scholarly journals SIS 2017. Statistics and Data Science: new challenges, new generations

2017 ◽  
Keyword(s):  
Data Science ◽  
Open Data ◽  
High Dimensional ◽  
Pattern Selection ◽  
Complex Data ◽  
Data Modelling ◽  
Testing Hypotheses ◽  
New Challenges ◽  
Validation Sampling

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data.

scholarly journals Loch Prospector: Metadata Visualization for Lakes of Open Data

2020 ◽  
Author(s):  
Neha Makhija ◽  
Mansi Jain ◽  
Nikolaos Tziavelis ◽  
Laura Di Rocco ◽  
Sara Di Bartolomeo ◽  
...  
Keyword(s):  
Data Management ◽  
Data Science ◽  
Open Data ◽  
Data Availability ◽  
Great Promise ◽  
Management Techniques ◽  
New Challenges ◽  
Integration Data ◽  
New Algorithms ◽  
Structural Aspects

Data lakes are an emerging storage paradigm that promotes data availability over integration. A prime example are repositories of Open Data which show great promise for transparent data science. Due to the lack of proper integration, Data Lakes may not have a common consistent schema and traditional data management techniques fall short with these repositories. Much recent research has tried to address the new challenges associated with these data lakes. Researchers in this area are mainly interested in the structural properties of the data for developing new algorithms, yet typical Open Data portals offer limited functionality in that respect and instead focus on data semantics.We propose Loch Prospector, a visualization to assist data management researchers in exploring and understanding the most crucial structural aspects of Open Data — in particular, metadata attributes — and the associated task abstraction for their work. Our visualization enables researchers to navigate the contents of data lakes effectively and easily accomplish what were previously laborious tasks. A copy of this paper with all supplemental material is available at osf.io/zkxv9

Supporting open data: the key role of data managers

2021 ◽  
Author(s):  
Alice Fremand
Keyword(s):  
Data Management ◽  
Data Science ◽  
New Technologies ◽  
Open Data ◽  
Research Data ◽  
Complex Data ◽  
Data Set ◽  
Data Publication ◽  
World Data ◽  
The World

<p>Open data is not a new concept. Over sixty years ago in 1959, knowledge sharing was at the heart of the Antarctic Treaty which included in article III 1c the statement: “scientific observations and results from Antarctica shall be exchanged and made freely available”. ​At a similar time, the World Data Centre (WDC) system was created to manage and distribute the data collected from the International Geophysical Year (1957-1958) led by the International Council of Science (ICSU) building the foundations of today’s research data management practices.</p><p>What about now? The WDC system still exists through the World Data System (WDS). Open data has been endorsed by a majority of funders and stakeholders. Technology has dramatically evolved. And the profession of data manager/curator has emerged. Utilising their professional expertise means that their role is far wider than the long-term curation and publication of data sets.</p><p>Data managers are involved in all stages of the data life cycle: from data management planning, data accessioning to data publication and re-use. They implement open data policies; help write data management plans and provide advice on how to manage data during, and beyond the life of, a science project. In liaison with software developers as well as scientists, they are developing new strategies to publish data either via data catalogues, via more sophisticated map-based viewer services or in machine-readable form via APIs. Often, they bring the expertise of the field they are working in to better assist scientists satisfy Findable, Accessible, Interoperable and Re-usable (FAIR) principles. Recent years have seen the development of a large community of experts that are essential to share, discuss and set new standards and procedures. The data are published to be re-used, and data managers are key to promoting high-quality datasets and participation in large data compilations.</p><p>To date, there is no magical formula for FAIR data. The Research Data Alliance is a great platform allowing data managers and researchers to work together, develop and adopt infrastructure that promotes data-sharing and data-driven research. However, the challenge to properly describe each data set remains. Today, scientists are expecting more and more from their data publication or data requests: they want interactive maps, they want more complex data systems, they want to query data, combine data from different sources and publish them rapidly.  By developing new procedures and standards, and looking at new technologies, data managers help set the foundations to data science.</p>

scholarly journals Novel tools and methods for designing and wrangling multifunctional, machine-readable evidence synthesis databases

