The document discusses Clio Infra, a digital research infrastructure developed by the International Institute of Social History to integrate historical databases and make datasets accessible for analysis and visualization. It describes Clio Infra's features for collaborative data curation on Dataverse platforms, standardized geocoding, and interactive visualization of datasets on historical maps through a data processing engine. The goal is to facilitate open data sharing, promote collaboration between researchers, and provide tools for descriptive analysis, filtering, and exporting data for further statistical analysis.
Python tool to data analysis and artificial intelligenceMd Aksam VK
This document provides an overview of Python tools for data analysis and artificial intelligence. It discusses various data types and structures in Python like lists, tuples, and dictionaries. It also covers data exploration techniques including reading files, handling different variable types, and data visualization. Machine learning algorithms like label encoding, logistic regression, naive bayes classification, k-means clustering, and support vector machines are explained. Code files with examples of implementing these techniques in Python are referenced.
Exploration and 3D GIS Software - MapInfo Professional Discover3D 2015Prakher Hajela Saxena
Pitney Bowes Software provides natural resource management solutions including GIS software for mineral exploration, mining, oil and gas, and forestry industries. Their solutions help users discover, evaluate, develop and manage natural assets. Their portfolio includes MapInfo Pro, Discover3D, and Engage3D Pro which provide spatial analysis, 3D modeling, and visualization capabilities for evaluating natural resource assets.
The document summarizes Prague's OpenData project which aims to make city data transparent and available in open formats. It discusses the goals of providing citywide OpenData and the organizations involved. It also lists some of the initial datasets that have been made available through the project, including data on Prague districts, education, housing, environment, sports, transportation, and libraries. The datasets are available in formats like CSV, XLSX, PDF, and through APIs on a city server.
This document discusses and compares software for qualitative and quantitative data analysis. It introduces Computer Assisted Qualitative Data Analysis Software (CAQDAS) which was developed in 1991 to aid in coding, searching, memoing, and grouping codes and documents for qualitative analysis. Popular CAQDAS include NVivo, MAXQDA, and ATLAS.ti. It also discusses statistical software packages for quantitative analysis like SPSS, SAS, and R which can be used for tasks like data mining, statistical analysis, forecasting, and more. SPSS and SAS require purchase while R is open source and free to download.
DSD-INT 2016 (Open) Data science in SWITCH-ON - BootDeltares
Presentation by Gerben Boot (Deltares) at Earth Observation and Data Science Symposium, during Delft Software Days 2016. Monday 24 October 2016, Delft.
Process and steps that are followed in creation of successful visualization. Taking an example of Encyclopaedia of life data and tableu visualization prototype
Big Graph : Tools, Techniques, Issues, Challenges and Future Directions csandit
Analyzing interconnection structures among the data through the use of graph algorithms and
graph analytics has been shown to provide tremendous value in many application domains (like
social networks, protein networks, transportation networks, bibliographical networks,
knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions of
edges have become very common. In principle, graph analytics is an important big data
discovery technique. Therefore, with the increasing abundance of large scale graphs, designing
scalable systems for processing and analyzing large scale graphs has become one of the
timeliest problems facing the big data research community. In general, distributed processing of
big graphs is a challenging task due to their size and the inherent irregular structure of graph
computations. In this paper, we present a comprehensive overview of the state-of-the-art to
better understand the challenges of developing very high-scalable graph processing systems. In
addition, we identify a set of the current open research challenges and discuss some promising
directions for future research.
Creating and Utilizing Linked Open Statistical Data for the Development of Ad...Evangelos Kalampokis
The document discusses the OpenCube approach for working with linked data cubes. OpenCube develops components to support the full lifecycle of linked statistical data, from publishing raw data cubes to consuming them through analytics, visualizations, and other applications. It presents several components that have been implemented, including tools for publishing statistical data in various formats, browsing and visualizing data cubes, and integrating with R for advanced analytics. Initial evaluations of the components have provided insights around publishing and working with large linked statistical datasets.
Python tool to data analysis and artificial intelligenceMd Aksam VK
This document provides an overview of Python tools for data analysis and artificial intelligence. It discusses various data types and structures in Python like lists, tuples, and dictionaries. It also covers data exploration techniques including reading files, handling different variable types, and data visualization. Machine learning algorithms like label encoding, logistic regression, naive bayes classification, k-means clustering, and support vector machines are explained. Code files with examples of implementing these techniques in Python are referenced.
Exploration and 3D GIS Software - MapInfo Professional Discover3D 2015Prakher Hajela Saxena
Pitney Bowes Software provides natural resource management solutions including GIS software for mineral exploration, mining, oil and gas, and forestry industries. Their solutions help users discover, evaluate, develop and manage natural assets. Their portfolio includes MapInfo Pro, Discover3D, and Engage3D Pro which provide spatial analysis, 3D modeling, and visualization capabilities for evaluating natural resource assets.
