Scientists have standardized on the SI unit system since the late 1700’s. While much work has been done over the years to refine and redefine the system, little has formally done to standardize the representation of the SI units in electronic systems.
This paper will present a summary of current efforts toward electronic representation of scientific units in text, XML, and RDF, an analysis of needs for current computer/network systems, and an outline of future work.
A Generic Scientific Data Model and Ontology for Representation of Chemical DataStuart Chalk
The current movement toward openness and sharing of data is likely to have a profound effect on the speed of scientific research and the complexity of questions we can answer. However, a fundamental problem with currently available datasets (and their metadata) is heterogeneity in terms of implementation, organization, and representation.
To address this issue we have developed a generic scientific data model (SDM) to organize and annotate raw and processed data, and the associated metadata. This paper will present the current status of the SDM, implementation of the SDM in JSON-LD, and the associated scientific data model ontology (SDMO). Example usage of the SDM to store data from a variety of sources with be discussed along with future plans for the work.
Rule-based Capture/Storage of Scientific Data from PDF Files and Export using...Stuart Chalk
Recently, the US government has mandated that publicly funded scientific research data be freely made available in a useable form, allowing integration of data in other systems. While this mandate has been articulated, existing publications and new papers (PDF) still do not provide accessible data, meaning that the usefulness is limited without human intervention.
This presentation outlines our efforts to extract scientific data from PDF files, using the PDFToText software and regular expressions (regex), and process it into a form that structures the data and its context (metadata). Extracted data is processed (cleaned, normalized), organized, and inserted into a contextually developed MySQL database. The data and metadata can then be output using a generic JSON-LD based scientific data model (SDM) under development in our laboratory.
Toward Semantic Representation of Science in Electronic Laboratory Notebooks ...Stuart Chalk
An electronic laboratory Notebook (ELN) can be characterized as a system that allows scientists to capture the data and resources used in performing scientific experiments. This allows users to easily organize and find their data however, little information about the scientific process is recorded.
In this paper we highlight the current status of progress toward semantic representation of science in ELNs.
Started in 2004 (under ASTM Committee E13.15) the Analytical Information Markup Language (AnIML) is an XML based standard for capturing, sharing, viewing, and archiving analytical instrument data from any analytical technique.
This paper discusses the AnIML standard in terms of philosophy, structure, usage, and the resources available to work with the standard. Examples will be given for different techniques as well as strategies for migration of legacy data. Finally, the current status of the standard and time frame for promulgation through ASTM will be reported.
Eureka Research Workbench: A Semantic Approach to an Open Source Electroni...Stuart Chalk
Scientists are looking for ways to leverage web 2.0 technologies in the research laboratory and as a consequence a number of approaches to web-based electronic notebooks are being evaluated. In this presentation I discuss the Eureka Research Workbench, an electronic laboratory notebook built on semantic technology and XML. Using this approach the context of the information recorded in the laboratory can be captured and searched along with the data itself. A discussion of the current system is presented along with the next planned development of the framework and long-term plans relative to linked open data. Presented at the 246th American Chemical Society Meeting in Indianapolis, IN, USA on September 12th, 2013.
247th ACS Meeting: The Eureka Research WorkbenchStuart Chalk
Academic scientists need a tool to capture the science they do so that it can be shared in open science, integrated with linked data, and shared/searched. Eureka is an evolving platform to do this.
A Generic Scientific Data Model and Ontology for Representation of Chemical DataStuart Chalk
The current movement toward openness and sharing of data is likely to have a profound effect on the speed of scientific research and the complexity of questions we can answer. However, a fundamental problem with currently available datasets (and their metadata) is heterogeneity in terms of implementation, organization, and representation.
To address this issue we have developed a generic scientific data model (SDM) to organize and annotate raw and processed data, and the associated metadata. This paper will present the current status of the SDM, implementation of the SDM in JSON-LD, and the associated scientific data model ontology (SDMO). Example usage of the SDM to store data from a variety of sources with be discussed along with future plans for the work.
