scholarly journals Identifiers.org: Compact Identifier services in the cloud

2020 ◽  
Author(s):  
Manuel Bernal-Llinares ◽  
Javier Ferrer-Gómez ◽  
Nick Juty ◽  
Carole Goble ◽  
Sarala M Wimalaratne ◽  
...  
Keyword(s):  
Life Science ◽  
Life Sciences ◽  
High Availability ◽  
Third Party ◽  
Low Latency ◽  
Cloud Environment ◽  
Science Data ◽  
High Quality ◽  
Front End ◽  
Readable Text

Abstract Motivation Since its launch in 2010, Identifiers.org has become an important tool for the annotation and cross-referencing of Life Science data. In 2016, we established the Compact Identifier (CID) scheme (prefix: accession) to generate globally unique identifiers for data resources using their locally assigned accession identifiers. Since then, we have developed and improved services to support the growing need to create, reference and resolve CIDs, in systems ranging from human readable text to cloud-based e-infrastructures, by providing high availability and low-latency cloud-based services, backed by a high-quality, manually curated resource. Results We describe a set of services that can be used to construct and resolve CIDs in Life Sciences and beyond. We have developed a new front end for accessing the Identifiers.org registry data and APIs to simplify integration of Identifiers.org CID services with third-party applications. We have also deployed the new Identifiers.org infrastructure in a commercial cloud environment, bringing our services closer to the data. Availabilityand implementation https://identifiers.org.

scholarly journals The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences

2019 ◽  
Author(s):  
Rachel Drysdale ◽  
Charles E. Cook ◽  
Robert Petryszak ◽  
Vivienne Baillie-Gerritsen ◽  
Mary Barlow ◽  
...  
Keyword(s):  
Life Science ◽  
Life Sciences ◽  
Health Sector ◽  
Biological Data ◽  
Supplementary Information ◽  
Science Data ◽  
Data Infrastructure ◽  
Data Resource ◽  
Core Data ◽  
The Core

AbstractMotivationLife science research in academia, industry, agriculture, and the health sector depends critically on free and open data resources. ELIXIR (www.elixir-europe.org), the European Research Infrastructure for life sciences data, has identified a set of Core Data Resources within Europe that are of most fundamental importance for the long-term preservation of biological data. We explore characteristics of their usage, impact and assured funding horizon to assess their value and importance as an infrastructure, to understand sustainability of the infrastructure, and to demonstrate a model for assessing Core Data Resources worldwide.ResultsThe nineteen resources currently designated ELIXIR Core Data Resources form a data infrastructure in Europe which is a subset of the worldwide open life science data infrastructure. We show that, from 2014 to 2018, data managed by the Core Data Resources more than tripled while staff numbers increased by less than a tenth. Additionally, support for the Core Data Resources is precarious: together they have assured funding for less than a third of current staff after four years.Our findings demonstrate the importance of the ELIXIR Core Data Resources as repositories for research data and knowledge, while also demonstrating the uncertain nature of the funding environment for this infrastructure. ELIXIR is working towards longer-term support for the Core Data Resources and, through the Global Biodata Coalition, aims to ensure support for the worldwide life science data resource infrastructure of which the ELIXIR Core Data Resources are a [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals YummyData: providing high-quality open life science data

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Yasunori Yamamoto ◽  
Atsuko Yamaguchi ◽  
Andrea Splendiani
Keyword(s):  
Life Science ◽  
Science Data ◽  
High Quality

scholarly journals ISA API: An open platform for interoperable life science experimental metadata

2020 ◽  
Author(s):  
David Johnson ◽  
Keeva Cochrane ◽  
Robert P. Davey ◽  
Anthony Etuk ◽  
Alejandra Gonzalez-Beltran ◽  
...  
Keyword(s):  
Data Model ◽  
Life Science ◽  
Life Sciences ◽  
Common Data Model ◽  
Science Data ◽  
Software Suite ◽  
Early Adopters ◽  
Open Platform ◽  
Data Formats ◽  
Common Interface

AbstractBackgroundThe Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open-source community specifications and software tools for enabling discovery, exchange and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab – a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, a JSON serialization ISA-JSON was developed.ResultsIn this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python class objects. We describe the ISA API feature set, early adopters and its growing user community.ConclusionsThe ISA API provides users with rich programmatic metadata handling functionality to support automation, a common interface and an interoperable medium between the two ISA formats, as well as with other life science data formats required for depositing data in public databases.

scholarly journals Bridging Data Management and Knowledge Discovery in the Life Sciences

2008 ◽  
Vol 2 (1) ◽  
pp. 28-36 ◽  
Author(s):  
Karl Kugler ◽  
Maria Mercedes Tejada ◽  
Christian Baumgartner ◽  
Bernhard Tilg ◽  
Armin Graber ◽  
...  
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Data Warehouse ◽  
Life Science ◽  
Life Sciences ◽  
Relevant Information ◽  
Biomedical Data ◽  
Science Data ◽  
New Knowledge ◽  
Discovery Algorithms

In this work we present an application for integrating and analyzing life science data using a biomedical data warehouse system and tools developed in-house enabling knowledge discovery tasks. Knowledge discovery is known as a process where different steps have to be coupled in order to solve a specified question. In order to create such a combination of steps, a data miner using our in-house developed knowledge discovery tool KD3 is able to assemble functional objects to a data mining workflow. The generated workflows can easily be used for ulterior purposes by only adding new data and parameterizing the functional objects in the process. Workflows guide the performance of data integration and aggregation tasks, which were defined and implemented using a public available open source tool. To prove the concept of our application, intelligent query models were designed and tested for the identification of genotype-phenotype correlations in Marfan Syndrome. It could be shown that by using our application, a data miner can easily develop new knowledge discovery algorithms that may later be used to retrieve medical relevant information by clinical researchers.