The document summarizes Prague's OpenData project which aims to make city data transparent and available in open formats. It discusses the goals of providing citywide OpenData and the organizations involved. It also lists some of the initial datasets that have been made available through the project, including data on Prague districts, education, housing, environment, sports, transportation, and libraries. The datasets are available in formats like CSV, XLSX, PDF, and through APIs on a city server.
This document discusses and compares software for qualitative and quantitative data analysis. It introduces Computer Assisted Qualitative Data Analysis Software (CAQDAS) which was developed in 1991 to aid in coding, searching, memoing, and grouping codes and documents for qualitative analysis. Popular CAQDAS include NVivo, MAXQDA, and ATLAS.ti. It also discusses statistical software packages for quantitative analysis like SPSS, SAS, and R which can be used for tasks like data mining, statistical analysis, forecasting, and more. SPSS and SAS require purchase while R is open source and free to download.
DSD-INT 2016 (Open) Data science in SWITCH-ON - BootDeltares
Presentation by Gerben Boot (Deltares) at Earth Observation and Data Science Symposium, during Delft Software Days 2016. Monday 24 October 2016, Delft.
Process and steps that are followed in creation of successful visualization. Taking an example of Encyclopaedia of life data and tableu visualization prototype
Big Graph : Tools, Techniques, Issues, Challenges and Future Directions csandit
Analyzing interconnection structures among the data through the use of graph algorithms and
graph analytics has been shown to provide tremendous value in many application domains (like
social networks, protein networks, transportation networks, bibliographical networks,
knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions of
edges have become very common. In principle, graph analytics is an important big data
discovery technique. Therefore, with the increasing abundance of large scale graphs, designing
scalable systems for processing and analyzing large scale graphs has become one of the
timeliest problems facing the big data research community. In general, distributed processing of
big graphs is a challenging task due to their size and the inherent irregular structure of graph
computations. In this paper, we present a comprehensive overview of the state-of-the-art to
better understand the challenges of developing very high-scalable graph processing systems. In
addition, we identify a set of the current open research challenges and discuss some promising
directions for future research.
Creating and Utilizing Linked Open Statistical Data for the Development of Ad...Evangelos Kalampokis
The document discusses the OpenCube approach for working with linked data cubes. OpenCube develops components to support the full lifecycle of linked statistical data, from publishing raw data cubes to consuming them through analytics, visualizations, and other applications. It presents several components that have been implemented, including tools for publishing statistical data in various formats, browsing and visualizing data cubes, and integrating with R for advanced analytics. Initial evaluations of the components have provided insights around publishing and working with large linked statistical datasets.
The document outlines the goals and timeline of a project to develop a system to help digitize and interpret archaeological findings. The project aims to 1) develop tools to document archaeological findings using mobile devices, 2) digitize existing catalogues of artifacts, 3) provide on-site support for interpreting findings, 4) archive all collected data through web-accessible repositories, and 5) ensure long-term access and reuse of open data. It will involve developing technologies for digitizing paper records, populating a centralized database, enabling similarity searches of artifacts, and testing mobile and web-based tools with users over its 2018-2019 timeline.
R is used to create many types of internal and external products at Gravity-AOL, including one-off analyses, automated reports, internal R packages, internal dashboards, a customer-facing web app, and an analytical backend service. Managing an R environment at a company involves challenges like library and version management, hardware specifications, and non-R dependencies. R is well-suited for these products and tasks due to its rich analytical libraries, end-to-end functionality, rapid prototyping capabilities, and stability as a platform.
SPARC is an open source, cloud-based GIS data platform designed to support scenario planning for the Seven50 initiative. It allows for uploading and integrating data from various sources, automatic quality checking of data, and making data accessible to various tools through common templates. SPARC also provides access to contextual regional data organized into categories like land use, transportation, and demographics. It stores scenario planning data used to build future scenarios according to a common schema. Access is managed through user registration and different user classes have different access levels to the data.
Graphalytics: A big data benchmark for graph-processing platformsGraph-TA
Graphalytics is a benchmark for evaluating graph processing platforms. It includes a diverse set of algorithms and synthetic and real-world datasets. The benchmark harness collects performance metrics across platforms and enables in-depth bottleneck analysis through Granula. Graphalytics aims to enable fair comparison of different graph systems and help identify areas for improvement through a modern software development process.
The document describes a workshop on creating, expanding, and exploiting linked open statistical data. It discusses data cubes on the web and the RDF Data Cube Vocabulary for representing statistical data. The OpenCube toolkit is presented, including an OLAP browser for performing operations on integrated linked data cubes and a map view. The toolkit was deployed using Flemish government statistical data. The browser allows selecting dimensions and measures, drilling down, and expanding cubes by merging compatible sources. Challenges in combining disparate cubes were also discussed.
A Survey on Data Mapping Strategy for data stored in the storage cloud 111NavNeet KuMar
This document describes a method for processing large amounts of data stored in cloud storage using Hadoop clusters. Data is uploaded to cloud storage by users and then processed using MapReduce on Hadoop clusters. The method involves storing data in the cloud for processing and then running MapReduce algorithms on Hadoop clusters to analyze the data in parallel. The results are then stored back in the cloud for users to download. An architecture is proposed involving a controller that directs requests to Hadoop masters which coordinate nodes to perform mapping and reducing of data according to the algorithm implemented.