Rule-based Capture/Storage of Scientific Data from PDF Files and Export using...Stuart Chalk
Recently, the US government has mandated that publicly funded scientific research data be freely made available in a useable form, allowing integration of data in other systems. While this mandate has been articulated, existing publications and new papers (PDF) still do not provide accessible data, meaning that the usefulness is limited without human intervention.
This presentation outlines our efforts to extract scientific data from PDF files, using the PDFToText software and regular expressions (regex), and process it into a form that structures the data and its context (metadata). Extracted data is processed (cleaned, normalized), organized, and inserted into a contextually developed MySQL database. The data and metadata can then be output using a generic JSON-LD based scientific data model (SDM) under development in our laboratory.
Toward Semantic Representation of Science in Electronic Laboratory Notebooks ...Stuart Chalk
An electronic laboratory Notebook (ELN) can be characterized as a system that allows scientists to capture the data and resources used in performing scientific experiments. This allows users to easily organize and find their data however, little information about the scientific process is recorded.
In this paper we highlight the current status of progress toward semantic representation of science in ELNs.
Started in 2004 (under ASTM Committee E13.15) the Analytical Information Markup Language (AnIML) is an XML based standard for capturing, sharing, viewing, and archiving analytical instrument data from any analytical technique.
This paper discusses the AnIML standard in terms of philosophy, structure, usage, and the resources available to work with the standard. Examples will be given for different techniques as well as strategies for migration of legacy data. Finally, the current status of the standard and time frame for promulgation through ASTM will be reported.
Eureka Research Workbench: A Semantic Approach to an Open Source Electroni...Stuart Chalk
Scientists are looking for ways to leverage web 2.0 technologies in the research laboratory and as a consequence a number of approaches to web-based electronic notebooks are being evaluated. In this presentation I discuss the Eureka Research Workbench, an electronic laboratory notebook built on semantic technology and XML. Using this approach the context of the information recorded in the laboratory can be captured and searched along with the data itself. A discussion of the current system is presented along with the next planned development of the framework and long-term plans relative to linked open data. Presented at the 246th American Chemical Society Meeting in Indianapolis, IN, USA on September 12th, 2013.
247th ACS Meeting: The Eureka Research WorkbenchStuart Chalk
Academic scientists need a tool to capture the science they do so that it can be shared in open science, integrated with linked data, and shared/searched. Eureka is an evolving platform to do this.
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
ACS 248th Paper 136 JSmol/JSpecView Eureka IntegrationStuart Chalk
Integration of the combined JSmol/JSpecView molecular viewer/spectral viewer software in the Eureka Research Workbench. Can display molecular structures, spectra and the linked version where clicking on a peak shows molecular movement (IR).
Profiling systems have achieved notable adoption by research institutions.1 Multi-site search of research profiling systems has substantially evolved since the first deployment of systems such as DIRECT2Experts.2 CTSAsearch is a federated search engine using VIVO-compliant Linked Open Data (LOD) published by members of the NIH-funded Clinical and Translational Science (CTSA) consortium and other interested parties. Sixty-four institutions are currently included, spanning six distinct platforms and three continents (North America, Europe and Australia). In aggregate, CTSAsearch has data on 150-300 thousand unique researchers and their 10 million publications. The public interface is available at http://research.icts.uiowa.edu/polyglot.
Research Data Sharing: A Basic FrameworkPaul Groth
Some thoughts on thinking about data sharing. Prepared for the 2016 LERU Doctoral Summer School - Data Stewardship for Scientific Discovery and Innovation.
http://www.dtls.nl/fair-data/fair-data-training/leru-summer-school/
COMBINE 2019, EU-STANDS4PM, Heidelberg, Germany 18 July 2019
FAIR: Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any other kind of Research Object one can think of, is now a mantra; a method; a meme; a myth; a mystery. FAIR is about supporting and tracking the flow and availability of data across research organisations and the portability and sustainability of processing methods to enable transparent and reproducible results. All this is within the context of a bottom up society of collaborating (or burdened?) scientists, a top down collective of compliance-focused funders and policy makers and an in-the-middle posse of e-infrastructure providers.
