scholarly journals Digital Object Cloud for linking natural science collections information; The case of DiSSCo 

2018 ◽  
Vol 2 ◽  
pp. e25474
Author(s):  
Dimitrios Koureas ◽  
Wouter Addink ◽  
Alex Hardisty
Keyword(s):  
Natural Science ◽  
Large Scale ◽  
Digital Object ◽  
Science Museums ◽  
Large Scale Data ◽  
Governance Model ◽  
Different Types ◽  
Types Of Information ◽  
Scientific Collections ◽  
Fragmented Data

DiSSCo(The Distributed System of Scientific Collections) is a Research Infrastructure (RI) aiming at providing unified physical (transnational), remote (loans) and virtual (digital) access to the approximately 1.5 billion biological and geological specimens in collections across Europe. DiSSCo represents the largest ever formal agreement between natural science museums (114 organisations across 21 European countries). With political and financial support across 14 European governments and a robust governance model DiSSCo will deliver, by 2025, a series of innovative end-user discovery, access, interpretation and analysis services for natural science collections data. As part of DiSSCo's developing data model, we evaluate the application of Digital Objects (DOs), which can act as the centrepiece of its architecture. DOs have bit-sequences representing some content, are identified by globally unique persistent identifiers (PIDs) and are associated with different types of metadata. The PIDs can be used to refer to different types of information such as locations, checksums, types and other metadata to enable immediate operations. In the world of natural science collections, currently fragmented data classes (inter alia genes, traits, occurrences) that have derived from the study of physical specimens, can be re-united as parts in a virtual container (i.e., as components of a Digital Object). These typed DOs, when combined with software agents that scan the data offered by repositories, can act as complete digital surrogates of the physical specimens. In this paper we: investigate the architectural and technological applicability of DOs for large scale data RIs for bio- and geo-diversity, identify benefits and challenges of a DO approach for the DiSSCo RI and describe key specifications (incl. metadata profiles) for a specimen-based new DO type.

scholarly journals DiSSCo as a New Regional Model for Scientific Collections in Europe

2019 ◽  
Vol 3 ◽  
Author(s):  
Wouter Addink ◽  
Dimitrios Koureas ◽  
Ana Rubio
Keyword(s):  
Natural History ◽  
Natural Science ◽  
Large Scale ◽  
Advisory Board ◽  
Political Support ◽  
Research Infrastructure ◽  
Morphological Data ◽  
Natural History Museums ◽  
History Museums ◽  
Scientific Collections

European Natural Science Collections (NSC) are part of the global natural and cultural capital and represent 80% of the world bio-and geo-diversity. Data derived from these collections underpin thousands of scholarly publications and official reports (used to support legislative and regulatory processes relating to health, food, security, sustainability and environmental change) and let to inventions and products that today play an important role in our bio-economy. In the last decades, the research practice in natural sciences changed dramatically. Advances in digital, genomic and information technologies enable natural science collections to provide new insights but also ask for changing the current operational and business models of individual collections held at local natural history museums and universities. A new business model that provides unified access to collection objects and all scientific data derived from them. Although aggregating infrastructures like the Global Biodiversity Information Facility, GenBank and Catalogue of Life now successfully aggregate data on specific data classes, the landscape remains fragmented with limited capacity to bring together this information in a systematic and robust manner and with scattered access to the physical objects. The Distributed System of Scientific Collections (DiSSCo) represents a pan-European initiative, and the largest ever agreement of natural science museums, to jointly address the fragmentation of European collections. DiSSCo is unifying European natural science collections into a coherent new research infrastructure, able to provide bio- and geo-diversity data at the scale, form and precision required by a multi-disciplinary user base in science. DiSSCo is harmonising digitisation, curation and publication processes and workflows across the scientific collections in Europe and enables linking of occurrence, genomic, chemical and morphological data classes as well as publications and experts to the physical object. In this paper we will present the socio-cultural and governance aspects of this research infrastructure. DiSSCo is receiving political support from 11 countries in Europe and will gradually change its funding model from institutional to national funding, with temporary funding from the EC to support the preparation and development. Solutions to achieve large scale digitisation are currently designed in the EC funded ICEDIG project to underpin the future large scale digitisation carried out by the countries. Unified virtual (digitisation on demand) and transnational physical access to the collections is over the next four years being developed in the EC funded SYNTHESYS+ project. The governance of DiSSCo is designed to gradually change from a steering committee composed of a few large natural history museums contributing in cash to initiate the development into a legal entity in which national consortia are represented, with a central coordination office for daily management. Each country individually decides how its entities (scientific collection facilities, research councils, governmental bodies) are organised in their national consortium. A stakeholder and user forum, Scientific Advisory Board and International Advisory Board will ensure that DiSSCo will be functional in enabling science across disciplines and within the international landscape of infrastructures. Training and short scientific missions are being developed in the MOBILISE COST Action to build capacity in FAIR data production, publication and usage of scientific collection-derived data in Europe and to initiate the socio-cultural changes needed in the collection-holding institutes. A Helpdesk is being constructed in the SYNTHESYS+ and DiSSCo Prepare projects to further facilitate the use and scientific use cases have been collected in ICEDIG to develop and facilitate e-services tailored to scientific needs.