This presentation discusses bridging research and collections at the International Institute of Social History (IISH). It outlines the IISH's mission to conduct historical research and collect related data to make it available to other researchers. It describes the data and target groups at the IISH. It then discusses requirements and methodologies for historical research, data collecting, and software development to support researchers and collectors. Finally, it demonstrates potential solutions like the Evergreen library system and Clio Infra and HiTIME tools to help analyze text and link data.
The HiTiME project aims to develop a system that can recognize entities like people, organizations, locations, dates and professions in historical text documents. The system splits documents into words, recognizes entities using named entity recognition and stores the output in a database. It also aims to integrate with other systems at the International Institute of Social History to improve search, metadata and visualization of historical data. Some planned improvements include using additional natural language processing tools, disambiguating entities, recognizing composite entities, and integrating with applications like the Basic Word Sequence Analysis tool.
This document discusses APIs and the API economy. It defines an API as an interface between software systems that allows them to interact. APIs provide programmatic access to systems and processes within organizations. Building APIs improves digital ecosystems by enabling data sharing, reuse, and rapid prototyping. The document advocates for an open innovation approach where organizations use both internal and external knowledge through APIs to accelerate innovation. It presents a vision of APIs managing complex systems and data as products that fuel collaboration across communities.
- Dataverse is an open source data repository platform that aims to make research data findable, accessible, interoperable, and reusable (FAIR).
- It has features like persistent identifiers, metadata enrichment, and APIs that help promote and disseminate datasets through search engines and other tools to increase citations and visibility.
- The DataverseNL instance integrates with services from CLARIAH.nl and DANS EASY to ensure datasets are preserved and meet standards for interoperability.
Presentation on integration of data analysis tools with historical maps visualization and charts in Clio Infra Collaboration platform based on Dataverse system.
DANS is a institute of KNAW and NWO that provides open data repository and analysis services through its DataverseNL platform. DataverseNL allows researchers to share, archive, and analyze research data. It provides features such as persistent identifiers, metadata harvesting, and tools for visualization, statistics, and linking datasets. DataverseNL aims to make data Findable, Accessible, Interoperable and Re-usable in accordance with FAIR data principles. It can also integrate with other data archives and repositories to preserve and provide access to research data.
New tools are being developed by Czech Living Lab WirelessInfo, which allow users to easily publish their data and metadata as part of a Spatial Data Infrastructure (SDI). The paper describes the design of a Technological Infrastructure on the basis of ISO and OGC Standards and also the implementation of a prototype and first experiences. The solution is designed in distributed system form, which provides the connection to metadata about spatial data and services. This solution tests the principle of catalogue services at both national and international level which could be used in the UN SDI context. A catalogue portal is one of the independent components of GeoHosting complex system for raster and vector spatial data sharing. The catalogue portal provides data source searching on the basis of their metadata records through structure queries. The portal also contains edit functionality for new metadata records creating or editing. The metadata catalogue system corresponds to ISO 19115/19119/19139 standards [1], [2], [3], [4] and provides for cascade searching on the other standardized catalogue systems. The difference is, there exist different other initiatives offering publishing of own content like Google technology or OpenStreet Map, that GeoHosting is based fully on INSPIRE European standards and support establishing of network of distributed servers.
The document summarizes an ENGAGE workshop discussing barriers to open data reuse in Europe and findings from the ENGAGE project. Key points include:
1) Metadata was identified as a major barrier, with needs for a rich format to facilitate discovery and addressing issues like multilinguality, data ownership, and formats.
2) ENGAGE aims to provide a single access point and tools for researchers and citizens to discover, browse, download, visualize, and submit diverse public sector data sources.
3) ENGAGE 2.0 adds additional functionalities like dataset extension, conversion, cleansing, and crowdsourcing derived datasets while maintaining provenance information.
This document proposes a project to add reporting capabilities to the Polaris open source project. The project would develop a web interface to visualize anomalies detected in spacecraft telemetry data through interactive graphs. It would also generate PDF reports and allow exporting the graphs. The proposed workflow is to first understand the existing anomaly detector, then build a new "Polaris Detect" module including a React-based web interface showing time-series graphs of parameter values and outliers. The interface would allow selecting parameters and generating/exporting reports from the anomaly analysis. Milestones include implementing the command line system, web interface with interactive charts, and PDF/export functionality.
The document summarizes a project that published linked geospatial data from several pan-European datasets. The project transformed datasets into RDF, loaded them into Virtuoso, and linked the datasets. This resulted in a Virtuoso instance with over 700 million triples. The project also developed three interfaces for navigating and visualizing the data: a SPARQL endpoint, faceted search browser, and map visualization. The linked data can be reused for applications in areas like real estate, tourism, and agriculture.
This document proposes extending the Polaris project to generate analysis reports from spacecraft telemetry data. The proposed additions include:
1. Developing a web interface to visualize anomalies detected in telemetry parameters over time through interactive graphs.