Making the FAIR principles a reality is tricky. They are aspirations not standards. They are multi-dimensional and dependent on context such as the sensitivity and availability of the data and methods. We already see a jungle of projects, initiatives and programmes wrestling with the challenges. FAIR efforts have particularly focused on the “last mile” – “FAIRifying” destination community archive repositories and measuring their “compliance” to FAIR metrics (or less controversially “indicators”). But what about FAIR at the first mile, at source and how do we help Alice and Bob with their (secure) data management? If we tackle the FAIR first and last mile, what about the FAIR middle? What about FAIR beyond just data – like exchanging and reusing pipelines for precision medicine?
Since 2008 the FAIRDOM collaboration [1] has worked on FAIR asset management and the development of a FAIR asset Commons for multi-partner researcher projects [2], initially in the Systems Biology field. Since 2016 we have been working with the BioCompute Object Partnership [3] on standardising computational records of HTS precision medicine pipelines.
So, using our FAIRDOM and BioCompute Object binoculars let’s go on a FAIR safari! Let’s peruse the ecosystem, observe the different herds and reflect what where we are for FAIR personalised medicine.
References
[1] http://www.fair-dom.org
[2] http://www.fairdomhub.org
[3] http://www.biocomputeobject.org
Linking Scientific Metadata (presented at DC2010)Jian Qin
Linked entity data in metadata records builds a foundation for semantic web. Even though metadata records contain rich entity data, there is no linking between associated entities such as persons, datasets, projects, publications, or organizations. We conducted a small experiment using the dataset collection from the Hubbard Brook Ecosystem Study (HBES), in which we converted the entities and their relationships into RDF triples and linked the URIs contained in RDF triples to the corresponding entities in the Ecological Metadata Language (EML) records. Through the transformation program written in XML Stylesheet Language (XSL), we turned a plain EML record display into an interlinked semantic web of ecological datasets. The experiment suggests a methodological feasibility in incorporating linked entity data into metadata records. The paper also argues for the need of changing the scientific as well as general metadata paradigm.
In our series on The Yosemite Project, we explore RDF as a data standard for health data. In this presentation, we will discuss with Claude Nanjo, a Software Architect at Cognitive Medical Systems, ways to expose clinical knowledge as OWL and RDF resources on the Web in order to promote greater convergence in the representation of health knowledge in the longer term. We will also explore how one might rally and coordinate the community to seed the Web with a core set of high-value resources and technologies that could greatly enhance health interoperability.
Implementing the Open Government Directive using the technologies of the Soci...George Thomas
This presentation demonstrates the use of Semantic Web technologies with Social Networking tools, considering metadata specifications as Social Media. Example ontologies and instance data from the Capital Planning and Investment Control and Business Motivation are created that link 'what' (Agency IT investments) with 'why' (Agency goals and objectives), using a simple linking ontology. Knowledge Workers use a Semantic Halo Mediawiki to curate the data.
Model-Based Systems Engineering (MBSE) is an ambiguous concept that means many things to many different people. The purpose of this presentation is to “de-mystify” MBSE, with the intent of moving the sub-discipline forward. Model-Based Systems Engineering was envisioned to manage the increasing complexity within systems and System of Systems (SoS). This presentation defines MBSE as the formalized application of modeling (static and dynamic) to support system design and analysis, throughout all phases of the system lifecycle, and through the collection of modeling languages, structures, model-based processes, and presentation frameworks used to support the discipline of systems engineering in a model-based or model-driven context. Using this definition, the components of MBSE (modeling languages, processes, structures, and presentation frameworks) are defined. The current state of MBSE is then evaluated against a set of effective measures. Finally, this presents a vision for the future direction of MBSE.
