Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
57,536
result(s) for
"data preservation"
Sort by:
Collecting experiments : making Big Data biology
Databases have revolutionized nearly every aspect of our lives. Information of all sorts is being collected on a massive scale, from Google to Facebook and well beyond. But as the amount of information in databases explodes, we are forced to reassess our ideas about what knowledge is, how it is produced, to whom it belongs, and who can be credited for producing it. Every scientist working today draws on databases to produce scientific knowledge. Databases have become more common than microscopes, voltmeters, and test tubes, and the increasing amount of data has led to major changes in research practices and profound reflections on the proper professional roles of data producers, collectors, curators, and analysts. Collecting Experiments traces the development and use of data collections, especially in the experimental life sciences, from the early twentieth century to the present. It shows that the current revolution is best understood as the coming together of two older ways of knowing--collecting and experimenting, the museum and the laboratory. Ultimately, Bruno J. Strasser argues that by serving as knowledge repositories, as well as indispensable tools for producing new knowledge, these databases function as digital museums for the twenty-first century.
Book
3D Data Creation to Curation
3D Data Creation to Curation: Community Standards for 3D Data Preservation collects the efforts of the Community Standards for 3D Data Preservation (CS3DP) initiative--a large practicing community of librarians, researchers, engineers, and designers--to move toward establishment of shared guidelines, practices, and standards. Using a collaborative approach for standards development that promotes individual investment and broad adoption, this group has produced a work that captures the shared preservation needs of the whole community.
eBook
Large-scale Distributed Systems and Energy Efficiency
With concerns about global energy consumption at an all-time high, improving computer networks energy efficiency is becoming an increasingly important topic. Large-Scale Distributed Systems and Energy Efficiency: A Holistic View addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks. After an introductory overview of the energy demands of current Information and Communications Technology (ICT), individual chapters offer in-depth analyses of such topics as cloud computing, green networking (both wired and wireless), mobile computing, power modeling, the rise of green data centers and high-performance computing, resource allocation, and energy efficiency in peer-to-peer (P2P) computing networks. -Discusses measurement and modeling of the energy consumption method -Includes methods for energy consumption reduction in diverse computing environments -Features a variety of case studies and examples of energy reduction and assessment Timely and important, Large-Scale Distributed Systems and Energy Efficiency is an invaluable resource for ways of increasing the energy efficiency of computing systems and networks while simultaneously reducing the carbon footprint.
eBook
Ex-vivo perfusion of donor hearts for human heart transplantation (PROCEED II): a prospective, open-label, multicentre, randomised non-inferiority trial
The Organ Care System is the only clinical platform for ex-vivo perfusion of human donor hearts. The system preserves the donor heart in a warm beating state during transport from the donor hospital to the recipient hospital. We aimed to assess the clinical outcomes of the Organ Care System compared with standard cold storage of human donor hearts for transplantation.
We did this prospective, open-label, multicentre, randomised non-inferiority trial at ten heart-transplant centres in the USA and Europe. Eligible heart-transplant candidates (aged >18 years) were randomly assigned (1:1) to receive donor hearts preserved with either the Organ Care System or standard cold storage. Participants, investigators, and medical staff were not masked to group assignment. The primary endpoint was 30 day patient and graft survival, with a 10% non-inferiority margin. We did analyses in the intention-to-treat, as-treated, and per-protocol populations. This trial is registered with ClinicalTrials.gov, number NCT00855712.
Between June 29, 2010, and Sept 16, 2013, we randomly assigned 130 patients to the Organ Care System group (n=67) or the standard cold storage group (n=63). 30 day patient and graft survival rates were 94% (n=63) in the Organ Care System group and 97% (n=61) in the standard cold storage group (difference 2·8%, one-sided 95% upper confidence bound 8·8; p=0·45). Eight (13%) patients in the Organ Care System group and nine (14%) patients in the standard cold storage group had cardiac-related serious adverse events.
Heart transplantation using donor hearts adequately preserved with the Organ Care System or with standard cold storage yield similar short-term clinical outcomes. The metabolic assessment capability of the Organ Care System needs further study.
TransMedics.
Journal Article
A Decentralized Privacy-Preserving Healthcare Blockchain for IoT
Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology—remote patient monitoring—is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient’s life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network.