2. Creating a PDF generator to output graph summaries and anomalies.
3. Adding an option to export all graphs in a zip file for further use.
The goal is to help analysts efficiently study anomalies by generating customizable reports from Polaris results. The proposed workflow and timeline lay out developing an anomaly detection module, interactive visualization tools, and report generation capabilities over 3 months.
A Comprehensive Guide to Data Science Technologies.pdfGeethaPratyusha
In the fast-paced realm of data science, staying ahead requires a deep understanding of the tools and technologies that drive insights from data. From programming languages to advanced frameworks, the world of data science technologies is vast and dynamic. In this blog, we embark on a comprehensive guide, navigating through the essential tools that empower data scientists to unravel the mysteries hidden within datasets and shape the future of information analysis. For those seeking a structured and immersive learning experience, complementing this tech-centric journey with a well-crafted data science course is the key to unlocking boundless opportunities in this evolving field.
The document outlines the goals and timeline of a project to develop a system to help digitize and interpret archaeological findings. The project aims to 1) develop tools to document archaeological findings using mobile devices, 2) digitize existing catalogues of artifacts, 3) provide on-site support for interpreting findings, 4) archive all collected data through web-accessible repositories, and 5) ensure long-term access and reuse of open data. It will involve developing technologies for digitizing paper records, populating a centralized database, enabling similarity searches of artifacts, and testing mobile and web-based tools with users over its 2018-2019 timeline.
R is used to create many types of internal and external products at Gravity-AOL, including one-off analyses, automated reports, internal R packages, internal dashboards, a customer-facing web app, and an analytical backend service. Managing an R environment at a company involves challenges like library and version management, hardware specifications, and non-R dependencies. R is well-suited for these products and tasks due to its rich analytical libraries, end-to-end functionality, rapid prototyping capabilities, and stability as a platform.
SPARC is an open source, cloud-based GIS data platform designed to support scenario planning for the Seven50 initiative. It allows for uploading and integrating data from various sources, automatic quality checking of data, and making data accessible to various tools through common templates. SPARC also provides access to contextual regional data organized into categories like land use, transportation, and demographics. It stores scenario planning data used to build future scenarios according to a common schema. Access is managed through user registration and different user classes have different access levels to the data.
Graphalytics: A big data benchmark for graph-processing platformsGraph-TA
Graphalytics is a benchmark for evaluating graph processing platforms. It includes a diverse set of algorithms and synthetic and real-world datasets. The benchmark harness collects performance metrics across platforms and enables in-depth bottleneck analysis through Granula. Graphalytics aims to enable fair comparison of different graph systems and help identify areas for improvement through a modern software development process.
The document describes a workshop on creating, expanding, and exploiting linked open statistical data. It discusses data cubes on the web and the RDF Data Cube Vocabulary for representing statistical data. The OpenCube toolkit is presented, including an OLAP browser for performing operations on integrated linked data cubes and a map view. The toolkit was deployed using Flemish government statistical data. The browser allows selecting dimensions and measures, drilling down, and expanding cubes by merging compatible sources. Challenges in combining disparate cubes were also discussed.
A Survey on Data Mapping Strategy for data stored in the storage cloud 111NavNeet KuMar
This document describes a method for processing large amounts of data stored in cloud storage using Hadoop clusters. Data is uploaded to cloud storage by users and then processed using MapReduce on Hadoop clusters. The method involves storing data in the cloud for processing and then running MapReduce algorithms on Hadoop clusters to analyze the data in parallel. The results are then stored back in the cloud for users to download. An architecture is proposed involving a controller that directs requests to Hadoop masters which coordinate nodes to perform mapping and reducing of data according to the algorithm implemented.
This presentation discusses bridging research and collections at the International Institute of Social History (IISH). It outlines the IISH's mission to conduct historical research and collect related data to make it available to other researchers. It describes the data and target groups at the IISH. It then discusses requirements and methodologies for historical research, data collecting, and software development to support researchers and collectors. Finally, it demonstrates potential solutions like the Evergreen library system and Clio Infra and HiTIME tools to help analyze text and link data.
The HiTiME project aims to develop a system that can recognize entities like people, organizations, locations, dates and professions in historical text documents. The system splits documents into words, recognizes entities using named entity recognition and stores the output in a database. It also aims to integrate with other systems at the International Institute of Social History to improve search, metadata and visualization of historical data. Some planned improvements include using additional natural language processing tools, disambiguating entities, recognizing composite entities, and integrating with applications like the Basic Word Sequence Analysis tool.
This document discusses APIs and the API economy. It defines an API as an interface between software systems that allows them to interact. APIs provide programmatic access to systems and processes within organizations. Building APIs improves digital ecosystems by enabling data sharing, reuse, and rapid prototyping. The document advocates for an open innovation approach where organizations use both internal and external knowledge through APIs to accelerate innovation. It presents a vision of APIs managing complex systems and data as products that fuel collaboration across communities.