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
ACS 248th Paper 136 JSmol/JSpecView Eureka IntegrationStuart Chalk
Integration of the combined JSmol/JSpecView molecular viewer/spectral viewer software in the Eureka Research Workbench. Can display molecular structures, spectra and the linked version where clicking on a peak shows molecular movement (IR).
Profiling systems have achieved notable adoption by research institutions.1 Multi-site search of research profiling systems has substantially evolved since the first deployment of systems such as DIRECT2Experts.2 CTSAsearch is a federated search engine using VIVO-compliant Linked Open Data (LOD) published by members of the NIH-funded Clinical and Translational Science (CTSA) consortium and other interested parties. Sixty-four institutions are currently included, spanning six distinct platforms and three continents (North America, Europe and Australia). In aggregate, CTSAsearch has data on 150-300 thousand unique researchers and their 10 million publications. The public interface is available at http://research.icts.uiowa.edu/polyglot.
Research Data Sharing: A Basic FrameworkPaul Groth
Some thoughts on thinking about data sharing. Prepared for the 2016 LERU Doctoral Summer School - Data Stewardship for Scientific Discovery and Innovation.
http://www.dtls.nl/fair-data/fair-data-training/leru-summer-school/
COMBINE 2019, EU-STANDS4PM, Heidelberg, Germany 18 July 2019
FAIR: Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any other kind of Research Object one can think of, is now a mantra; a method; a meme; a myth; a mystery. FAIR is about supporting and tracking the flow and availability of data across research organisations and the portability and sustainability of processing methods to enable transparent and reproducible results. All this is within the context of a bottom up society of collaborating (or burdened?) scientists, a top down collective of compliance-focused funders and policy makers and an in-the-middle posse of e-infrastructure providers.
Making the FAIR principles a reality is tricky. They are aspirations not standards. They are multi-dimensional and dependent on context such as the sensitivity and availability of the data and methods. We already see a jungle of projects, initiatives and programmes wrestling with the challenges. FAIR efforts have particularly focused on the “last mile” – “FAIRifying” destination community archive repositories and measuring their “compliance” to FAIR metrics (or less controversially “indicators”). But what about FAIR at the first mile, at source and how do we help Alice and Bob with their (secure) data management? If we tackle the FAIR first and last mile, what about the FAIR middle? What about FAIR beyond just data – like exchanging and reusing pipelines for precision medicine?
Since 2008 the FAIRDOM collaboration [1] has worked on FAIR asset management and the development of a FAIR asset Commons for multi-partner researcher projects [2], initially in the Systems Biology field. Since 2016 we have been working with the BioCompute Object Partnership [3] on standardising computational records of HTS precision medicine pipelines.
So, using our FAIRDOM and BioCompute Object binoculars let’s go on a FAIR safari! Let’s peruse the ecosystem, observe the different herds and reflect what where we are for FAIR personalised medicine.
References
[1] http://www.fair-dom.org
[2] http://www.fairdomhub.org
[3] http://www.biocomputeobject.org
Linking Scientific Metadata (presented at DC2010)Jian Qin
Linked entity data in metadata records builds a foundation for semantic web. Even though metadata records contain rich entity data, there is no linking between associated entities such as persons, datasets, projects, publications, or organizations. We conducted a small experiment using the dataset collection from the Hubbard Brook Ecosystem Study (HBES), in which we converted the entities and their relationships into RDF triples and linked the URIs contained in RDF triples to the corresponding entities in the Ecological Metadata Language (EML) records. Through the transformation program written in XML Stylesheet Language (XSL), we turned a plain EML record display into an interlinked semantic web of ecological datasets. The experiment suggests a methodological feasibility in incorporating linked entity data into metadata records. The paper also argues for the need of changing the scientific as well as general metadata paradigm.
