�ݺ�ߣshows by User: anusuriya

�ݺ�ߣshows by User: anusuriya / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: anusuriya / Mon, 06 Jun 2022 08:08:07 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: anusuriya FAIR – Assessment or Improvement? /slideshow/fair-assessment-or-improvement/251926938 fairdatahmcasd-220606080807-060de3bc
Funders, publishers, and data service providers have strongly endorsed applying FAIR principles to maximize the reuse of research data since the principles were published in 2016. Much of existing work on FAIR assessment focuses on "what" needs to be measured, which led to the development of assessment metrics. However, the questions of "how" to measure the FAIRness of the research data and use the assessment results to improve data reuse haven't been fully demonstrated in practice yet. This presentation will cover some insights on these aspects derived from the development of a practical solution (F-UJI) to measure the progress of FAIR aspects of data programmatically.]]>
Funders, publishers, and data service providers have strongly endorsed applying FAIR principles to maximize the reuse of research data since the principles were published in 2016. Much of existing work on FAIR assessment focuses on "what" needs to be measured, which led to the development of assessment metrics. However, the questions of "how" to measure the FAIRness of the research data and use the assessment results to improve data reuse haven't been fully demonstrated in practice yet. This presentation will cover some insights on these aspects derived from the development of a practical solution (F-UJI) to measure the progress of FAIR aspects of data programmatically.]]> Mon, 06 Jun 2022 08:08:07 GMT /slideshow/fair-assessment-or-improvement/251926938 anusuriya@slideshare.net(anusuriya) FAIR – Assessment or Improvement? anusuriya Funders, publishers, and data service providers have strongly endorsed applying FAIR principles to maximize the reuse of research data since the principles were published in 2016. Much of existing work on FAIR assessment focuses on "what" needs to be measured, which led to the development of assessment metrics. However, the questions of "how" to measure the FAIRness of the research data and use the assessment results to improve data reuse haven't been fully demonstrated in practice yet. This presentation will cover some insights on these aspects derived from the development of a practical solution (F-UJI) to measure the progress of FAIR aspects of data programmatically. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fairdatahmcasd-220606080807-060de3bc-thumbnail.jpg?width=120&height=120&fit=bounds" /> Funders, publishers, and data service providers have strongly endorsed applying FAIR principles to maximize the reuse of research data since the principles were published in 2016. Much of existing work on FAIR assessment focuses on "what" needs to be measured, which led to the development of assessment metrics. However, the questions of "how" to measure the FAIRness of the research data and use the assessment results to improve data reuse haven't been fully demonstrated in practice yet. This presentation will cover some insights on these aspects derived from the development of a practical solution (F-UJI) to measure the progress of FAIR aspects of data programmatically.

FAIR – Assessment or Improvement? from Anusuriya Devaraju

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0 Simple Steps to Effective Research Data Sharing /slideshow/simple-steps-to-effective-research-data-sharing/250712104 resbaz2021asd-211123224050
Key story at ResBaz 2021 Sydney]]>
Key story at ResBaz 2021 Sydney]]> Tue, 23 Nov 2021 22:40:49 GMT /slideshow/simple-steps-to-effective-research-data-sharing/250712104 anusuriya@slideshare.net(anusuriya) Simple Steps to Effective Research Data Sharing anusuriya Key story at ResBaz 2021 Sydney <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/resbaz2021asd-211123224050-thumbnail.jpg?width=120&height=120&fit=bounds" /> Key story at ResBaz 2021 Sydney

Simple Steps to Effective Research Data Sharing from Anusuriya Devaraju

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0 F-UJI : An Automated Assessment Tool for Improving the FAIRness of Research Data /slideshow/fuji-an-automated-assessment-tool-for-improving-the-fairness-of-research-data/238684071 fairsfairfuji30092020-200930130928
A tool to support a programmatic assessment of the FAIRness of research data.]]>
A tool to support a programmatic assessment of the FAIRness of research data.]]> Wed, 30 Sep 2020 13:09:28 GMT /slideshow/fuji-an-automated-assessment-tool-for-improving-the-fairness-of-research-data/238684071 anusuriya@slideshare.net(anusuriya) F-UJI : An Automated Assessment Tool for Improving the FAIRness of Research Data anusuriya A tool to support a programmatic assessment of the FAIRness of research data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fairsfairfuji30092020-200930130928-thumbnail.jpg?width=120&height=120&fit=bounds" /> A tool to support a programmatic assessment of the FAIRness of research data.

F-UJI : An Automated Assessment Tool for Improving the FAIRness of Research Data from Anusuriya Devaraju

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0 An Automated Assessment of the FAIRness of Research Data /slideshow/an-automated-assessment-of-the-fairness-of-research-data/238433087 fairsfair52ngeospatialsensing-200909165707
Talk presented at Geospatial Sensing 2020]]>
Talk presented at Geospatial Sensing 2020]]> Wed, 09 Sep 2020 16:57:06 GMT /slideshow/an-automated-assessment-of-the-fairness-of-research-data/238433087 anusuriya@slideshare.net(anusuriya) An Automated Assessment of the FAIRness of Research Data anusuriya Talk presented at Geospatial Sensing 2020 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fairsfair52ngeospatialsensing-200909165707-thumbnail.jpg?width=120&height=120&fit=bounds" /> Talk presented at Geospatial Sensing 2020