Journal Article
Scientific Data Evaluation Index System for Scientific Data Preservation
[Purpose/Significance] Scientific data preservation is the premise of scientific data sharing and utilization, also is the basis of high-quality and efficient service of scientific data. [Method/Process] On the basis of investigating the progress of theoretical research and practice at home and abroad, this paper selects a number of indicators according to the factors that affect the preservation of scientific data, and then improves the index system by the method of expert investigation. \"Scientific data evaluation index system for scientific data preservation\" is constructed. The index system includes eight first-level indicators, such as \"legal factors\", \"reuse value\", and \"data reliability\". There are 32 secondary indicators, such as \"legal requirements\", \"special academic value\", and \"data accuracy\". Finally, the analytic hierarchy process is used to calculate the index weight. [Results/Conclusions] This paper puts forward three suggestions for scientific data management in China, such as actively promoting the legislation of scientific data management, improving the level of scientific research, standardizing academic ethics and improving the quality of scientific data, developing data storage and security technology, and promoting scientific data sharing and long-term preservation.
Journal Article
Blockchain-Based Data Preservation System for Medical Data
Medical care has become an indispensable part of people’s lives, with a dramatic increase in the volume of medical data (e.g., diagnosis certificates and medical records). Medical data, however, is easily stolen, tampered with, or even completely deleted. If the above occurs, medical data cannot be recorded or retrieved in a reliable manner, resulting in delay treatment progress, even endanger the patient’s life. In this paper, we propose a novel blockchain-based data preservation system (DPS) for medical data. To provide a reliable storage solution to ensure the primitiveness and verifiability of stored data while preserving privacy for users, we leverage the blockchain framework. With the proposed DPS, users can preserve important data in perpetuity, and the originality of the data can be verified if tampering is suspected. In addition, we use prudent data storage strategies and a variety of cryptographic algorithms to guarantee user privacy; e.g., an adversary is unable to read the plain text even if the data are stolen. We implement a prototype of the DPS based on the real world blockchain-based platform Ethereum. Performance evaluation results demonstrate the effectiveness and efficiency of the proposed system.
Journal Article
The Materials Data Facility: Data Services to Advance Materials Science Research
With increasingly strict data management requirements from funding agencies and institutions, expanding focus on the challenges of research replicability, and growing data sizes and heterogeneity, new data needs are emerging in the materials community. The materials data facility (MDF) operates two cloud-hosted services, data publication and data discovery, with features to promote open data sharing, self-service data publication and curation, and encourage data reuse, layered with powerful data discovery tools. The data publication service simplifies the process of copying data to a secure storage location, assigning data a citable persistent identifier, and recording custom (e.g., material, technique, or instrument specific) and automatically-extracted metadata in a registry while the data discovery service will provide advanced search capabilities (e.g., faceting, free text range querying, and full text search) against the registered data and metadata. The MDF services empower individual researchers, research projects, and institutions to (I) publish research datasets, regardless of size, from local storage, institutional data stores, or cloud storage, without involvement of third-party publishers; (II) build, share, and enforce extensible domain-specific custom metadata schemas; (III) interact with published data and metadata via representational state transfer (REST) application program interfaces (APIs) to facilitate automation, analysis, and feedback; and (IV) access a data discovery model that allows researchers to search, interrogate, and eventually build on existing published data. We describe MDF’s design, current status, and future plans.
Journal Article
Research Data Management Practices in University libraries: A study
The paper has studied the research data management (RDM) services implemented by different university libraries for managing, organizing, curating and preserving research data generated at their universities’ departments and laboratories, for data reuse and sharing. It has surveyed the central university libraries and the best 20 university libraries of the world to highlight how RDM is extended to the researchers. Further, it has suggested a model for the university libraries in the country to follow for actually deploying RDM services.
Journal Article
Haematology dimension reduction, a large scale application to regular care haematology data
Background
The routine diagnostic process increasingly entails the processing of high-volume and high-dimensional data that cannot be directly visualised. This processing may provide scaling issues that limit the implementation of these types of data into research as well as integrated diagnostics in routine care. Here, we investigate whether we can use existing dimension reduction techniques to provide visualisations and analyses for a complete bloodcount (CBC) while maintaining representativeness of the original data. We considered over 3 million CBC measurements encompassing over 70 parameters of cell frequency, size and complexity from the UMC Utrecht UPOD database. We evaluated PCA as an example of a linear dimension reduction techniques and UMAP, TriMap and PaCMAP as non-linear dimension reduction techniques. We assessed their technical performance using quality metrics for dimension reduction as well as biological representation by evaluating preservation of diurnal, age and sex patterns, cluster preservation and the identification of leukemia patients.
Results
We found that, for clinical hematology data, PCA performs systematically better than UMAP, TriMap and PaCMAP in representing the underlying data. Biological relevance was retained for periodicity in the data. However, we also observed a decrease in predictive performance of the reduced data for both age and sex, as well as an overestimation of clusters within the reduced data. Finally, we were able to identify the diverging patterns for leukemia patients after use of dimensionality reduction methods.
Conclusions
We conclude that for hematology data, the use of unsupervised dimension reduction techniques should be limited to data visualization applications, as implementing them in diagnostic pipelines may lead to decreased quality of integrated diagnostics in routine care.
Journal Article