In our series on The Yosemite Project, we explore RDF as a data standard for health data. In this presentation, we will discuss with Claude Nanjo, a Software Architect at Cognitive Medical Systems, ways to expose clinical knowledge as OWL and RDF resources on the Web in order to promote greater convergence in the representation of health knowledge in the longer term. We will also explore how one might rally and coordinate the community to seed the Web with a core set of high-value resources and technologies that could greatly enhance health interoperability.
Implementing the Open Government Directive using the technologies of the Soci...George Thomas
This presentation demonstrates the use of Semantic Web technologies with Social Networking tools, considering metadata specifications as Social Media. Example ontologies and instance data from the Capital Planning and Investment Control and Business Motivation are created that link 'what' (Agency IT investments) with 'why' (Agency goals and objectives), using a simple linking ontology. Knowledge Workers use a Semantic Halo Mediawiki to curate the data.
Model-Based Systems Engineering (MBSE) is an ambiguous concept that means many things to many different people. The purpose of this presentation is to “de-mystify” MBSE, with the intent of moving the sub-discipline forward. Model-Based Systems Engineering was envisioned to manage the increasing complexity within systems and System of Systems (SoS). This presentation defines MBSE as the formalized application of modeling (static and dynamic) to support system design and analysis, throughout all phases of the system lifecycle, and through the collection of modeling languages, structures, model-based processes, and presentation frameworks used to support the discipline of systems engineering in a model-based or model-driven context. Using this definition, the components of MBSE (modeling languages, processes, structures, and presentation frameworks) are defined. The current state of MBSE is then evaluated against a set of effective measures. Finally, this presents a vision for the future direction of MBSE.
Kelly technologies is the best data science training institute in hyderabad.We provide our trainings by industrial real time experts so that our students know about real time market technology.
The presentation I gave at the 2007 Semantic Technology Conference. Declarative programming” has become the latest buzzword to describe languages that abstractly define systems requirements (the what) and leave the implementation (the how) to be determined by an independent process. This makes the semantics (meaning) of declarative data elements even more critical as these systems are shared between organizations. This presentation: (1) Provides a background of declarative programming (2) Describes why understanding the semantic aspects of declarative systems is critical to cost-effective software development.
Driving Deep Semantics in Middleware and Networks: What, why and how?Amit Sheth
Amit Sheth, "Driving Deep Semantics in Middleware and Networks: What, why and how?," Keynote talk at Semantic Sensor Networks Workshop at the 5th International Semantic Web Conference (ISWC-2006), November 6, 2006, Athens, Georgia, USA.
Semantic Web in Action: Ontology-driven information search, integration and a...Amit Sheth
Amit Sheth's Keynote talk given at: “Semantic Web in Action: Ontology-driven information search, integration and analysis,” Net Object Days 2003 and MATES03, Erfurt, Germany, September 23, 2003. http://knoesis.org
Note: slides 51-55 have audio.
DBMS UNIT_1: Introduction Application of DMBS,
Advantages & Disadvantages.
Internal Level/Schema
Conceptual Level/Schema
Physical Level/Schema
Logical Data Independence
Physical Data Independence
Role of DBA
Type of Database user
Environment Canada's Data Management ServiceSafe Software
A brief history in TimeSeries data at Environment Canada. An Enterprise view of how FME can be integrated into departmental data management activities.
Registry types, Synergies and Differences (Data registry, metadata registry, terminology registry, ...) Talk at the NKOS Special Session, International Conference on Dublin Core and Metadata Applications, Berlin, 2008-09-22-26.
Amit P. Sheth, “Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships,” Keynote at the 29th Conference on Current Trends in Theory and Practice of Informatics (SOFSEM 2002), Milovy, Czech Republic, November 22–29, 2002.
Keynote: http://www.sofsem.cz/sofsem02/keynote.html
Related paper: http://knoesis.wright.edu/?q=node/2063
This whitepaper focuses on “real-world” systems, that is, systems that interact with the external physical world and must live within the constraints imposed by real-world physics. Good examples include air-traffic control systems, real-time stock trading, command and control (C2) systems, unmanned vehicles, robotic and vetronics, and Supervisory Control and Data Acquisition (SCADA) systems.