An Automated Assessment of the FAIRness of Research Data from Anusuriya Devaraju

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0 Towards A Web-Enabled Geo-Sample Web: An Open Source Resource Registration and Management System for Connecting Geo-Samples to the Web /slideshow/towards-a-webenabled-geosample-web-an-open-source-resource-registration-and-management-system-for-connecting-geosamples-to-the-web-80681443/80681443 foss4g2017-171011062819
Within the earth sciences the curation and sharing of geo-samples is crucial to supporting reproducible research, in addition to extending the use of the samples in new research, and saving costs by avoiding sample loss and duplicating sampling activities. In the Commonwealth Scientic and Industrial Research Organisation (CSIRO), researchers gather various geo-samples as part of their eld studies and collaborative projects. The diversity of the samples and their unsystematic management led ambiguous sample numbers, incomplete sample descriptions, and diculties in nding the samples and their related data. These problems are also found in universities, research institutes and government agencies, which usually curate and manage diverse samples. To address this problem, we developed an open source registration and management system to identify geo-samples unambiguously and to manage their metadata and data systematically. The system supports the linking of samples and sample collections to the real world features from where they were collected, as well as to their data and reports on the Web. This paper describes the implementation of the system including its underlying design considerations, and its applications. The system was built upon the International Geo Sample Number persistent identier system with Semantic Web technologies. It has been implemented and tested with individual users and three sample repositories in the organization.]]>
Within the earth sciences the curation and sharing of geo-samples is crucial to supporting reproducible research, in addition to extending the use of the samples in new research, and saving costs by avoiding sample loss and duplicating sampling activities. In the Commonwealth Scientic and Industrial Research Organisation (CSIRO), researchers gather various geo-samples as part of their eld studies and collaborative projects. The diversity of the samples and their unsystematic management led ambiguous sample numbers, incomplete sample descriptions, and diculties in nding the samples and their related data. These problems are also found in universities, research institutes and government agencies, which usually curate and manage diverse samples. To address this problem, we developed an open source registration and management system to identify geo-samples unambiguously and to manage their metadata and data systematically. The system supports the linking of samples and sample collections to the real world features from where they were collected, as well as to their data and reports on the Web. This paper describes the implementation of the system including its underlying design considerations, and its applications. The system was built upon the International Geo Sample Number persistent identier system with Semantic Web technologies. It has been implemented and tested with individual users and three sample repositories in the organization.]]> Wed, 11 Oct 2017 06:28:19 GMT /slideshow/towards-a-webenabled-geosample-web-an-open-source-resource-registration-and-management-system-for-connecting-geosamples-to-the-web-80681443/80681443 anusuriya@slideshare.net(anusuriya) Towards A Web-Enabled Geo-Sample Web: An Open Source Resource Registration and Management System for Connecting Geo-Samples to the Web anusuriya Within the earth sciences the curation and sharing of geo-samples is crucial to supporting reproducible research, in addition to extending the use of the samples in new research, and saving costs by avoiding sample loss and duplicating sampling activities. In the Commonwealth Scienti�c and Industrial Research Organisation (CSIRO), researchers gather various geo-samples as part of their �eld studies and collaborative projects. The diversity of the samples and their unsystematic management led ambiguous sample numbers, incomplete sample descriptions, and di�culties in �nding the samples and their related data. These problems are also found in universities, research institutes and government agencies, which usually curate and manage diverse samples. To address this problem, we developed an open source registration and management system to identify geo-samples unambiguously and to manage their metadata and data systematically. The system supports the linking of samples and sample collections to the real world features from where they were collected, as well as to their data and reports on the Web. This paper describes the implementation of the system including its underlying design considerations, and its applications. The system was built upon the International Geo Sample Number persistent identi�er system with Semantic Web technologies. It has been implemented and tested with individual users and three sample repositories in the organization. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/foss4g2017-171011062819-thumbnail.jpg?width=120&height=120&fit=bounds" /> Within the earth sciences the curation and sharing of geo-samples is crucial to supporting reproducible research, in addition to extending the use of the samples in new research, and saving costs by avoiding sample loss and duplicating sampling activities. In the Commonwealth Scienti�c and Industrial Research Organisation (CSIRO), researchers gather various geo-samples as part of their �eld studies and collaborative projects. The diversity of the samples and their unsystematic management led ambiguous sample numbers, incomplete sample descriptions, and di�culties in �nding the samples and their related data. These problems are also found in universities, research institutes and government agencies, which usually curate and manage diverse samples. To address this problem, we developed an open source registration and management system to identify geo-samples unambiguously and to manage their metadata and data systematically. The system supports the linking of samples and sample collections to the real world features from where they were collected, as well as to their data and reports on the Web. This paper describes the implementation of the system including its underlying design considerations, and its applications. The system was built upon the International Geo Sample Number persistent identi�er system with Semantic Web technologies. It has been implemented and tested with individual users and three sample repositories in the organization.

Towards A Web-Enabled Geo-Sample Web: An Open Source Resource Registration and Management System for Connecting Geo-Samples to the Web from Anusuriya Devaraju

