The ability to take data, understand it, visualize it and extract useful information from it is becoming a hugely important skill. How can you turn all those logs, histories of purchases and trades or open government data, into useful information that help your business make money?
In this talk, we’ll look at doing data science using F#. The F# language is perfectly suited for this task – type providers integrate external data directly into the language – your language suddenly _understands_ CSV, XML, JSON, REST services and other sources. The interactive development style makes it easy to explore data and test your algorithms as you’re writing them. Rich set of libraries for working with data frames, time series and for visualization gives you all the tools you need. And finally – F# easily integrates with statistical environments like R and Matlab, giving you access to the industry standard libraries.
The ability to take data, understand it, visualize it and extract useful information from it is becoming a hugely important skill. How can you turn all those logs, histories of purchases and trades or open government data, into useful information that help your business make money?
In this talk, we’ll look at doing data science using F#. The F# language is perfectly suited for this task – type providers integrate external data directly into the language – your language suddenly _understands_ CSV, XML, JSON, REST services and other sources. The interactive development style makes it easy to explore data and test your algorithms as you’re writing them. Rich set of libraries for working with data frames, time series and for visualization gives you all the tools you need. And finally – F# easily integrates with statistical environments like R and Matlab, giving you access to the industry standard libraries.
Information-rich programming in F# (ML Workshop 2012)Tomas Petricek
We live in an information-rich world that provides huge opportunities for programmers to explore and create exciting applications. Traditionally, statically typed programming languages have not been aware of the data types that are implicitly available in the outer world such as the web, databases and semi-structured files. This tutorial presents the recent work on F# Type Providers - a language mechanism that enables a smooth integration of diverse data sources into an ML-style programming language. As a case study, we look at a type provider for accessing the world development indicators from the World Bank and we will discuss some intriguing research problems associated with mapping real-world data into an ML-style type system.
The ability to take data, understand it, visualize it and extract useful information from it is becoming a hugely important skill. How can you turn all those logs, histories of purchases and trades or open government data, into useful information that help your business make money?
In this talk, we’ll look at doing data science using F#. The F# language is perfectly suited for this task – type providers integrate external data directly into the language – your language suddenly _understands_ CSV, XML, JSON, REST services and other sources. The interactive development style makes it easy to explore data and test your algorithms as you’re writing them. Rich set of libraries for working with data frames, time series and for visualization gives you all the tools you need. And finally – F# easily integrates with statistical environments like R and Matlab, giving you access to the industry standard libraries.
The ability to take data, understand it, visualize it and extract useful information from it is becoming a hugely important skill. How can you turn all those logs, histories of purchases and trades or open government data, into useful information that help your business make money?
In this talk, we’ll look at doing data science using F#. The F# language is perfectly suited for this task – type providers integrate external data directly into the language – your language suddenly _understands_ CSV, XML, JSON, REST services and other sources. The interactive development style makes it easy to explore data and test your algorithms as you’re writing them. Rich set of libraries for working with data frames, time series and for visualization gives you all the tools you need. And finally – F# easily integrates with statistical environments like R and Matlab, giving you access to the industry standard libraries.
Information-rich programming in F# (ML Workshop 2012)Tomas Petricek
We live in an information-rich world that provides huge opportunities for programmers to explore and create exciting applications. Traditionally, statically typed programming languages have not been aware of the data types that are implicitly available in the outer world such as the web, databases and semi-structured files. This tutorial presents the recent work on F# Type Providers - a language mechanism that enables a smooth integration of diverse data sources into an ML-style programming language. As a case study, we look at a type provider for accessing the world development indicators from the World Bank and we will discuss some intriguing research problems associated with mapping real-world data into an ML-style type system.
This is a presentation that I prepared and delivered to students enrolled in the Bachelor's-level Information Studies programme offered by Charles Stur University.
For many libraries, an institutional repository is an online archive to collect, preserve, and make accessible the intellectual output of an institution. For a growing bloc, the goal is to go further, beyond knowledge preservation to knowledge creation. These libraries are using their repositories to provide faculty with a proven publishing option by facilitating the production and distribution of original content often too niche for traditional publishers.
How do metadata librarians sift the incoming metadata with these different goals in mind? How do they optimize content for discovery in a wide range of resources such as online catalogs, external research databases, and major search engines? For a library that is also providing publishing services, what additional steps are necessary?
