is accompanying the "Recommender system for science: A basic taxonomy" paper published at IRCDL 2022 conference. This study had a Systematic Mapping Approach on the Recommender system for science. In particular, the study aims at responding to four questions on recommender systems in science cases: users and their interests representation, item typologies and their representation, recommendation algorithms, and evaluation, and then providing a taxonomy. This dataset contains 209 papers of interest that have been published between 2015 and 2022. The dataset has 11 columns which organised as follows: Column Title: This column contains the title of the papers. Column DOI: This column contains the DOI of the papers. Column Publication_year: This column contains the year that the paper is published. Column DB: This column contains the repository that the paper is retrieved. Column Keywords: This column contains the keywords provided for the paper. Column Content_type: This column contains the paper type which can be: Article, Conference or Review. Column Citing_paper_count: This column contains the citation number of the paper. Column Recommended_artefact: This column contains the scientific product that is recommended to users which can be paper, workflow, collaborator, dataset or others. Column User_type: This column contains the type of user who receives the recommendation, which can be an Individual user or a Group of users. Column Algorithm: This column contains the recommendation algorithm that the paper proposed, which can be: HB (Hybrid-based), CB (Content-based), CFB (Collaborative-filtering-based), or GB (Graph-based). Column Evaluation_method: This column contains the method of the algorithm evaluation which can be OFFLINE, ONLINE, BOTH, or NO_EVALUATION.DOI: 10.5281/zenodo.6006905
Project(s): Blue Cloud
, EOSC-Pillar , SoBigData-PlusPlus Metrics:
See at: CNR ExploRA
2022
Contribution to conference
Open Access
Recommender systems for science: a basic taxonomy
Arezoumandan M., Ghannadrad A., Candela L., Castelli D.
The ever-growing availability of research artefacts of potential interest for users calls for helpers to assist their discovery. Artefacts of interest vary for the typology, e.g., papers, datasets, software. User interests are multifaceted and evolving. This paper analyses and classifies studies on recommender systems exploited to suggest research artefacts to researchers regarding the type of algorithm, users and their representations, item typologies and their representation, and evaluation methods used to assess the effectiveness of the recommendations. This study found that most of the current scientific artefacts recommender system focused only on recommending paper to individual researchers, just a few papers focused on dataset recommendation and software recommender system is unprecedented.Source: IRCDL 2022 - 18th Italian Research Conference on Digital Libraries, Padova, Italy, 24-25/02/2022
Project(s): Blue Cloud, EOSC-Pillar , SoBigData-PlusPlus
Arezoumandan M., Ghannadrad A., Candela L., Castelli D.
The ever-growing availability of research artefacts of potential interest for users calls for helpers to assist their discovery. Artefacts of interest vary for the typology, e.g., papers, datasets, software. User interests are multifaceted and evolving. This paper analyses and classifies studies on recommender systems exploited to suggest research artefacts to researchers regarding the type of algorithm, users and their representations, item typologies and their representation, and evaluation methods used to assess the effectiveness of the recommendations. This study found that most of the current scientific artefacts recommender system focused only on recommending paper to individual researchers, just a few papers focused on dataset recommendation and software recommender system is unprecedented.Source: IRCDL 2022 - 18th Italian Research Conference on Digital Libraries, Padova, Italy, 24-25/02/2022
Project(s): Blue Cloud
, EOSC-Pillar , SoBigData-PlusPlus
See at:
ISTI Repository 
| CNR ExploRA
2022
Conference article
Open Access
Recommender systems for science: a basic taxonomy
Ghannadrad A., Arezoumandan M., Candela L., Castelli D.
The ever-growing availability of research artefacts of potential interest for users calls for helpers to assist their discovery. Artefacts of interest vary for the typology, e.g. papers, datasets, software. User interests are multifaceted and evolving. This paper analyses and classifies studies on recommender systems exploited to suggest research artefacts to researchers regarding the type of algorithm, users and their representations, item typologies and their representation, and evaluation methods used to assess the effectiveness of the recommendations. This study found that most of the current scientific artefacts recommender system focused only on recommending paper to individual researchers, just a few papers focused on dataset recommendation and software recommender system is unprecedented.Source: IRCDL 2022 - 18th Italian Research Conference on Digital Libraries, Padua, Italy, 24-25/02/2022
Project(s): Blue Cloud, EOSC-Pillar , SoBigData-PlusPlus
Ghannadrad A., Arezoumandan M., Candela L., Castelli D.