- Dataverse is an open source data repository platform that aims to make research data findable, accessible, interoperable, and reusable (FAIR).
- It has features like persistent identifiers, metadata enrichment, and APIs that help promote and disseminate datasets through search engines and other tools to increase citations and visibility.
- The DataverseNL instance integrates with services from CLARIAH.nl and DANS EASY to ensure datasets are preserved and meet standards for interoperability.
Presentation on integration of data analysis tools with historical maps visualization and charts in Clio Infra Collaboration platform based on Dataverse system.
DANS is a institute of KNAW and NWO that provides open data repository and analysis services through its DataverseNL platform. DataverseNL allows researchers to share, archive, and analyze research data. It provides features such as persistent identifiers, metadata harvesting, and tools for visualization, statistics, and linking datasets. DataverseNL aims to make data Findable, Accessible, Interoperable and Re-usable in accordance with FAIR data principles. It can also integrate with other data archives and repositories to preserve and provide access to research data.
New tools are being developed by Czech Living Lab WirelessInfo, which allow users to easily publish their data and metadata as part of a Spatial Data Infrastructure (SDI). The paper describes the design of a Technological Infrastructure on the basis of ISO and OGC Standards and also the implementation of a prototype and first experiences. The solution is designed in distributed system form, which provides the connection to metadata about spatial data and services. This solution tests the principle of catalogue services at both national and international level which could be used in the UN SDI context. A catalogue portal is one of the independent components of GeoHosting complex system for raster and vector spatial data sharing. The catalogue portal provides data source searching on the basis of their metadata records through structure queries. The portal also contains edit functionality for new metadata records creating or editing. The metadata catalogue system corresponds to ISO 19115/19119/19139 standards [1], [2], [3], [4] and provides for cascade searching on the other standardized catalogue systems. The difference is, there exist different other initiatives offering publishing of own content like Google technology or OpenStreet Map, that GeoHosting is based fully on INSPIRE European standards and support establishing of network of distributed servers.
The document summarizes an ENGAGE workshop discussing barriers to open data reuse in Europe and findings from the ENGAGE project. Key points include:
1) Metadata was identified as a major barrier, with needs for a rich format to facilitate discovery and addressing issues like multilinguality, data ownership, and formats.
2) ENGAGE aims to provide a single access point and tools for researchers and citizens to discover, browse, download, visualize, and submit diverse public sector data sources.
3) ENGAGE 2.0 adds additional functionalities like dataset extension, conversion, cleansing, and crowdsourcing derived datasets while maintaining provenance information.
This document proposes a project to add reporting capabilities to the Polaris open source project. The project would develop a web interface to visualize anomalies detected in spacecraft telemetry data through interactive graphs. It would also generate PDF reports and allow exporting the graphs. The proposed workflow is to first understand the existing anomaly detector, then build a new "Polaris Detect" module including a React-based web interface showing time-series graphs of parameter values and outliers. The interface would allow selecting parameters and generating/exporting reports from the anomaly analysis. Milestones include implementing the command line system, web interface with interactive charts, and PDF/export functionality.
The document summarizes a project that published linked geospatial data from several pan-European datasets. The project transformed datasets into RDF, loaded them into Virtuoso, and linked the datasets. This resulted in a Virtuoso instance with over 700 million triples. The project also developed three interfaces for navigating and visualizing the data: a SPARQL endpoint, faceted search browser, and map visualization. The linked data can be reused for applications in areas like real estate, tourism, and agriculture.
This document proposes extending the Polaris project to generate analysis reports from spacecraft telemetry data. The proposed additions include:
1. Developing a web interface to visualize anomalies detected in telemetry parameters over time through interactive graphs.
2. Creating a PDF generator to output graph summaries and anomalies.
3. Adding an option to export all graphs in a zip file for further use.
The goal is to help analysts efficiently study anomalies by generating customizable reports from Polaris results. The proposed workflow and timeline lay out developing an anomaly detection module, interactive visualization tools, and report generation capabilities over 3 months.
A Comprehensive Guide to Data Science Technologies.pdfGeethaPratyusha
In the fast-paced realm of data science, staying ahead requires a deep understanding of the tools and technologies that drive insights from data. From programming languages to advanced frameworks, the world of data science technologies is vast and dynamic. In this blog, we embark on a comprehensive guide, navigating through the essential tools that empower data scientists to unravel the mysteries hidden within datasets and shape the future of information analysis. For those seeking a structured and immersive learning experience, complementing this tech-centric journey with a well-crafted data science course is the key to unlocking boundless opportunities in this evolving field.
The document discusses the Elsevier Executable Papers Challenge which aims to develop models for publishing computational science papers that are executable. It provides an overview of several finalist submissions that developed platforms and environments for creating executable papers, including SHARE which hosts virtual machines for paper submissions and A-R-E which supports the full paper lifecycle from authoring to publication. The document advocates for the idea of executable journals where submitted papers include working code that can be executed on a shared platform and remain available for other papers to build upon, clearly communicating methods and reducing duplication of work.