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0 Data You May Like: A Recommender System for Research Data Discovery /slideshow/data-you-may-like-a-recommender-system-for-research-data-discovery/70543761 agu2016recsys-161230065658
Various data portals been developed to facilitate access to research datasets from different sources. For example, the Data Publisher for Earth & Environmental Science (PANGAEA), the Registry of Research Data Repositories (re3data.org), and the National Geoscience Data Centre (NGDC). Due to data quantity and heterogeneity, finding relevant datasets on these portals may be difficult and tedious. Keyword searches based on specific metadata elements or multi-key indexes may return irrelevant results. Faceted searches may be unsatisfactory and time consuming, especially when facet values are exhaustive. We need a much more intelligent way to complement existing searching mechanisms in order to enhance user experiences of the data portals. We developed a recommender system that helps users to find the most relevant research datasets on the CSIRO’s Data Access Portal (DAP). The system is based on content-based filtering. We computed the similarity of datasets based on data attributes (e.g., descriptions, fields of research, location, contributors, and provenance) and inference from transaction logs (e.g., the relations among datasets and between queries and datasets). We improved the recommendation quality by assigning weights to data similarities. The weight values are drawn from a survey involving data users. The recommender results for a given dataset are accessible programmatically via a web service. Taking both data attributes and user actions into account, the recommender system will make it easier for researchers to find and reuse data offered through the data portal.]]>
Various data portals been developed to facilitate access to research datasets from different sources. For example, the Data Publisher for Earth & Environmental Science (PANGAEA), the Registry of Research Data Repositories (re3data.org), and the National Geoscience Data Centre (NGDC). Due to data quantity and heterogeneity, finding relevant datasets on these portals may be difficult and tedious. Keyword searches based on specific metadata elements or multi-key indexes may return irrelevant results. Faceted searches may be unsatisfactory and time consuming, especially when facet values are exhaustive. We need a much more intelligent way to complement existing searching mechanisms in order to enhance user experiences of the data portals. We developed a recommender system that helps users to find the most relevant research datasets on the CSIRO’s Data Access Portal (DAP). The system is based on content-based filtering. We computed the similarity of datasets based on data attributes (e.g., descriptions, fields of research, location, contributors, and provenance) and inference from transaction logs (e.g., the relations among datasets and between queries and datasets). We improved the recommendation quality by assigning weights to data similarities. The weight values are drawn from a survey involving data users. The recommender results for a given dataset are accessible programmatically via a web service. Taking both data attributes and user actions into account, the recommender system will make it easier for researchers to find and reuse data offered through the data portal.]]> Fri, 30 Dec 2016 06:56:57 GMT /slideshow/data-you-may-like-a-recommender-system-for-research-data-discovery/70543761 anusuriya@slideshare.net(anusuriya) Data You May Like: A Recommender System for Research Data Discovery anusuriya Various data portals been developed to facilitate access to research datasets from different sources. For example, the Data Publisher for Earth & Environmental Science (PANGAEA), the Registry of Research Data Repositories (re3data.org), and the National Geoscience Data Centre (NGDC). Due to data quantity and heterogeneity, finding relevant datasets on these portals may be difficult and tedious. Keyword searches based on specific metadata elements or multi-key indexes may return irrelevant results. Faceted searches may be unsatisfactory and time consuming, especially when facet values are exhaustive. We need a much more intelligent way to complement existing searching mechanisms in order to enhance user experiences of the data portals. We developed a recommender system that helps users to find the most relevant research datasets on the CSIRO’s Data Access Portal (DAP). The system is based on content-based filtering. We computed the similarity of datasets based on data attributes (e.g., descriptions, fields of research, location, contributors, and provenance) and inference from transaction logs (e.g., the relations among datasets and between queries and datasets). We improved the recommendation quality by assigning weights to data similarities. The weight values are drawn from a survey involving data users. The recommender results for a given dataset are accessible programmatically via a web service. Taking both data attributes and user actions into account, the recommender system will make it easier for researchers to find and reuse data offered through the data portal. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agu2016recsys-161230065658-thumbnail.jpg?width=120&height=120&fit=bounds" /> Various data portals been developed to facilitate access to research datasets from different sources. For example, the Data Publisher for Earth & Environmental Science (PANGAEA), the Registry of Research Data Repositories (re3data.org), and the National Geoscience Data Centre (NGDC). Due to data quantity and heterogeneity, finding relevant datasets on these portals may be difficult and tedious. Keyword searches based on specific metadata elements or multi-key indexes may return irrelevant results. Faceted searches may be unsatisfactory and time consuming, especially when facet values are exhaustive. We need a much more intelligent way to complement existing searching mechanisms in order to enhance user experiences of the data portals. We developed a recommender system that helps users to find the most relevant research datasets on the CSIRO’s Data Access Portal (DAP). The system is based on content-based filtering. We computed the similarity of datasets based on data attributes (e.g., descriptions, fields of research, location, contributors, and provenance) and inference from transaction logs (e.g., the relations among datasets and between queries and datasets). We improved the recommendation quality by assigning weights to data similarities. The weight values are drawn from a survey involving data users. The recommender results for a given dataset are accessible programmatically via a web service. Taking both data attributes and user actions into account, the recommender system will make it easier for researchers to find and reuse data offered through the data portal.

Data You May Like: A Recommender System for Research Data Discovery from Anusuriya Devaraju

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0 Web-enabled Physical Samples: Curating and Publishing Physical Samples in CSIRO /slideshow/webenabled-physical-samples-curating-and-publishing-physical-samples-in-csiro/67264562 eresearch2016igsn-161017031633
The Implementation and Applications of IGSN in CSIRO]]>
The Implementation and Applications of IGSN in CSIRO]]> Mon, 17 Oct 2016 03:16:32 GMT /slideshow/webenabled-physical-samples-curating-and-publishing-physical-samples-in-csiro/67264562 anusuriya@slideshare.net(anusuriya) Web-enabled Physical Samples: Curating and Publishing Physical Samples in CSIRO anusuriya The Implementation and Applications of IGSN in CSIRO <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eresearch2016igsn-161017031633-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Implementation and Applications of IGSN in CSIRO

Web-enabled Physical Samples: Curating and Publishing Physical Samples in CSIRO from Anusuriya Devaraju