2021 ◽  
Author(s):  
Neal Robert Haddaway ◽  
Charles T. Gray ◽  
Matthew Grainger
Keyword(s):  
Systematic Review ◽  
Data Science ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Open Data ◽  
Study Data ◽  
Data Reuse ◽  
Complex Data ◽  
Standard Practices ◽  
Machine Readable

One of the most important steps in the process of conducting a systematic review or map is data extraction and the production of a database of coding, metadata and study data. There are many ways to structure these data, but to date, no guidelines or standards have been produced for the evidence synthesis community to support their production. Furthermore, there is little adoption of easily machine-readable, readily reusable and adaptable databases: these databases would be easier to translate into different formats by review authors, for example for tabulation, visualisation and analysis, and also by readers of the review/map. As a result, it is common for systematic review and map authors to produce bespoke, complex data structures that, although typically provided digitally, require considerable efforts to understand, verify and reuse. Here, we report on an analysis of systematic reviews and maps published by the Collaboration for Environmental Evidence, and discuss major issues that hamper machine readability and data reuse or verification. We highlight different justifications for the alternative data formats found: condensed databases; long databases; and wide databases. We describe these challenges in the context of data science principles that can support curation and publication of machine-readable, Open Data. We then go on to make recommendations to review and map authors on how to plan and structure their data, and we provide a suite of novel R-based functions to support efficient and reliable translation of databases between formats that are useful for presentation (condensed, human readable tables), filtering and visualisation (wide databases), and analysis (long databases). We hope that our recommendations for adoption of standard practices in database formatting, and the tools necessary to rapidly move between formats will provide a step-change in transparency and replicability of Open Data in evidence synthesis.

scholarly journals Recommendations to enhance rigor and reproducibility in biomedical research

GigaScience ◽  
2020 ◽  
Vol 9 (6) ◽  
Author(s):  
Jaqueline J Brito ◽  
Jun Li ◽  
Jason H Moore ◽  
Casey S Greene ◽  
Nicole A Nogoy ◽  
...  
Keyword(s):  
Biomedical Research ◽  
Data Science ◽  
Life Science ◽  
Source Code ◽  
Open Data ◽  
Science Research ◽  
Scientific Contribution ◽  
Subsequent Work ◽  
Computational Tools

Abstract Biomedical research depends increasingly on computational tools, but mechanisms ensuring open data, open software, and reproducibility are variably enforced by academic institutions, funders, and publishers. Publications may present software for which source code or documentation are or become unavailable; this compromises the role of peer review in evaluating technical strength and scientific contribution. Incomplete ancillary information for an academic software package may bias or limit subsequent work. We provide 8 recommendations to improve reproducibility, transparency, and rigor in computational biology—precisely the values that should be emphasized in life science curricula. Our recommendations for improving software availability, usability, and archival stability aim to foster a sustainable data science ecosystem in life science research.

scholarly journals Novel tools and methods for designing and wrangling multifunctional, machine-readable evidence synthesis databases

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Neal R. Haddaway ◽  
Charles T. Gray ◽  
Matthew Grainger
Keyword(s):  
Systematic Review ◽  
Data Science ◽  
Data Extraction ◽  
Evidence Synthesis ◽  
Open Data ◽  
Study Data ◽  
Data Reuse ◽  
Complex Data ◽  
Standard Practices ◽  
Machine Readable