Overview of the world of geospatial metadata, and the role of the EDINA service GoGeo in creating, saving, and discovering it. Presented on 19 June 2014 by Tony Mathys in Aberdeen, Scotland.
Lecture at an event "SEEDS Kick-off meeting", FORS, Lausanne, Switzerland.
Related materials: http://www.snf.ch/en/funding/programmes/scopes/Pages/default.aspx
http://seedsproject.ch/?page_id=368
The global need to securely derive (instant) insights, have motivated data architectures from distributed storage, to data lakes, data warehouses and lake-houses. In this talk we describe Tag.bio, a next generation data mesh platform that embeds vital elements such as domain centricity/ownership, Data as Products, Self-serve architecture, with a federated computational layer. Tag.bio data products combine data sets, smart APIs, statistical and machine learning algorithms into decentralized data products for users to discover insights using FAIR Principles. Researchers can use its point and click (no-code) system to instantly perform analysis and share versioned, reproducible results. The platform combines a dynamic cohort builder with analysis protocols and applications (low-code) to drive complex analysis workflows. Applications within data products are fully customizable via R and Python plugins (pro-code), and the platform supports notebook based developer environments with individual workspaces.
Join us for a talk/demo session on Tag.bio data mesh platform and learn how major pharma industries and university health systems are using this technology to promote value based healthcare, precision healthcare, find cures for disease, and promote collaboration (without explicitly moving data around). The talk also outlines Tag.bio secure data exchange features for real world evidence datasets, privacy centric data products (confidential computing) as well as integration with cloud services
DSpace-CRIS: a CRIS enhanced repository platformAndrea Bollini
International Conference on Economics and Business Information 19 to 20 April 2016 in Berlin
This presentation introduces you to the version 5.5.0 of the DSpace-CRIS extension. With such extension you can capture the full picture of the research activities conduct in your institution and their context. It enables to showcase the experts, the facilities, the services and much more to attract funding, facilitate collaborations and curate the scientific reputation of your Institution.
T.E.R:R.A.I.N. is a system that allows community groups to digitize their resources and store spatial data in an online repository. It was created to help community groups: 1) map local data that is unique to the community and not available elsewhere, and 2) enter data online and link to other applications. A pilot project involved developing features for an open-source digital repository software used to map plants in Pukekura Park with data entered by students and the public. The system is designed to be free, open source software that is customizable, easy to install and use, with support from a large developer community.
BDE SC6-ws-05/12/2016 technology part - SWCBigData_Europe
The document discusses a pilot project within the Big Data Europe program to create an online dashboard of economic data from municipal budgets. The project aims to aggregate budget and spending data from multiple sources and formats, normalize it using RDF, and analyze and visualize the data to provide insights for citizens, researchers, and decision makers. Technical components used include Apache Flume, Kafka, Spark, HDFS, Virtuoso triplestore, and D3 for visualization. An initial version has been implemented and will be evaluated with municipalities and other stakeholders.
Towards Digital Twin standards following an open source approachFIWARE
Digital Twins are gaining momentum when designing smart solutions in different application domains. However, there is a lack of open standards that warrant interoperability and portability of solutions, avoiding vendor lock-in.
During the presentation, we will review major developments in this area, focused on the adoption of a standard API for accessing Digital Twin Data and Smart Data Models. We will review how a Digital Twin approach enables data integration at different levels: architecting vertical smart solutions, within smart organizations and across organizations. At all levels interfacing with IoT, BigData, AI/ML, Blockchain, or Robotics technologies.
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Building an Operating System for Open Science: data integration challenges, Dataverse data repository and knowledge graphs. Lecture by Slava Tykhonov, DANS-KNAW, for the Journées Scientifiques de Rochebrune 2023 (JSR'23).
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Slides of the presentation for CLARIAH community on the ideas how to make controlled vocabularies sustainable and FAIR (Findable, Accessible, Interoperable, Reusable) with the help of Decentralized Identifiers (DIDs).
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The Covid-19 Museum has an ambition to create a platform to deposit, consult, aggregate and study heterogeneous data about the pandemics using features of a distributed web service. To achieve this purpose, Dataverse has been selected as a reliable FAIR data repository with built-in search engine and functionality that allows adding computing resources to explore archived resources both on data and metadata. Presentation by
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Data analysis in dataverse & visualization of datasets on historical maps
1. CLIO INFRA
Data analysis in Dataverse & visualization
of datasets on historical maps
Dataverse Community meeting 2015,
June 11, Harvard University
Vyacheslav Tykhonov Richard Zijdeman Jerry de Vries
International Institute of Social History
2. Introduction
The International Institute of Social History (IISH) collects information in the field of social
history and makes it available to the public. The IISH is one of the major scientific heritage
institutions in the Netherlands. In the field of socio-economic historical research IISH plays a
prominent role.
CLIO INFRA is Digital Research Infrastructure for the Arts and Humanities developed to
integrate a number of databases (hubs) consisting of data on global social, economic and
institutional indicators over the past five centuries, with special attention for the past 200
years. Clio Infra developed by IISH.