scholarly journals Technical capacities of digitisation centres within ICEDIG participating institutions

2020 ◽  
Vol 6 ◽  
Author(s):  
Naomi Cocks ◽  
Laurence Livermore ◽  
Vincent Smith ◽  
Matt Woodburn
Keyword(s):  
Distributed System ◽  
Natural Science ◽  
Large Scale ◽  
Practical Reasons ◽  
Institutional Collaboration ◽  
Level Of Aggregation ◽  
Scientific Collections ◽  
Future Work

DiSSCo, the Distributed System of Scientific Collections, is seeking to centralise certain infrastructure and activities relating to the digitisation of natural science collections. Deciding what activities to distribute, what to centralise, and what geographic level of aggregation (e.g. regional, national or pan European) is most appropriate for each task, was one of the challenges set out within the EC-funded ICEDIG project. In this paper we present the results of a survey of several European collections to establish current digitisation capacity, strengths and skills associated with existing digitisation infrastructure. Our results indicate that most of the institutions surveyed are engaged in large-scale digitisation of collections and that this is usually being undertaken by dedicated teams of digitisers within each institution. Some cross institutional collaboration is happening, but this is still the exception for a variety of funder and practical reasons. These results inform future work that establishes a set of principles to determine how digitisation infrastructure might be most efficiently organised across European organisations in order to maximise progress on the digitisation of the estimated 1.5 billion specimens held within European natural science collections.

scholarly journals Reconstructing SARS-CoV-2 response signaling and regulatory networks

2020 ◽  
Author(s):  
Jun Ding ◽  
Jose Lugo-Martinez ◽  
Ye Yuan ◽  
Darrell N. Kotton ◽  
Ziv Bar-Joseph
Keyword(s):  
Data Integration ◽  
Computational Methods ◽  
Regulatory Networks ◽  
Large Scale ◽  
Human Cells ◽  
Host Cells ◽  
Interaction Data ◽  
Large Scale Data ◽  
Different Types ◽  
Scale Data

AbstractSeveral molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of sequence, functional and interaction data to reconstruct networks and pathways activated by the virus in host cells. We identify the key proteins in these networks and further intersect them with genes differentially expressed at conditions that are known to impact viral activity. Several of the top ranked genes do not directly interact with virus proteins though some were shown to impact other coronaviruses highlighting the importance of large-scale data integration for understanding virus and host activity.Software and interactive visualization: https://github.com/phoenixding/sdremsc

scholarly journals FAIR Digital Objects and Natural Science Collection Data

2020 ◽  
Vol 4 ◽  
Author(s):  
Sharif Islam
Keyword(s):  
Natural Science ◽  
Large Scale ◽  
Building Blocks ◽  
Fragmented Landscape ◽  
Data Infrastructure ◽  
Collection Data ◽  
Digital Objects ◽  
New Research ◽  
Scientific Collections ◽  
Access Policies