As the provider of Digital Commons, a repository and publishing platform for over 350 institutions, bepress has first-hand experience with these topics, and our consultants advise regularly on best practices for collecting, publishing, distributing, and archiving content. This presentation is intended for library professionals, whether their goal is to collect previously published works or to go further into library-led publishing. After an overview of common sources and destinations for metadata, attendees will come away with a set of considerations for streamlining workflows and optimizing content for discovery and distribution in major venues.
Eli Windchy is the VP, Consulting Services at bepress which provides software and services to the scholarly community. She received a Master's in Archaeology from University of Virginia, taught organic gardening, and for the last ten years has also been getting dirty with the metadata of Digital Commons repositories. She co-directs courses in institutional repository management and publishing, and she enjoys addressing the challenges of interoperability and scholarly communication.
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
Towards knowledge maintenance in scientific digital libraries with the keysto...jodischneider
JCDL2020 full paper.
Abstract:
Scientific digital libraries speed dissemination of scientific publications, but also the propagation of invalid or unreliable knowledge. Although many papers with known validity problems are highly cited, no auditing process is currently available to determine whether a citing paper’s findings fundamentally depend on invalid or unreliable knowledge. To address this, we introduce a new framework, the keystone framework, designed to identify when and how citing unreliable findings impacts a paper, using argumentation theory and citation context analysis. Through two pilot case studies, we demonstrate how the keystone framework can be applied to knowledge maintenance tasks for digital libraries, including addressing citations of a non-reproducible paper and identifying statements most needing validation in a high-impact paper. We identify roles for librarians, database maintainers, knowledge base curators, and research software engineers in applying the framework to scientific digital libraries.
doi:10.1145/3383583.3398514
Preprint: http://jodischneider.com/pubs/jcdl2020.pdf
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
Print source literature 24 March 2023.pptxsanjaychavan62
Hi i am Dr. Sanjay Chavan, i am share my ppts on print source literature for newcomer researcher in chemistry who is seeking for research give idea about literature reviews and defiantly this power point presentation is very help full. before the research work learnt about the research methodology with respective subject is very essential so this is very effective information for the newly enter this field.
Collection evaluation techniques for academic libraries ALISS
Sally Halper, Lead Content Specialist - Business & Management, British Library. An excellent introduction to some really good practical qualitative and quantitative tools including White's brief tests. A bibliography of further readings is also provided.
Presentation given by Peter Burnhill, director of EDINA, at #ReCon_15 : Beyond the paper: publishing data, software and more. Edinburgh, 19 June 2015
Peter Burnhill
http://reconevent.com/
This is a presentation that I prepared and delivered to students enrolled in the Bachelor's-level Information Studies programme offered by Charles Stur University.
For many libraries, an institutional repository is an online archive to collect, preserve, and make accessible the intellectual output of an institution. For a growing bloc, the goal is to go further, beyond knowledge preservation to knowledge creation. These libraries are using their repositories to provide faculty with a proven publishing option by facilitating the production and distribution of original content often too niche for traditional publishers.
How do metadata librarians sift the incoming metadata with these different goals in mind? How do they optimize content for discovery in a wide range of resources such as online catalogs, external research databases, and major search engines? For a library that is also providing publishing services, what additional steps are necessary?
As the provider of Digital Commons, a repository and publishing platform for over 350 institutions, bepress has first-hand experience with these topics, and our consultants advise regularly on best practices for collecting, publishing, distributing, and archiving content. This presentation is intended for library professionals, whether their goal is to collect previously published works or to go further into library-led publishing. After an overview of common sources and destinations for metadata, attendees will come away with a set of considerations for streamlining workflows and optimizing content for discovery and distribution in major venues.
Eli Windchy is the VP, Consulting Services at bepress which provides software and services to the scholarly community. She received a Master's in Archaeology from University of Virginia, taught organic gardening, and for the last ten years has also been getting dirty with the metadata of Digital Commons repositories. She co-directs courses in institutional repository management and publishing, and she enjoys addressing the challenges of interoperability and scholarly communication.
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
Towards knowledge maintenance in scientific digital libraries with the keysto...jodischneider
JCDL2020 full paper.