3. Our mission
Clio Infra is the bridge between Data Collections and Research Datasets.
“Old school” researchers time:
Collecting datasets in CSV, Access or Excel and other formats mostly on his own without real collaboration
between all researchers in the same field. Very closed model.
Digital Humanities era:
Young researchers use various computer tools to collect, analyze and combine research datasets they’ve
got from older generation of researchers. They can contribute and help each other and build communities.
The future:
The goal is to build tools to get datasets from “old school” researchers, standardize and store data in the
Clouds in order provide access to Open Data for researchers all over the world and made possible
collaboration between them.
4. Clio Infra Collaboration platform
Clio Infra functionality based on the Dataverse solution:
- teams collaboratively can curate, share and analyze research datasets
- teams members can share the responsibility to collect data on specific variables (for
example, countries) and inform each other about changes and additions
- dataset version control system is able to track changes in datasets
- other researchers can download their own copy of the data if dataset is published as Open
Data
Dataverse is flexible metadata store (repository) that connected with Research datasets
storage by our Data Processing Engine (DPE)
5. Added Value for future Researchers
The benefits of data sharing can be classified in terms of Metadata and Data access and
sharing (Collection tools) and Statistical Analysis and Data Mining (Research tools):
● access to a specific case study, citing and finding data
● access to the universe of data from Dataverse network that can organize and display
them for browsing and searching
● data filtering: researchers with proper authorization can obtain the subset of data
provided by data collector
● data analysis to run descriptive statistics and graphics, visualization, plotting on
historical maps
● Data APIs to export data for further analysis by popular statistical packages (STATA,
SPSS, R, iPython Notebook) and advanced data mining tools that will be developed in
the future (always up-to-date solution)
6. Collaboration possibilities
Descriptive metadata:
- dataset file
- documentation
- standardized tables with codes can be part of dataset
Sharing and collaboration capabilities:
- requesting unique API token by every researcher - user of Dataverse
- exchanging API token between researchers and granting permissions to work on the
same datasets as a team using data analysis tools
7. Collaboration Data Workflow
- Draft Datasets are visible only for owners
- with API tokens researchers can get access to interactive dashboard to get some
insights about the data stored in the Dataverse
- every dataset converted to dataframe
- dashboard can provide access to all variables from the dataframe and visualize them
on charts, graphs, historical maps and treemaps
After dataset is prepared to go public, it can be published in Collabs:
- guest users can download the copy of dataset
- team members with permissions and authorized API token can contribute to dataset
8. Data Analysis in Clio Infra
● Data Processing Engine has python core and developed for nlgis.nl (Netherlands
Geographic Information System) and distributed as widget
● interactive data exploration dashboard based on D3 library
● every dataset from Dataverse is available as Data API (json) and can be connected to
any statistical package
● filtering the data on specific years and variables based on dataframes (pandas, python
for data analysis)
● data quality check is the quick visual tool to apply Benford’s law for all values from
specific dataset
● dataframes can be open by researchers in various statistical packages like iPython
notebook, R Studio, SPSS, STATA, Mathlab, etc
9. Data Processing Engine (DPE) specification
● can split values from any dataset in number of categories specified by researcher (8 by
default)
● algorithm to categorize data values in proper categories can be selected manually
(percentile by default)
● can define maximum possible categories for specific dataset if there is no way to get
categories number specified by user of the system (for example, if there are 2-3
categories of data values)
● data ranges should be defined to get possibility to visualize data on some chart or map
in the right scale
● colors can be specified by user (Color Brewing, see http://colorbrewer2.org)
● legend generated and attached to all visualizations automatically
● values with missing data shown as 'no data' regions on map
● all data values delivered by Data API to make the data analysis platform independent
and communicate with other systems or statistical packages
10. API Service (Data API)
Data API provided by Data Processing Engine is the most important functionality for the well
equipped digital infrastructure:
● easy way to analyze data in popular statistical packages (STATA, SPSS, Excel)
● use common data science programming languages like Python, R to perform more
advanced research using external Data Science libraries
● analyze data with toolboxes like Wolfram|Alpha and other Discovery Platforms (added
value for the future)
● suitable for other researchers and developers to use advanced technique and data
mining tools that aren’t developed yet
11. Example of output from Data API
● every dataset ingested by DPE available as Data API with unique handler
● API can be filtered by variables extracted from the content of data file
Example:
/api/data?&handle=F16UDU:30:31&countrycode=USA&year=1880&categories=8&datarange=calculate
"United States of America": {
"code": "F16UDU30_31",
"color": "#FF7F00",
"countrycode": "USA",
"id": "2085",
"indicator": "Total Urban Population",
"intcode": "840",
"r": 923.99,
"region": "W. Offshoots",
"units": "x 1000",
"value": 14264.0,
"year": 1880
}
}
12. Data visualization and plotting data on historical maps
● Data Processing Engine (DPE) is the core of data visualization process and connected
to geoservice
● data attributes like scales and colors calculated by DPE on the fly based on the input of
researcher (for example, number of categories to split data) and the part of Data API
● histograms, cross tabulations, enhanced descriptive statistics based on pandas
dataframes
● visualization of datasets on historical maps will be available to plot data on maps for
last 500 years
13. Historical maps services: Geoservice and Geocoder
Delivered by Webmapper.nl and integrated with common Clio Infra infrastructure.