The Distributed System of Scientific Collections (DiSSCo) is a new Research Infrastructure that is working towards the unification of all European natural science collections under common curation, access policies, and practices (Addink et al. 2019). The physical specimens in the collections and the vast amount of data derived from and linked to these specimens are important building blocks for this unification process. Primarily coming from large scale digitization projects (Blagoderov et al. 2012) along with new types of data collection, curation, and sharing methods (e.g. Kays et al. 2020), these specimens hold data that are critical for different scientific endeavours (Cook et al. 2020, Hedrick et al. 2020). Therefore it is important that the data infrastructure and the relevant services can provide a long-term sustainable and reliable access to these data. To that end, DiSSCo is working towards transforming a fragmented landscape of the natural science collections into an integrated data infrastructure that can ensure that these data can be easily Findable, more Accessible, Interoperable and Reusable – in other words, comply with the FAIR Guiding Principles (Wilkinson et al. 2016). A key decision for the design of this FAIR data infrastructure was to adopt FAIR Digital Objects (Wittenburg and Strawn 2019) that will enable the creation of Digital Specimen—a machine-actionable digital twin of the physical specimen (Lannom et al. 2020). This FAIR Digital Object by design, ensures FAIRness of the data (De Smedt et al. 2020) and thus will allow DiSSCo to provide services that are essential for natural science collection-based research. This talk summarises the motivation behind this adoption by showing how design decisions and best practices were influenced by the FAIR data principles, global discussions around FAIR Digital Objects and outputs from the Research Data Alliance (RDA) interest and working groups.

scholarly journals Implementasi Algoritma Apriori Untuk Menentukan Pola Data Penyakit Pada Anak Usia Dini (Studi Kasus: RS. Estomihi)

2020 ◽  
Vol 7 (2) ◽  
pp. 308
Author(s):  
Citra Verawati Purba ◽  
Efori Buulolo
Keyword(s):  
Early Childhood ◽  
Large Scale ◽  
Apriori Algorithm ◽  
Medical Record Data ◽  
Childhood Disease ◽  
Risk Of Death ◽  
Large Scale Data ◽  
Record Data ◽  
Different Types ◽  
Scale Data

Hospital is a place to treat patients with different types of diseases, and hospitals are also one of the health services that are medical, healing and recovery for patients. Many people who want to seek treatment at the hospital both parents and including  early  childhood. Based on this, researchers are interested in looking for patterns from large-scale data and associating data with one another using the Apriori algorithm. Thus, the diseases suffered by early childhood can be classified based on the medical record data, so that the  pattern of early childhood disease can be known. The information produced can be used by the Hospital and the doctors on duty at the hospital to take the necessary actions in order to prevent the spread of a disease and also reduce the risk of death of patients suffering from disease

scholarly journals Spatiotemporal Aspects of Big Data

2018 ◽  
Vol 23 (2) ◽  
pp. 90-100 ◽  
Author(s):  
Saadia Karim ◽  
Tariq Rahim Soomro ◽  
S. M. Aqil Burney
Keyword(s):  
Big Data ◽  
Real Time ◽  
Large Scale ◽  
Qualitative Approach ◽  
Spatiotemporal Data ◽  
New Era ◽  
Large Scale Data ◽  
Different Types ◽  
Architectural Characteristics ◽  
Scale Data

Abstract Data has evolved into a large-scale data as big data in the recent era. The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data. The big data with spatiotemporal aspects helps achieve more efficient results and, therefore, many different types of frameworks have been introduced in cooperate world. In the present research, a qualitative approach is used to present the framework classification in two categories: architecture and features. Frameworks have been compared on the basis of architectural characteristics and feature attributes as well. These two categories project a significant effect on the execution of spatiotemporal data in big data. Frameworks are able to solve the real-time problems in less time of cycle. This study presents spatiotemporal aspects in big data with reference to several dissimilar environments and frameworks.

Effect of sleep on memory for binding different types of visual information

2020 ◽  
Author(s):  
John J Shaw ◽  
Zhisen Urgolites ◽  
Padraic Monaghan
Keyword(s):  
Visual Information ◽  
Storage Capacity ◽  
Declarative Memory ◽  
Recognition Task ◽  
Forced Choice ◽  
Long Term Memory ◽  
Term Memory ◽  
Different Types ◽  
Types Of Information

Visual long-term memory has a large and detailed storage capacity for individual scenes, objects, and actions. However, memory for combinations of actions and scenes is poorer, suggesting difficulty in binding this information together. Sleep can enhance declarative memory of information, but whether sleep can also boost memory for binding information and whether the effect is general across different types of information is not yet known. Experiments 1 to 3 tested effects of sleep on binding actions and scenes, and Experiments 4 and 5 tested binding of objects and scenes. Participants viewed composites and were tested 12-hours later after a delay consisting of sleep (9pm-9am) or wake (9am-9pm), on an alternative forced choice recognition task. For action-scene composites, memory was relatively poor with no significant effect of sleep. For object-scene composites sleep did improve memory. Sleep can promote binding in memory, depending on the type of information to be combined.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data
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