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0 The Implementation of the International Geo Sample Number in CSIRO: Experiences and Future Outlook /slideshow/the-implementation-of-the-international-geo-samplenumber-in-csiro-experiences-and-future-outlook/63509546 psdl2016final-160628050818
In 2014 the Commonwealth Scientific and Industrial Research Organisation (CSIRO) began to implement the International Geo Sample Number (IGSN) to allow unambiguous identification of physical samples and data derived from these samples. In this paper we describe the requirements for the implementation of persistent identifiers for physical samples in the organisation and technical solutions we developed to meet these requirements.]]>
In 2014 the Commonwealth Scientific and Industrial Research Organisation (CSIRO) began to implement the International Geo Sample Number (IGSN) to allow unambiguous identification of physical samples and data derived from these samples. In this paper we describe the requirements for the implementation of persistent identifiers for physical samples in the organisation and technical solutions we developed to meet these requirements.]]> Tue, 28 Jun 2016 05:08:18 GMT /slideshow/the-implementation-of-the-international-geo-samplenumber-in-csiro-experiences-and-future-outlook/63509546 anusuriya@slideshare.net(anusuriya) The Implementation of the International Geo Sample Number in CSIRO: Experiences and Future Outlook anusuriya In 2014 the Commonwealth Scientific and Industrial Research Organisation (CSIRO) began to implement the International Geo Sample Number (IGSN) to allow unambiguous identification of physical samples and data derived from these samples. In this paper we describe the requirements for the implementation of persistent identifiers for physical samples in the organisation and technical solutions we developed to meet these requirements. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/psdl2016final-160628050818-thumbnail.jpg?width=120&height=120&fit=bounds" /> In 2014 the Commonwealth Scientific and Industrial Research Organisation (CSIRO) began to implement the International Geo Sample Number (IGSN) to allow unambiguous identification of physical samples and data derived from these samples. In this paper we describe the requirements for the implementation of persistent identifiers for physical samples in the organisation and technical solutions we developed to meet these requirements.

The Implementation of the International Geo Sample Number in CSIRO: Experiences and Future Outlook from Anusuriya Devaraju

]]> 270 6 https://cdn.slidesharecdn.com/ss_thumbnails/psdl2016final-160628050818-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Publishing Physical Sample Records on the Web /slideshow/publishing-physical-sample-records-on-the-web/59248025 cssstcp-160308093405
This presentation covers registration and publication of samples with the International Geo Sample Number (IGSN) in CSIRO.]]>
This presentation covers registration and publication of samples with the International Geo Sample Number (IGSN) in CSIRO.]]> Tue, 08 Mar 2016 09:34:05 GMT /slideshow/publishing-physical-sample-records-on-the-web/59248025 anusuriya@slideshare.net(anusuriya) Publishing Physical Sample Records on the Web anusuriya This presentation covers registration and publication of samples with the International Geo Sample Number (IGSN) in CSIRO. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cssstcp-160308093405-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation covers registration and publication of samples with the International Geo Sample Number (IGSN) in CSIRO.

Publishing Physical Sample Records on the Web from Anusuriya Devaraju

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0 Using Feedback from Data Consumers to Capture Quality Information on Environmental Research Data /anusuriya/using-feedback-from-data-consumers-to-capture-quality-information-on-environmental-research-data agu2015asd2-160112013841
Data quality information is essential to facilitate reuse of Earth science data. Recorded quality information must be sufficient for other researchers to select suitable data sets for their analysis and confirm the results and conclusions. In the research data ecosystem, several entities are responsible for data quality. Data producers (researchers and agencies) play a major role in this aspect as they often include validation checks or data cleaning as part of their work. It is possible that the quality information is not supplied with published data sets; if it is available, the descriptions might be incomplete, ambiguous or address specific quality aspects. Data repositories have built infrastructures to share data, but not all of them assess data quality. They normally provide guidelines of documenting quality information. Some suggests that scholarly and data journals should take a role in ensuring data quality by involving reviewers to assess data sets used in articles, and incorporating data quality criteria in the author guidelines. However, this mechanism primarily addresses data sets submitted to journals. We believe that data consumers will complement existing entities to assess and document the quality of published data sets. This has been adopted in crowd-source platforms such as Zooniverse, OpenStreetMap, Wikipedia, Mechanical Turk and Tomnod. This paper presents a framework designed based on open source tools to capture and share data users’ feedback on the application and assessment of research data. The framework comprises a browser plug-in, a web service and a data model such that feedback can be easily reported, retrieved and searched. The feedback records are also made available as Linked Data to promote integration with other sources on the Web. Vocabularies from Dublin Core and PROV-O are used to clarify the source and attribution of feedback. The application of the framework is illustrated with the CSIRO’s Data Access Portal. ]]>