More and more these “real-world” systems are integrated using a Data-Centric Publish- Subscribe approach, specifically the programming model defined by the Object Management Group (OMG) Data Distribution Service (DDS) specification.
This whitepaper describes the basic characteristics of real-world systems programming, reasons why DDS is the best standard middleware technology to use to integrate these systems, and a set of “best practices” guidelines that should be applied when using DDS to implement these systems.
Panel presentation to a graduate class at the University of Arizona School of Information Resources and Library Science. Invited by Dr. Jana Bradley. July 2006.
This paper describe a proposed open semantic representation of chemical structure using JSON-LD (JSON for linked data) and an example of the semantic inferencing of a chemical structure concept (chirality).
ChemExtractor: Enhanced Rule-Based Capture and Identification of PDF Based Pr...Stuart Chalk
Paper presented at the 253rd ACS Meeting in San Francisco, CA. Presentation describes an approach to extracting chemical property data from PDF documents using single line regular expression (regex) created from regex snippets (for data elements) and a regex template (pattern of data elements).
Science is rapidly being brought into the electronic realm and electronic laboratory notebooks (ELN) are a big part of this activity. The representation of the scientific process in the context of an ELN is an important component to making the data recorded in ELNs semantically integrated.
This presentation outlined initial developments of an Electronic Notebook Ontology (ENO) that will help tie together the ExptML ontology, HCLS Community Profile data descriptions, and the VIVO-ISF ontology.
Sharing Science Data: Semantically Reimagining the IUPAC Solubility Series DataStuart Chalk
The IUPAC Solubility Data Series published its first volume in 1979. Since then over 100 volumes of high quality peer reviewed solubility data has been published, first in hardcopy and subsequently electronically as part of the Journal of Physical and Chemistry Reference Data.
In February of this year the National Institute of Standards and Technology (NIST) funded a grant to explore taking the 18 currently available online volumes of data and re-purpose them as a REST based website, with documented API, and semantic representation/annotation. In this way the high quality data from these volumes can be shared, both to humans and computers. In addition, the semantic representation of the data allows integration of the data with other semantically enabled data at repositories across the globe.
This presentation will give an overview of the process of schema development for the dataset, implementation in MySQL, website construction in the CakePHP framework, and architecture of the API access points. A report on the ontology development to support the project will also be discussed.
Bringing Flow injection Analysis to the Semantic WebStuart Chalk
As a mechanism to improve the sharing of data in Flow Injection Analysis, the Flow Analysis Database (http://www.fia.unf.edu) has been re-imagined to improve communication of the research on FIA, SIA, and related technologies across the vibrant communities in Europe, Asia, and the Americas.
This talk will present the new version of the Flow Analysis Database by highlighting
- The REST interface for each access to citation, analyte, matrix, technique, and keyword based resources
- Documented API for automated data integration
- Integration of the ChAMP specification
- Ontological support for FA concepts
- Individual user accounts with author bibliography
Future additions will include
- Language translation support using Google Translate
- ORCID integration
- Personal FIA library, and update notification
Reactions to the Open Spectral DatabaseStuart Chalk
In honor of JC Bradley, and the spirit of openness that he inspired, a new online resource called the Open Spectral Database (http://www.osdb.info) was announced in August of this year (aplha version). Built using open source tools, using open code, and open to community input about design and functionality, the OSD is available for anyone to submit spectral data and make it available to the scientific community. This paper will detail the reaction to the website, look at how the site has evolved since August (beta version), and offer a glimpse of what the future may bring for the site.
A Standard Data Format for Computational Chemistry: CSXStuart Chalk
An overview of the Common Standard for eXchange (CSX) a new markup language for the storage of computational chemistry calculation data. CSX stores publication and molecular system metadata along with calculation data and, optionally, raw input and output files associated with a calculation. The computational chemistry community is invited to participate in the development of CSX. For more information see http://www.chemicalsemantics.com.