Abstract:
Scientific digital libraries speed dissemination of scientific publications, but also the propagation of invalid or unreliable knowledge. Although many papers with known validity problems are highly cited, no auditing process is currently available to determine whether a citing paper’s findings fundamentally depend on invalid or unreliable knowledge. To address this, we introduce a new framework, the keystone framework, designed to identify when and how citing unreliable findings impacts a paper, using argumentation theory and citation context analysis. Through two pilot case studies, we demonstrate how the keystone framework can be applied to knowledge maintenance tasks for digital libraries, including addressing citations of a non-reproducible paper and identifying statements most needing validation in a high-impact paper. We identify roles for librarians, database maintainers, knowledge base curators, and research software engineers in applying the framework to scientific digital libraries.
doi:10.1145/3383583.3398514
Preprint: http://jodischneider.com/pubs/jcdl2020.pdf
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
Print source literature 24 March 2023.pptxsanjaychavan62
Hi i am Dr. Sanjay Chavan, i am share my ppts on print source literature for newcomer researcher in chemistry who is seeking for research give idea about literature reviews and defiantly this power point presentation is very help full. before the research work learnt about the research methodology with respective subject is very essential so this is very effective information for the newly enter this field.
Collection evaluation techniques for academic libraries ALISS
Sally Halper, Lead Content Specialist - Business & Management, British Library. An excellent introduction to some really good practical qualitative and quantitative tools including White's brief tests. A bibliography of further readings is also provided.
Presentation given by Peter Burnhill, director of EDINA, at #ReCon_15 : Beyond the paper: publishing data, software and more. Edinburgh, 19 June 2015
Peter Burnhill
http://reconevent.com/
Similar to The References of References: Enriching Library Catalogs via Domain-Specific Reference Mining (20)
Notes de bas de page: d’un outil savant aux hyperliensGiovanni Colavizza
Presentation pour l'exposition "De l'argile au nouage", Bibliotheques Mazarine et de Genève. 12/11/2015. http://institutions.ville-geneve.ch/fr/bge/actualites/actualites/expositions/archives-bastions/de-largile-au-nuage/.
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.
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
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/
BREEDING METHODS FOR DISEASE RESISTANCE.pptxRASHMI M G
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
3. Motivation - the Scholar
Issues: lack of data [Sula and Miller, 2014] leads to absence of
services: estimated coverage of Web of Science for Humanities
circa 13% [Mingers and Leydesdorff, 2015].
3
Sciences:
Google Scholar
English
mainly papers
Lower-cost information
gathering
Humanities:
no Google Scholar-like system
multiple languages
mainly monographs
Higher-cost information
gathering
4. Motivation - the Footnote
How humanists cite? Footnotes [see e.g. Hellqvist, 2009]
4
5. Motivation - the Archive
Approximately half citations to primary sources [Wiberley Jr., 2009]
5
7. Proposal: Enriching library catalogs
7
Use reference monographs, the “canon” of the
domain, to extract references to the rest of the
literature and enrich library catalogs.
8. Project: Linked Books
Focused on a case study/domain:
the history of Venice.
Partners so far:
• Ca’ Foscari University Library System
• Biblioteca Marciana
• Istituto Veneto di Scienze, Lettere ed Arti
• Archivio di Stato di Venezia
• EPFL
8
10. Corpus selection
10
Result: 1904 monographs, 701 with
a structured list of references.
Use the means of the library:
1- Consultation shelves
2- Dewey and subject classification
3- Scholarly bibliographies
4- Keyword search
19. Lookup
19
1. Against OPAC SBN (via API)
Steps:
1. search candidates by title
2. match reference metadata
3. assign each candidate a
confidence score
4. return set of candidates
Evaluation:
• 2k references (out of 181k)
• 41.7% no candidates
• 58.3% with candidates:
• 72.3% -> first candidate
correct
Goal: disambiguation of references
Issues:
• OCR errors -> impact on search by title (low recall)
• API as a “black box” + bottleneck of search by title
20. Lookup
20
2. Against metadata of digitized books
Lookup
Goal: verify cohesiveness of digitized corpus
Method:
• based on SBN lookup
• but lookup against digitization
metadata
• tuned to maximize precision
• returns 1 or no matches
Evaluation*:
• 500 references (out of 181k)
• precision ~ 1.00
• recall > 0.95
Result:
• only 7% of references extracted from 701 monographs point
inwards (i.e. towards the 1904 monographs)
21. 21
Core of the discipline
co-citation network from
extracted references*
giant component = 59%
of selected corpus
books in the giant
component -> core of
reference works on
history of Venice
giant component ->
32.5% with only works in
consultation
22. Conclusions and Outlook
22
data- and citation-driven approach to assess and
exploit, from an IR point of view, domain-specific
library holdings on the history of Venice
next big challenge: extraction, consolidation and
disambiguation of references contained within
footnotes (journals)