Data quality information is essential to facilitate reuse of Earth science data. Recorded quality information must be sufficient for other researchers to select suitable data sets for their analysis and confirm the results and conclusions. In the research data ecosystem, several entities are responsible for data quality. Data producers (researchers and agencies) play a major role in this aspect as they often include validation checks or data cleaning as part of their work. It is possible that the quality information is not supplied with published data sets; if it is available, the descriptions might be incomplete, ambiguous or address specific quality aspects. Data repositories have built infrastructures to share data, but not all of them assess data quality. They normally provide guidelines of documenting quality information. Some suggests that scholarly and data journals should take a role in ensuring data quality by involving reviewers to assess data sets used in articles, and incorporating data quality criteria in the author guidelines. However, this mechanism primarily addresses data sets submitted to journals. We believe that data consumers will complement existing entities to assess and document the quality of published data sets. This has been adopted in crowd-source platforms such as Zooniverse, OpenStreetMap, Wikipedia, Mechanical Turk and Tomnod. This paper presents a framework designed based on open source tools to capture and share data users’ feedback on the application and assessment of research data. The framework comprises a browser plug-in, a web service and a data model such that feedback can be easily reported, retrieved and searched. The feedback records are also made available as Linked Data to promote integration with other sources on the Web. Vocabularies from Dublin Core and PROV-O are used to clarify the source and attribution of feedback. The application of the framework is illustrated with the CSIRO’s Data Access Portal. ]]> Tue, 12 Jan 2016 01:38:41 GMT /anusuriya/using-feedback-from-data-consumers-to-capture-quality-information-on-environmental-research-data anusuriya@slideshare.net(anusuriya) Using Feedback from Data Consumers to Capture Quality Information on Environmental Research Data anusuriya Data quality information is essential to facilitate reuse of Earth science data. Recorded quality information must be sufficient for other researchers to select suitable data sets for their analysis and confirm the results and conclusions. In the research data ecosystem, several entities are responsible for data quality. Data producers (researchers and agencies) play a major role in this aspect as they often include validation checks or data cleaning as part of their work. It is possible that the quality information is not supplied with published data sets; if it is available, the descriptions might be incomplete, ambiguous or address specific quality aspects. Data repositories have built infrastructures to share data, but not all of them assess data quality. They normally provide guidelines of documenting quality information. Some suggests that scholarly and data journals should take a role in ensuring data quality by involving reviewers to assess data sets used in articles, and incorporating data quality criteria in the author guidelines. However, this mechanism primarily addresses data sets submitted to journals. We believe that data consumers will complement existing entities to assess and document the quality of published data sets. This has been adopted in crowd-source platforms such as Zooniverse, OpenStreetMap, Wikipedia, Mechanical Turk and Tomnod. This paper presents a framework designed based on open source tools to capture and share data users’ feedback on the application and assessment of research data. The framework comprises a browser plug-in, a web service and a data model such that feedback can be easily reported, retrieved and searched. The feedback records are also made available as Linked Data to promote integration with other sources on the Web. Vocabularies from Dublin Core and PROV-O are used to clarify the source and attribution of feedback. The application of the framework is illustrated with the CSIRO’s Data Access Portal. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/agu2015asd2-160112013841-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data quality information is essential to facilitate reuse of Earth science data. Recorded quality information must be sufficient for other researchers to select suitable data sets for their analysis and confirm the results and conclusions. In the research data ecosystem, several entities are responsible for data quality. Data producers (researchers and agencies) play a major role in this aspect as they often include validation checks or data cleaning as part of their work. It is possible that the quality information is not supplied with published data sets; if it is available, the descriptions might be incomplete, ambiguous or address specific quality aspects. Data repositories have built infrastructures to share data, but not all of them assess data quality. They normally provide guidelines of documenting quality information. Some suggests that scholarly and data journals should take a role in ensuring data quality by involving reviewers to assess data sets used in articles, and incorporating data quality criteria in the author guidelines. However, this mechanism primarily addresses data sets submitted to journals. We believe that data consumers will complement existing entities to assess and document the quality of published data sets. This has been adopted in crowd-source platforms such as Zooniverse, OpenStreetMap, Wikipedia, Mechanical Turk and Tomnod. This paper presents a framework designed based on open source tools to capture and share data users’ feedback on the application and assessment of research data. The framework comprises a browser plug-in, a web service and a data model such that feedback can be easily reported, retrieved and searched. The feedback records are also made available as Linked Data to promote integration with other sources on the Web. Vocabularies from Dublin Core and PROV-O are used to clarify the source and attribution of feedback. The application of the framework is illustrated with the CSIRO’s Data Access Portal.