Overview of the Analytical Information Markup Language (AnIML)Stuart Chalk
Overview and update on the Analytical Information Markup Language (AnIML) a standard for storage of analytical instrument data and associated metadata. http://animl.sourceforge.net
ACS 248th Paper 146 VIVO/ScientistsDB Integration into EurekaStuart Chalk
Development of plugins for access to researchers identified in VIVO on the ScientistsDB website. Also developed a plugin to access Elasticsearch from within Eureka.
Presentation on a project run in my Chemical Information Science course. Valuable referenced chemical data from 'reliable' static webpages was 'scraped', cleaned, and added to a database for search.
Presentation on the Chemical Analysis Metadata Platform (ChAMP) as a new project to characterize and organize metadata about chemical analysis methods. The project will develop an ontology, controlled vocabularies, and design rules
247th ACS Meeting: Experiment Markup Language (ExptML)Stuart Chalk
To integrate science into the semantic web it is important to capture the context of research as it is done. ExptML is designed to store information and workflows from the scientific process.
Presentation on the use of the Eureka Research Workbench to store data and scientific workflow information. Presented online as part of the Dial-a-molecule 'Liberating Laboratory Data' event (http://www.dial-a-molecule.org/wp/events-listing/liberating-laboratory-data/)
Presentation on the use of the Analytical Information Markup Language (AnIML) to store and access scientific data. Presented online as part of the Dial-a-molecule 'Liberating Laboratory Data' event (http://www.dial-a-molecule.org/wp/events-listing/liberating-laboratory-data/)
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...
Scientific Units in the Electronic Age
1. Scientific Units
in the Electronic Age
Stuart J. Chalk, Department of Chemistry
University of North Florida
schalk@unf.edu
CINF Paper 49 – 251st ACS Meeting Spring 2016
#ACSCINFDataSummit
2. Why Do Computers Need to Know About Units?
What Do We Need?
Unit Systems
Implementation of
Units for Computers
Text Units
XML Units
Semantic Units
What We Really Need
Things To Do
Conclusion
Outline
From: http://unitsml.nist.gov/Presentations/UnitsML_for_TC.pdf
3.
4. Computers are used to represent data
Data is not useful unless it has a context – meaning
Part of the context of data is its unit of measure
Publication of scientific data – it needs definitive units!
Why do Computers
Need to Know About Units?
From: http://www.slideshare.net/petermurrayrust/text-and-data-mining-explained-at-ftdm
5. What Do We Need?
A way to uniquely identify and give meaning to units…
…and dimensions, quantities, properties(?)
A way to uniquely identify and give meaning to fundamental
constants and conversion factors
Must describe the semantics of prefixes
Must accommodate all languages (spoken and computer)
Must be usable at different levels of technology
Must be unit system agnostic
Must be as future-proof as possible (extensible/adaptable)