AbstractOne of the most important steps in the process of conducting a systematic review or map is data extraction and the production of a database of coding, metadata and study data. There are many ways to structure these data, but to date, no guidelines or standards have been produced for the evidence synthesis community to support their production. Furthermore, there is little adoption of easily machine-readable, readily reusable and adaptable databases: these databases would be easier to translate into different formats by review authors, for example for tabulation, visualisation and analysis, and also by readers of the review/map. As a result, it is common for systematic review and map authors to produce bespoke, complex data structures that, although typically provided digitally, require considerable efforts to understand, verify and reuse. Here, we report on an analysis of systematic reviews and maps published by the Collaboration for Environmental Evidence, and discuss major issues that hamper machine readability and data reuse or verification. We highlight different justifications for the alternative data formats found: condensed databases; long databases; and wide databases. We describe these challenges in the context of data science principles that can support curation and publication of machine-readable, Open Data. We then go on to make recommendations to review and map authors on how to plan and structure their data, and we provide a suite of novel R-based functions to support efficient and reliable translation of databases between formats that are useful for presentation (condensed, human readable tables), filtering and visualisation (wide databases), and analysis (long databases). We hope that our recommendations for adoption of standard practices in database formatting, and the tools necessary to rapidly move between formats will provide a step-change in transparency and replicability of Open Data in evidence synthesis.

Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging

2019 ◽  
Vol 2 (1) ◽  
pp. 119-138 ◽  
Author(s):  
Russell A. Poldrack ◽  
Krzysztof J. Gorgolewski ◽  
Gaël Varoquaux
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Open Data ◽  
Data Sets ◽  
Imaging Data ◽  
Python Language ◽  
Neuroimaging Data ◽  
Brain Imaging Data ◽  
Human Neuroimaging

The reproducibility of scientific research has become a point of critical concern. We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community. We discuss the range of open data-sharing resources that have been developed for neuroimaging data, as well as the role of data standards (particularly the brain imaging data structure) in enabling the automated sharing, processing, and reuse of large neuroimaging data sets. We outline how the open source Python language has provided the basis for a data science platform that enables reproducible data analysis and visualization. We also discuss how new advances in software engineering, such as containerization, provide the basis for greater reproducibility in data analysis. The emergence of this new ecosystem provides an example for many areas of science that are currently struggling with reproducibility.

scholarly journals Selenium and Dogs: A Systematic Review

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 418
Author(s):  
Viola Zentrichová ◽  
Alena Pechová ◽  
Simona Kovaříková
Keyword(s):  
Male Fertility ◽  
Clinical Signs ◽  
Complex Data ◽  
Selenium Status ◽  
Dog Food ◽  
Balanced Diet ◽  
Naturally Occurring ◽  
Correct Function ◽  
Indirect Assay

The intent of this review is to summarize the knowledge about selenium and its function in a dog’s body. For this purpose, systematic literature search was conducted. For mammals, including dogs, a balanced diet and sufficient intake of selenium are important for correct function of metabolism. As for selenium poisoning, there are no naturally occurring cases known. Nowadays, we do not encounter clinical signs of its deficiency either, but it can be subclinical. For now, the most reliable method of assessing selenium status of a dog is measuring serum or plasma levels. Levels in full blood can be measured too, but there are no reference values. The use of glutathione peroxidase as an indirect assay is questionable in canines. Commercial dog food manufactures follow recommendations for minimal and maximal selenium levels and so dogs fed commercial diets should have balanced intake of selenium. For dogs fed home-made diets, complex data are missing. However, subclinical deficiency seems to affect, for example, male fertility or recovery from parasitical diseases. Very interesting is the role of selenium in prevention and treatment of cancer.

scholarly journals The Role of Network Science in Glioblastoma

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1045
Author(s):  
Marta B. Lopes ◽  
Eduarda P. Martins ◽  
Susana Vinga ◽  
Bruno M. Costa
Keyword(s):  
Personalized Medicine ◽  
Drug Development ◽  
Information Flow ◽  
Clinical Studies ◽  
Network Science ◽  
Cancer Genomics ◽  
High Dimensional ◽  
Network Discovery ◽  
Software Implementations

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

Sign in / Sign up

Export Citation Format

Share Document