Using Feedback from Data Consumers to Capture Quality Information on Environmental Research Data from Anusuriya Devaraju

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0 CAPTURING DATA PROVENANCE WITH A USER-DRIVEN FEEDBACK APPROACH /slideshow/capturing-data-provenance-with-a-userdriven-feedback-approach/55916396 modsim2015asd-151208024255-lva1-app6891
Various portals have been developed to provide an easy way to discover and access public research data sets from various organizations. Data sets are made available with descriptive metadata based on common (e.g., OGC, CUAHSI, FGDC, INSPIRE, ISO, Dublin Core) or proprietary standards to facilitate better understanding and use of the data sets. Provenance descriptions may be included as part of the metadata and are specified from a data provider’s perspective. These can include, for example, different entities and activities involved in a data creation flow, such as sensing platforms, personnel, and data calculation and transformation processes. Moving beyond the provider-centric descriptions, data provenance may be complemented with forward provenance records supplied by data consumers. The records may be gathered via a user-driven feedback approach. The feedback information from data consumers gives valuable insights into application and assessment of published data sets. This might include descriptions about a scientific analysis in which the data sets were used, the corrected version of an actual data set or any discovered issues and suggestions concerning the quality of the published data sets. Data providers might then use this information to handle erroneous data and improve existing metadata, their data collection and processing methods. Contributors can use the feedback channel to share their scientific analyses. Data consumers can learn more about data sets based on other people’s experiences, and potentially save time by avoiding the need for interpreting or cleaning data sets. The goals of the study are to capture feedback from data users on published research data sets, link this to actual data sets, and finally support search and discovery of research data using feedback information. This paper reports preliminary results addressing the goals. We provide a summary of current practices on gathering feedback from end-users on research data portals, and discuss their relevance and limitations. Examples from the Earth Science domain on how commentaries from data users might be useful in practice are also included. Then, we present a data model representing key aspects of user feedback. We propose a system architecture to gather and manage feedback from end-users. We describe how the core PROV model may be used to represent the provenance of user feedback information. Technical solutions for linking feedback to existing data portals are also specified.]]>
Various portals have been developed to provide an easy way to discover and access public research data sets from various organizations. Data sets are made available with descriptive metadata based on common (e.g., OGC, CUAHSI, FGDC, INSPIRE, ISO, Dublin Core) or proprietary standards to facilitate better understanding and use of the data sets. Provenance descriptions may be included as part of the metadata and are specified from a data provider’s perspective. These can include, for example, different entities and activities involved in a data creation flow, such as sensing platforms, personnel, and data calculation and transformation processes. Moving beyond the provider-centric descriptions, data provenance may be complemented with forward provenance records supplied by data consumers. The records may be gathered via a user-driven feedback approach. The feedback information from data consumers gives valuable insights into application and assessment of published data sets. This might include descriptions about a scientific analysis in which the data sets were used, the corrected version of an actual data set or any discovered issues and suggestions concerning the quality of the published data sets. Data providers might then use this information to handle erroneous data and improve existing metadata, their data collection and processing methods. Contributors can use the feedback channel to share their scientific analyses. Data consumers can learn more about data sets based on other people’s experiences, and potentially save time by avoiding the need for interpreting or cleaning data sets. The goals of the study are to capture feedback from data users on published research data sets, link this to actual data sets, and finally support search and discovery of research data using feedback information. This paper reports preliminary results addressing the goals. We provide a summary of current practices on gathering feedback from end-users on research data portals, and discuss their relevance and limitations. Examples from the Earth Science domain on how commentaries from data users might be useful in practice are also included. Then, we present a data model representing key aspects of user feedback. We propose a system architecture to gather and manage feedback from end-users. We describe how the core PROV model may be used to represent the provenance of user feedback information. Technical solutions for linking feedback to existing data portals are also specified.]]> Tue, 08 Dec 2015 02:42:55 GMT /slideshow/capturing-data-provenance-with-a-userdriven-feedback-approach/55916396 anusuriya@slideshare.net(anusuriya) CAPTURING DATA PROVENANCE WITH A USER-DRIVEN FEEDBACK APPROACH anusuriya Various portals have been developed to provide an easy way to discover and access public research data sets from various organizations. Data sets are made available with descriptive metadata based on common (e.g., OGC, CUAHSI, FGDC, INSPIRE, ISO, Dublin Core) or proprietary standards to facilitate better understanding and use of the data sets. Provenance descriptions may be included as part of the metadata and are specified from a data provider’s perspective. These can include, for example, different entities and activities involved in a data creation flow, such as sensing platforms, personnel, and data calculation and transformation processes. Moving beyond the provider-centric descriptions, data provenance may be complemented with forward provenance records supplied by data consumers. The records may be gathered via a user-driven feedback approach. The feedback information from data consumers gives valuable insights into application and assessment of published data sets. This might include descriptions about a scientific analysis in which the data sets were used, the corrected version of an actual data set or any discovered issues and suggestions concerning the quality of the published data sets. Data providers might then use this information to handle erroneous data and improve existing metadata, their data collection and processing methods. Contributors can use the feedback channel to share their scientific analyses. Data consumers can learn more about data sets based on other people’s experiences, and potentially save time by avoiding the need for interpreting or cleaning data sets. The goals of the study are to capture feedback from data users on published research data sets, link this to actual data sets, and finally support search and discovery of research data using feedback information. This paper reports preliminary results addressing the goals. We provide a summary of current practices on gathering feedback from end-users on research data portals, and discuss their relevance and limitations. Examples from the Earth Science domain on how commentaries from data users might be useful in practice are also included. Then, we present a data model representing key aspects of user feedback. We propose a system architecture to gather and manage feedback from end-users. We describe how the core PROV model may be used to represent the provenance of user feedback information. Technical solutions for linking feedback to existing data portals are also specified. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/modsim2015asd-151208024255-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Various portals have been developed to provide an easy way to discover and access public research data sets from various organizations. Data sets are made available with descriptive metadata based on common (e.g., OGC, CUAHSI, FGDC, INSPIRE, ISO, Dublin Core) or proprietary standards to facilitate better understanding and use of the data sets. Provenance descriptions may be included as part of the metadata and are specified from a data provider’s perspective. These can include, for example, different entities and activities involved in a data creation flow, such as sensing platforms, personnel, and data calculation and transformation processes. Moving beyond the provider-centric descriptions, data provenance may be complemented with forward provenance records supplied by data consumers. The records may be gathered via a user-driven feedback approach. The feedback information from data consumers gives valuable insights into application and assessment of published data sets. This might include descriptions about a scientific analysis in which the data sets were used, the corrected version of an actual data set or any discovered issues and suggestions concerning the quality of the published data sets. Data providers might then use this information to handle erroneous data and improve existing metadata, their data collection and processing methods. Contributors can use the feedback channel to share their scientific analyses. Data consumers can learn more about data sets based on other people’s experiences, and potentially save time by avoiding the need for interpreting or cleaning data sets. The goals of the study are to capture feedback from data users on published research data sets, link this to actual data sets, and finally support search and discovery of research data using feedback information. This paper reports preliminary results addressing the goals. We provide a summary of current practices on gathering feedback from end-users on research data portals, and discuss their relevance and limitations. Examples from the Earth Science domain on how commentaries from data users might be useful in practice are also included. Then, we present a data model representing key aspects of user feedback. We propose a system architecture to gather and manage feedback from end-users. We describe how the core PROV model may be used to represent the provenance of user feedback information. Technical solutions for linking feedback to existing data portals are also specified.

CAPTURING DATA PROVENANCE WITH A USER-DRIVEN FEEDBACK APPROACH from Anusuriya Devaraju

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0 An Open Source Web Service for Registering and Managing Environmental Samples /slideshow/an-open-source-web-service-for-registering-and-managing-environmental-samples/53854776 foss4g2015-151013014554-lva1-app6892
CSIRO-IGSN Implementation]]>
CSIRO-IGSN Implementation]]> Tue, 13 Oct 2015 01:45:54 GMT /slideshow/an-open-source-web-service-for-registering-and-managing-environmental-samples/53854776 anusuriya@slideshare.net(anusuriya) An Open Source Web Service for Registering and Managing Environmental Samples anusuriya CSIRO-IGSN Implementation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/foss4g2015-151013014554-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> CSIRO-IGSN Implementation

An Open Source Web Service for Registering and Managing Environmental Samples from Anusuriya Devaraju