Geoservice basic requirements:
- should provide actual GeoAPI with historical polygons for all countries and regions
based on standardized codes
- available as geojson/topojson for online visualization
- QGIS toolbox should be supported to upload new maps and update old maps as shape
files
Geocoder should be able to standardize all geographical locations in different datasets:
- USSR and Soviet Union should be recognized as one country with the same PID
- Germany before and after 1990
- Indonesia before and after 1999
14. GeoAPI example
Geoservice can provide polygons for specific years on the national or world level rendered
as topojson or geojson.
GeoAPI:
/api/maps?world=on&year=1962
Polygons for all countries will be delivered as topojson:
arcs":[[1782,2186]]}]}},"arcs":[[[8387,6231],[0,5],[1,1],[1,-1],[2,0],[2,-1],[3,-4],[1,
-3],[0,-1],[-1,-5],[0,-1],[-1,2],[0,1],[-2,2],[-3,1],[-1,0],[-1,0],[-1,3],[0,1]],
[[8390,6247],[1,1],[0,1],[2,1],[1,0],[1,-2],[-1,-5],[-1,0],[-1,1],[-1,1],[-1,1],[0,1]],
[[8391,6204],[0,2],[-1,1],[-1,-1],[0,1],[0,1],[1,3],[1,0],[2,-6],[0,-1],[0,-1],[0,-1],
[-1,1],[-1,1]],[[8364,6093],[0,2],[2,5],[1,0],[1,-2],[-1,-6],[-1,-3],[-1,0],[-1,0],[0,1],
[0,3]],[[5941,6575],[0,-1],[-1,0],[-1,1],[0,1],[-1,0],[-1,0],[-1,0],[-1,0],[0,-1],[-1,-1],
[0,-2],[0,-1],[0,-2],[0,-1],[-1,-3],[-3,-2],[-4,-4],[-1,-1],[-1,0],[-2,-1],[-2,0],[-1,0],[-1,
-1],[-1,-2],[-5,1],[-1,0],[-1,-1],[-1,-1],[-1,0],[-2,1],[-4,3],[-1,1],[-1,2],[-2,8],[-1,4],
[-1,9],[0,1],[0,2],[0,1],[1,0],[0,-1],[0,-1],[1,-1],[0,-1],[1,0]
15. Integration of Dataverse, DPE, geoservice and geocoder in 6 steps
1. Data API communicates with geocoder to find standardized codes for all locations in
dataset
2. the same geocode with associated polygons is coming from geoservice
3. data and geoservice matching in the frontend by any geospatial javascript library
4. attributes like colors and scales filling the map polygons
5. legend with scales, values and colors coming from DPE to make map look complete
6. notes, sources and other metadata extracting from dataverse API to provide clear
explanation of generated historical map
Live demo for Labor Conflicts in 100 years
16. Dataset Quality Check
Benford’s law to do test of the quality of data
Linked Edit Rules: a methodology to publish, link, combine and execute edit rules on the
Web as Linked Data to verify consistency of statistical datasets and recognize wrong filled
data values (for example, characters in values where numbers expected)
Chart visualization to get overview of missing data values
18. Datasets combining and aggregation in data exploration tools
● tools that can predict the same variables disambiguated in different datasets (Year and
Jaar)
● geocoding services can standardize different regions (Netherlands → NL, Amsterdam
→ AMS)
● all possible relationship paths can be ranked from the “best” (100%) to the “worst”
(0%), for example, value “05” for variable “Month” can be recognized as “May”
● standardized datasets can be used as “reference” data for other datasets from other
researcher groups and depot services (CHIA, Harvard DataVerse Network, MIT,
DANS)
19. Clio Infra Infrastructure is suitable for different kinds of datasets
Quantitative datasets that store quantity data values (numbers):
- data measured (length, speed, height, age)
- example: current clio-infra datasets
Qualitative datasets store quality observations (descriptions):
- data can be observed (colors, textures, professions, groups)
- example: HISCO, world strikes dataset
Data Visualization is different for different kinds of datasets:
- quantity can be plotted on charts, graphs, historical maps
- quality (hierarchy) can be visualized on treemaps and maps
21. Summary: data exploration and analysis will extend Dataverse functionality
● data quality check during upload/update of dataset
● automatic ingestion and recognition of years and locations in datasets
● integrated with geocoder and geoservice to get polygons for maps
● interactive dashboard to do visual exploration of variables from dataset (graph /
histogram / scatterplot)
● data processing engine to plot data on interactive historical maps (if dataset has
geospatial data)
● correlation for continuous variables
● regression analysis for estimating the relationships among variables
● building treemaps for qualitative data analysis (hierarchical data coming soon)