The ever-growing availability of research artefacts of potential interest for users calls for helpers to assist their discovery. Artefacts of interest vary for the typology, e.g. papers, datasets, software. User interests are multifaceted and evolving. This paper analyses and classifies studies on recommender systems exploited to suggest research artefacts to researchers regarding the type of algorithm, users and their representations, item typologies and their representation, and evaluation methods used to assess the effectiveness of the recommendations. This study found that most of the current scientific artefacts recommender system focused only on recommending paper to individual researchers, just a few papers focused on dataset recommendation and software recommender system is unprecedented.Source: IRCDL 2022 - 18th Italian Research Conference on Digital Libraries, Padua, Italy, 24-25/02/2022
Project(s): Blue Cloud
, EOSC-Pillar , SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Dataset
Unknown
Virtual research environments ethnography: a preliminary study
Arezoumandan M., Candela L., Castelli D., Ghannadrad A., Mangione D., Pagano P.
This dataset is accompanying the paper "Virtual Research Environments Ethnography: a Preliminary Study" paper published at 14th International Workshop on Science Gateways 15th-17th June 2022, Trento, Italy. This is a systematic mapping study on the literature about Science gateways, Virtual Research Environments, and Virtual Laboratories.DOI: 10.5281/zenodo.6481183
Project(s): Blue Cloud, EOSC-Pillar , DESIRA , SoBigData-PlusPlus
Metrics:
Arezoumandan M., Candela L., Castelli D., Ghannadrad A., Mangione D., Pagano P.
This dataset is accompanying the paper "Virtual Research Environments Ethnography: a Preliminary Study" paper published at 14th International Workshop on Science Gateways 15th-17th June 2022, Trento, Italy. This is a systematic mapping study on the literature about Science gateways, Virtual Research Environments, and Virtual Laboratories.DOI: 10.5281/zenodo.6481183
Project(s): Blue Cloud
, EOSC-Pillar , DESIRA , SoBigData-PlusPlus Metrics:
See at: CNR ExploRA
2022
Conference article
Open Access
Virtual research environments ethnography: a preliminary study
Arezoumandan M., Candela L., Castelli D., Ghannadrad A., Mangione D., Pagano P.
Virtual Research Environments, Science Gateways and Virtual Laboratories are systems aiming at serving the needs of their designated communities of practice by providing them with a working environment for performing their tasks. These systems have been proposed and exploited in diverse application domains and scopes ranging from education to simulation, collaboration, and open science. This paper analyses the literature published from 2010 to start characterising this manifold family of systems. In particular, the study identified and analysed a corpus of 1167 research papers to highlight their distribution over time, the most frequent publication venues and the characterising topics.Source: IWSG 2022 - 14th International Workshop on Science Gateways, Trento, Italy, 15-17/06/2022
DOI: 10.5281/zenodo.7883104
Project(s): Blue Cloud, EOSC-Pillar , DESIRA , SoBigData-PlusPlus
Metrics:
Arezoumandan M., Candela L., Castelli D., Ghannadrad A., Mangione D., Pagano P.
Virtual Research Environments, Science Gateways and Virtual Laboratories are systems aiming at serving the needs of their designated communities of practice by providing them with a working environment for performing their tasks. These systems have been proposed and exploited in diverse application domains and scopes ranging from education to simulation, collaboration, and open science. This paper analyses the literature published from 2010 to start characterising this manifold family of systems. In particular, the study identified and analysed a corpus of 1167 research papers to highlight their distribution over time, the most frequent publication venues and the characterising topics.Source: IWSG 2022 - 14th International Workshop on Science Gateways, Trento, Italy, 15-17/06/2022
DOI: 10.5281/zenodo.7883104
Project(s): Blue Cloud
, EOSC-Pillar , DESIRA , SoBigData-PlusPlus Metrics:
See at:
ISTI Repository | ZENODO | CNR ExploRA