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0 Enabling Quality Control of SensorWeb Observations /slideshow/enabling-quality-control-of-sensorweb-observations/30081174 sensornets2014-asd-140116040059-phpapp02
The rapid development of sensing technologies had led to the creation of large volumes of environmental observation data. Data quality control information informs users how it was gathered, processed, examined. Sensor Web is a web-centric framework that involves observations from various providers. It is essential to capture quality control information within the framework to ensure that observation data are of known and documented quality. In this paper, we present a quality control framework covering different environmental observation data, and show how it is implemented in the TERENO data infrastructure. The infrastructure is modeled after the OGC’s Sensor Web Enablement (SWE) standards.]]>
The rapid development of sensing technologies had led to the creation of large volumes of environmental observation data. Data quality control information informs users how it was gathered, processed, examined. Sensor Web is a web-centric framework that involves observations from various providers. It is essential to capture quality control information within the framework to ensure that observation data are of known and documented quality. In this paper, we present a quality control framework covering different environmental observation data, and show how it is implemented in the TERENO data infrastructure. The infrastructure is modeled after the OGC’s Sensor Web Enablement (SWE) standards.]]> Thu, 16 Jan 2014 04:00:59 GMT /slideshow/enabling-quality-control-of-sensorweb-observations/30081174 anusuriya@slideshare.net(anusuriya) Enabling Quality Control of SensorWeb Observations anusuriya The rapid development of sensing technologies had led to the creation of large volumes of environmental observation data. Data quality control information informs users how it was gathered, processed, examined. Sensor Web is a web-centric framework that involves observations from various providers. It is essential to capture quality control information within the framework to ensure that observation data are of known and documented quality. In this paper, we present a quality control framework covering different environmental observation data, and show how it is implemented in the TERENO data infrastructure. The infrastructure is modeled after the OGC’s Sensor Web Enablement (SWE) standards. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sensornets2014-asd-140116040059-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> The rapid development of sensing technologies had led to the creation of large volumes of environmental observation data. Data quality control information informs users how it was gathered, processed, examined. Sensor Web is a web-centric framework that involves observations from various providers. It is essential to capture quality control information within the framework to ensure that observation data are of known and documented quality. In this paper, we present a quality control framework covering different environmental observation data, and show how it is implemented in the TERENO data infrastructure. The infrastructure is modeled after the OGC’s Sensor Web Enablement (SWE) standards.

Enabling Quality Control of SensorWeb Observations from Anusuriya Devaraju

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http://activitystrea.ms/schema/1.0/posted

0 Representing and Reasoning about Geographic Occurrences in the Sensor Web /slideshow/representing-and-reasoning-about-geographic-occurrences-in-the-sensor-web-12610036/12610036 phddefense2012-asd-v3-0-120419184235-phpapp02
Observations are fed into the Sensor Web through a growing number of environmental sensors, including technical and human observers. While a wealth of observations is now accessible, there is still a gap between low-level observations and the high-level descriptive information they reflect. For example, we may ask what the measurements mean when a weather buoy provides a temperature time series. The challenge is not to gather a vast number of observations, but rather to make sense of them in environmental monitoring and decision making. In order to infer meaningful information about occurrences from observations, a description of how one gets from the former to information about the latter must be expressed. This thesis develops an ontology to formally capture the relationships between geographic occurrences and the properties observed by in situ sensors. Building upon the existing positions on experiential and historical perspectives, stimulus-centric sensing, event-process algebra and thematic roles, the ontology elucidates the key concepts associated with geographic occurrences that are particularly significant from a sensing point of view. A use case for reasoning about blizzards and their temporal parts from real time series supplied by the Environment Canada illustrates the ontological approach. This thesis evaluates its findings on the basis of a comparison with an alternative approach in the Sensor Web, a verification of the use case results using an official event report published by the weather agency and an analytical assessment approached from the system development perspective. The theoretical contribution of the thesis lies in the development of a formal model, which constitutes common building blocks for constructing application ontologies that account for inferences of geographic events from observations. With regards to its practical contribution, the thesis has demonstrated how ontological vocabularies are exploited with reasoning mechanisms to infer information about events, and to formulate symbolic spatio-temporal queries. ]]>
Observations are fed into the Sensor Web through a growing number of environmental sensors, including technical and human observers. While a wealth of observations is now accessible, there is still a gap between low-level observations and the high-level descriptive information they reflect. For example, we may ask what the measurements mean when a weather buoy provides a temperature time series. The challenge is not to gather a vast number of observations, but rather to make sense of them in environmental monitoring and decision making. In order to infer meaningful information about occurrences from observations, a description of how one gets from the former to information about the latter must be expressed. This thesis develops an ontology to formally capture the relationships between geographic occurrences and the properties observed by in situ sensors. Building upon the existing positions on experiential and historical perspectives, stimulus-centric sensing, event-process algebra and thematic roles, the ontology elucidates the key concepts associated with geographic occurrences that are particularly significant from a sensing point of view. A use case for reasoning about blizzards and their temporal parts from real time series supplied by the Environment Canada illustrates the ontological approach. This thesis evaluates its findings on the basis of a comparison with an alternative approach in the Sensor Web, a verification of the use case results using an official event report published by the weather agency and an analytical assessment approached from the system development perspective. The theoretical contribution of the thesis lies in the development of a formal model, which constitutes common building blocks for constructing application ontologies that account for inferences of geographic events from observations. With regards to its practical contribution, the thesis has demonstrated how ontological vocabularies are exploited with reasoning mechanisms to infer information about events, and to formulate symbolic spatio-temporal queries. ]]> Thu, 19 Apr 2012 18:42:34 GMT /slideshow/representing-and-reasoning-about-geographic-occurrences-in-the-sensor-web-12610036/12610036 anusuriya@slideshare.net(anusuriya) Representing and Reasoning about Geographic Occurrences in the Sensor Web anusuriya Observations are fed into the Sensor Web through a growing number of environmental sensors, including technical and human observers. While a wealth of observations is now accessible, there is still a gap between low-level observations and the high-level descriptive information they reflect. For example, we may ask what the measurements mean when a weather buoy provides a temperature time series. The challenge is not to gather a vast number of observations, but rather to make sense of them in environmental monitoring and decision making. In order to infer meaningful information about occurrences from observations, a description of how one gets from the former to information about the latter must be expressed. This thesis develops an ontology to formally capture the relationships between geographic occurrences and the properties observed by in situ sensors. Building upon the existing positions on experiential and historical perspectives, stimulus-centric sensing, event-process algebra and thematic roles, the ontology elucidates the key concepts associated with geographic occurrences that are particularly significant from a sensing point of view. A use case for reasoning about blizzards and their temporal parts from real time series supplied by the Environment Canada illustrates the ontological approach. This thesis evaluates its findings on the basis of a comparison with an alternative approach in the Sensor Web, a verification of the use case results using an official event report published by the weather agency and an analytical assessment approached from the system development perspective. The theoretical contribution of the thesis lies in the development of a formal model, which constitutes common building blocks for constructing application ontologies that account for inferences of geographic events from observations. With regards to its practical contribution, the thesis has demonstrated how ontological vocabularies are exploited with reasoning mechanisms to infer information about events, and to formulate symbolic spatio-temporal queries. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/phddefense2012-asd-v3-0-120419184235-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Observations are fed into the Sensor Web through a growing number of environmental sensors, including technical and human observers. While a wealth of observations is now accessible, there is still a gap between low-level observations and the high-level descriptive information they reflect. For example, we may ask what the measurements mean when a weather buoy provides a temperature time series. The challenge is not to gather a vast number of observations, but rather to make sense of them in environmental monitoring and decision making. In order to infer meaningful information about occurrences from observations, a description of how one gets from the former to information about the latter must be expressed. This thesis develops an ontology to formally capture the relationships between geographic occurrences and the properties observed by in situ sensors. Building upon the existing positions on experiential and historical perspectives, stimulus-centric sensing, event-process algebra and thematic roles, the ontology elucidates the key concepts associated with geographic occurrences that are particularly significant from a sensing point of view. A use case for reasoning about blizzards and their temporal parts from real time series supplied by the Environment Canada illustrates the ontological approach. This thesis evaluates its findings on the basis of a comparison with an alternative approach in the Sensor Web, a verification of the use case results using an official event report published by the weather agency and an analytical assessment approached from the system development perspective. The theoretical contribution of the thesis lies in the development of a formal model, which constitutes common building blocks for constructing application ontologies that account for inferences of geographic events from observations. With regards to its practical contribution, the thesis has demonstrated how ontological vocabularies are exploited with reasoning mechanisms to infer information about events, and to formulate symbolic spatio-temporal queries.