scholarly journals The Dutch Techcentre for Life Sciences: Enabling data-intensive life science research in the Netherlands

F1000Research ◽  
2016 ◽  
Vol 4 ◽  
pp. 33 ◽  
Author(s):  
Lars Eijssen ◽  
Chris T. Evelo ◽  
Ruben Kok ◽  
Barend Mons ◽  
Rob Hooft ◽  
...  
Keyword(s):  
The Netherlands ◽  
Life Science ◽  
Life Sciences ◽  
Academic Research ◽  
Science Research ◽  
Science Data ◽  
Data Intensive ◽  
For Profit ◽  
Data Stewardship ◽  
Research Organisations

We describe the Data programme of the Dutch Techcentre for Life Sciences (DTL, www.dtls.nl). DTL is a new national organisation in scientific research that facilitates life scientists with technologies and technological expertise in an era where new projects often are data-intensive, multi-disciplinary, and multi-site. It is run as a lean not-for-profit organisation with research organisations (both academic and industrial) as paying members. The small staff of the organisation undertakes a variety of tasks that are necessary to perform or support modern academic research, but that are not easily undertaken in a purely academic setting. DTL Data takes care of such tasks related to data stewardship, facilitating exchange of knowledge and expertise, and brokering access to e-infrastructure. DTL also represents the Netherlands in ELIXIR, the European infrastructure for life science data. The organisation is still being fine-tuned and this will continue over time, as it is crucial for this kind of organisation to adapt to a constantly changing environment. However, already being underway for several years, our experiences can benefit researchers in other fields or other countries setting up similar initiatives.

scholarly journals ISA API: An open platform for interoperable life science experimental metadata

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
David Johnson ◽  
Dominique Batista ◽  
Keeva Cochrane ◽  
Robert P Davey ◽  
Anthony Etuk ◽  
...  
Keyword(s):  
Data Model ◽  
Life Science ◽  
Life Sciences ◽  
Common Data Model ◽  
Science Data ◽  
Software Suite ◽  
Early Adopters ◽  
Open Platform ◽  
Data Formats ◽  
Common Interface

Abstract Background The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open source community specifications and software tools for enabling discovery, exchange, and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab—a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, the JSON serialization ISA-JSON was developed. Results In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters, and its growing user community. Conclusions The ISA API provides users with rich programmatic metadata-handling functionality to support automation, a common interface, and an interoperable medium between the 2 ISA formats, as well as with other life science data formats required for depositing data in public databases.

scholarly journals Future opportunities and trends for e-infrastructures and life sciences: going beyond the grid to enable life science data analysis

2015 ◽  
Vol 6 ◽  
Author(s):  
Afonso M. S. Duarte ◽  
Fotis E. Psomopoulos ◽  
Christophe Blanchet ◽  
Alexandre M. J. J. Bonvin ◽  
Manuel Corpas ◽  
...  
Keyword(s):  
Data Analysis ◽  
Life Science ◽  
Life Sciences ◽  
Science Data

scholarly journals 344 The Ag Guide serves as a primary standard for animal scientists and AAALAC accreditation of Ag research programs

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 71-72
Author(s):  
Janeen Salak-Johnson
Keyword(s):  
Animal Welfare ◽  
Primary Standard ◽  
Animal Research ◽  
Third Party ◽  
Best Interests ◽  
Professional Judgment ◽  
High Quality ◽  
Accreditation Program ◽  
Agricultural Animal ◽  
Animal Care

Abstract Institutions that engage in animal research and production must ensure that high standards of animal care and use meet expectations of society while being ethical stewards of the animals they use in research. In order to achieve engagement in best practices, the Ag Guide is the most appropriate standard for assessing agricultural animals used in research and teaching. The Ag Guide minimizes the potential to overuse performance standards while enhancing the ability to appropriately address specific performance-derived exceptions to situations for which they have been validated. The primary objectives of the standards established in the Ag Guide are well-aligned with the goals of the AAALAC International accreditation program. The Ag Guide provides scientifically-sound, performance-based approaches to animal care and housing, which meet the expectations of AAALAC’s accreditation program. AAALAC provides a third-party peer review of all facets of the animal care and use program that serves as an effective mechanism to ensure institutions meet the standards of the Ag Guide. The process is designed to help identify the strengths and weaknesses of the program to ensure high-quality scientific outcomes and a high level of animal welfare. AAALAC accreditation program for agricultural animal research program is built on the cornerstone of the Ag Guide standards and connects science and responsible animal care. AAALAC accreditation promotes a comprehensive, institutionally supported program with a commitment to continuous improvement, humane and ethical animal care resulting in high-quality animal welfare, and scientific validity. AAALAC takes the position that, in accredited programs, the housing and care for agricultural animals should meet the standards that prevail on a high-quality, well-managed farm and the Ag Guide serves as this foundation. Therefore, the use of the Ag Guide for agricultural animal programs ensures a review that is based on science, professional judgment, and the best interests of the animal.

scholarly journals A Design of an Integrated Cloud-based Intrusion Detection System with Third Party Cloud Service

2021 ◽  
Vol 11 (1) ◽  
pp. 365-379
Author(s):  
Wisam Elmasry ◽  
Akhan Akbulut ◽  
Abdul Halim Zaim
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Cloud Service ◽  
Third Party ◽  
Traffic Flows ◽  
Cloud Environment ◽  
System Service ◽  
Reporting Service ◽  
Bagging Ensemble

Abstract Although cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.

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