6. What Do We Need?
A standards organization to host a platform providing
unambiguous representations of any unit of measure
Tools/services to allow identification of units that can be
used to represent the same quantity
Tools/services to allow systems to interconvert units and
provide a mechanism to document the conversion
Must be compliant with the tools/services supporting big
data and the semantic web
8. International System of Units (SI)
Including CGS and MKS
UK Imperial System
US Customary Units
Burmese
Indian
Astronomical Units
Troy (mass) Units
Historical/Obsolete Units and Unit Systems
https://en.wikipedia.org/wiki/List_of_obsolete_units_of_measurement
Unit Systems
10. International Virtual Observatory Alliance (IVOA)
http://www.ivoa.net/documents/VOUnits/
Standardization of string representations of unit
labels (“VOUnits”) in the astronomy community
Text String Representation
From: http://www.ivoa.net/documents/VOUnits/20140523/VOUnits-REC-1.0-20140523.pdf
11. MathML (https://www.w3.org/TR/mathml-units)
Presentation of Units
Unit Symbols in Content MathML
Conversion
of Units
XML Representation
<apply>
<divide/>
<csymbol definitionURL='http://.../units/meter#c>cm</csymbol>
<csymbol definitionURL='http://.../units/second>s</csymbol>
</apply>
<apply>
<times/>
<csymbol definitionURL='http://.../units/meter#c>cm</csymbol>
<apply>
<power/>
<csymbol definitionURL='http://.../units/second>s</csymbol>
<cn type='integer'>-1</cn>
</apply>
</apply>
<csymbol definitionURL='http://.../units/kyne'>kyn</csymbol>
12. Geographic Markup Language (GML)
http://www.opengeospatial.org/standards/gml
Provides
Unit References (“unitOfMeasure” element)
Unit Definitions (“unitDefinition” element)
“BaseUnit”, “DerivedUnit”, and “ConventionalUnit”
XML Representation
<gml:DerivedUnit gml:id="m3">
<gml:identifier codeSpace=“…/?iid=79">cubic metre</gml:identifier>
<gml:quantityType>Volume</gml:quantityType>
<gml:derivationUnitTerm uom="#m" exponent="3"/>
</gml:DerivedUnit>
13. Scientific, Technical, and
Medical Publishing (STTML)
Part of the Chemical Markup Language (CML)
http://cml.sourceforge.net/schema/
XML Representation
<stm:unit id="second" name="second" unitType="time">
<stm:description>The SI unit of time</stm:description>
</stm:unit>
<stm:unit id="newton" name="newton" unitType="force”>
<stm:description>The SI unit of force</stm:description>
</stm:unit>
<stm:unit id="g" name="gram" unitType="mass" parentSI="kg" multiplierToSI="0.001"
abbreviation="g”>
<stm:description>0.001 kg.</stm:description>
</stm:unit>
<stm:unit id="inch" name="inch" parentSI="meter" abbreviation="in" multiplierToSI="0.0254" >
<stm:description>An imperial measure of length</stm:description>
</stm:unit>
14. UnitsML
http://unitsml.nist.gov/
NIST Project
– intended to be published as a standard under OASIS
Fundamental representation of dimensions, units, quantities
UnitsDB a symbiotic project (internal to NIST)
Never formally standardized under OASIS…
…and NIST ran out of funding
XML Representation
19. “Semantics” - the branch of linguistics and
logic concerned with meaning
Rather than just identify a unit,
indicate the meaning of a unit
Resource Description Framework (RDF)
Subject-Predicate-Object “triples”
RDF-XML (https://www.w3.org/TR/rdf-syntax-grammar/)
JSON-LD (JSON for Linked Data)
Ontological Definitions
Semantic Representation
20. Units of Measure Ontology (UO)
https://github.com/bio-ontology-research-group/unit-ontology
http://www.ontobee.org/ontology/UO
Semantic Representation
21. Semantic Web for Earth
and Environmental
Technology (SWEET)
http://sweet.jpl.nasa.gov/
“SWEET 2.3 is highly modular
with 6000 concepts in 200
separate ontologies.”
Semantic Representation
22. Quantities, Units, Dimensions and Data Types Ontology (QUDT)
Version 1
http://www.qudt.org/
Version 2
http://www.linkedmodel.org/doc/2015/DOC_schema-qudt-v2.0
Space Time Vocabulary
http://qudt.org/vocab/unit/units-space-and-time.html
Physical Chemistry and Molecular Physics Vocabulary
http://qudt.org/vocab/unit/units-physical-chemistry-and-
molecular-physics.html
Semantic Representation
31. Write up a summary of current state of units
Develop a proposal to implement units in computers based on
best features of existing work
Development of a metrology ontology
(33 results on google – none are a general ontology for metrology)
Evaluate support technology needed to implement the ontology
Identify specific use cases and implement solutions
Formalize as a standard, with use cases and example
implementations
Applicable for text, XML, and Semantic formats…
...databases, ELNs, instrument software,
Things To Do