Representing and Reasoning about Geographic Occurrences in the Sensor Web from Anusuriya Devaraju

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0 Semantic interoperability /slideshow/semantic-interoperability-8032086/8032086 week4semanticinteroperability-110519160650-phpapp01
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]]> Thu, 19 May 2011 16:06:47 GMT /slideshow/semantic-interoperability-8032086/8032086 anusuriya@slideshare.net(anusuriya) Semantic interoperability anusuriya <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/week4semanticinteroperability-110519160650-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Semantic interoperability from Anusuriya Devaraju

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0 Semantic Sensor Web /slideshow/semantic-sensor-web/6518260 semanticsensorweb-110111094503-phpapp01
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]]> Tue, 11 Jan 2011 09:45:01 GMT /slideshow/semantic-sensor-web/6518260 anusuriya@slideshare.net(anusuriya) Semantic Sensor Web anusuriya <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/semanticsensorweb-110111094503-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Semantic Sensor Web from Anusuriya Devaraju

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0 Linked Data /slideshow/linked-data-5707285/5707285 week8linkeddata-101108151521-phpapp01
Lecture on Linked Data and Linked Geo-Data]]>
Lecture on Linked Data and Linked Geo-Data]]> Mon, 08 Nov 2010 15:15:05 GMT /slideshow/linked-data-5707285/5707285 anusuriya@slideshare.net(anusuriya) Linked Data anusuriya Lecture on Linked Data and Linked Geo-Data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/week8linkeddata-101108151521-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Lecture on Linked Data and Linked Geo-Data

Linked Data from Anusuriya Devaraju

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0 Combining Process and Sensor Ontologies to Support Geo-Sensor Data Retrieval /slideshow/combining-process-and-sensor-ontologies-to-support-geosensor-data-retrieval/5241435 giscience2010v3-100920093540-phpapp01
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]]> Mon, 20 Sep 2010 09:35:38 GMT /slideshow/combining-process-and-sensor-ontologies-to-support-geosensor-data-retrieval/5241435 anusuriya@slideshare.net(anusuriya) Combining Process and Sensor Ontologies to Support Geo-Sensor Data Retrieval anusuriya <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/giscience2010v3-100920093540-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Combining Process and Sensor Ontologies to Support Geo-Sensor Data Retrieval from Anusuriya Devaraju

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0 Fois2010 final /slideshow/fois2010-final/5119800 fois2010final-100903011808-phpapp01
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]]> Fri, 03 Sep 2010 01:17:56 GMT /slideshow/fois2010-final/5119800 anusuriya@slideshare.net(anusuriya) Fois2010 final anusuriya <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fois2010final-100903011808-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Fois2010 final from Anusuriya Devaraju

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http://activitystrea.ms/schema/1.0/posted

0 https://cdn.slidesharecdn.com/profile-photo-anusuriya-48x48.jpg?cb=1658289590 anusuriya.com/ https://cdn.slidesharecdn.com/ss_thumbnails/fairdatahmcasd-220606080807-060de3bc-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/fair-assessment-or-improvement/251926938 FAIR – Assessment or I... https://cdn.slidesharecdn.com/ss_thumbnails/resbaz2021asd-211123224050-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/simple-steps-to-effective-research-data-sharing/250712104 Simple Steps to Effect... https://cdn.slidesharecdn.com/ss_thumbnails/fairsfairfuji30092020-200930130928-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/fuji-an-automated-assessment-tool-for-improving-the-fairness-of-research-data/238684071 F-UJI : An Automated A...