ScienceBase is a research infrastructure developed and operated by the U.S. Geological Survey with users and uses across a number of other agency and organization partners. Over four years ago, we released an Application Programming Interface (API) as the foundation of the system and took on the mindset that our progress would be measured by the uptake of the API by others beyond ourselves in developing interesting applications. We now measure success more by someone finding ScienceBase, organizing their data and information, developing an innovative API-driven application and then serendipitous discovery through a science meeting. Because of the way we built the RESTful API, we can characterize what parts of the system are employed. Analysis of usage data helps us take the supposition out of what works and guides design and funding decisions. This analytics-based process facilitates regular adjustments to our thinking and allows us to test design decisions as hypotheses rather than untestable aspirations.
Accumulo Summit 2015: From Big Data to Linked Data: Making Sense of Massive, ...Accumulo Summit
Talk Abstract
Linked Data Analysis is an approach designed to more efficiently and effectively interrogate and analyze highly connected Big Data. Linked Data Analysis leverages graph- style search, algorithms, and visualization to power pattern discovery, pattern matching, and anomaly detection. In this talk we will also discuss some primary use cases for Linked Data Analysis, including cyber incident detection and response
Speakers
Ely Kahn Co-founder and VP of Business Development, Sqrrl
Ely Kahn is a co-founder and VP of Business Development for Sqrrl. Previously, Ely served in a variety of positions in the Federal Government, including Director of Cybersecurity at the National Security Staff in White House, Deputy Chief of Staff at the National Protection Programs Directorate in the Department of Homeland Security, and Director of Risk Management and Strategic Innovation in the Transportation Security Administration. Before his service in the Federal Government, Ely was a management consultant with Booz Allen Hamilton. Ely has a BA from Harvard University and a MBA from the Wharton School at the University of Pennsylvania.
Adam Fuchs Chief Technology Officer, Sqrrl
As the ChiefTechnology Officer and co-founder of Sqrrl,Adam Fuchs is responsible for ensuring that Sqrrl is leading the world in Big Data Infrastructure technology. Previously at the National Security Agency, Adam was an innovator and technical director for several database projects, handling some of the world’s largest and most diverse data sets. He is a co-founder of the Apache Accumulo project. Adam has a BS in Computer Science from the University of Washington and has completed extensive graduate-level course work at the University of Maryland.
Webinar: Evolution of Data Management for the IoTSnapLogic
The Internet of Things is here - slowly, unevenly and in vertical markets, creating new data silos. Evolving data management will be required to realize the full potential of the IoT. There are specific needs for properly addressing IoT data, which legacy ETL tools and database management systems simply don't handle well.
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Accumulo Summit 2015: From Big Data to Linked Data: Making Sense of Massive, ...Accumulo Summit
Talk Abstract
Linked Data Analysis is an approach designed to more efficiently and effectively interrogate and analyze highly connected Big Data. Linked Data Analysis leverages graph- style search, algorithms, and visualization to power pattern discovery, pattern matching, and anomaly detection. In this talk we will also discuss some primary use cases for Linked Data Analysis, including cyber incident detection and response
Speakers
Ely Kahn Co-founder and VP of Business Development, Sqrrl
Ely Kahn is a co-founder and VP of Business Development for Sqrrl. Previously, Ely served in a variety of positions in the Federal Government, including Director of Cybersecurity at the National Security Staff in White House, Deputy Chief of Staff at the National Protection Programs Directorate in the Department of Homeland Security, and Director of Risk Management and Strategic Innovation in the Transportation Security Administration. Before his service in the Federal Government, Ely was a management consultant with Booz Allen Hamilton. Ely has a BA from Harvard University and a MBA from the Wharton School at the University of Pennsylvania.
Adam Fuchs Chief Technology Officer, Sqrrl
As the ChiefTechnology Officer and co-founder of Sqrrl,Adam Fuchs is responsible for ensuring that Sqrrl is leading the world in Big Data Infrastructure technology. Previously at the National Security Agency, Adam was an innovator and technical director for several database projects, handling some of the world’s largest and most diverse data sets. He is a co-founder of the Apache Accumulo project. Adam has a BS in Computer Science from the University of Washington and has completed extensive graduate-level course work at the University of Maryland.
Webinar: Evolution of Data Management for the IoTSnapLogic
The Internet of Things is here - slowly, unevenly and in vertical markets, creating new data silos. Evolving data management will be required to realize the full potential of the IoT. There are specific needs for properly addressing IoT data, which legacy ETL tools and database management systems simply don't handle well.
This presentation is from a webinar with Joseph A. di Paolantonio, industry expert working at the convergence of IoT with data management and analytics, where he discusses five recommendations on gaining advantage through the latest IoT data management technologies and business processes.
Scaling ML-Based Threat Detection For Production Cyber AttacksDatabricks
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_Search? Made Simple: Elastic + App SearchElasticsearch
Get an in-depth look at Elastic App Search, the fastest and simplest way to add search to your internal or external application. Learn how to quickly deploy highly relevant and performant search in your app.
Watch this recorded demonstration of SnapLogic from our team of experts who answer your hybrid cloud and big data integration questions.
demo, ipaas, elastic integration, cloud data, app integration, data integration, hybrid could integration
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
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Google Cloud Data Platform - Why Google for Data Analysis?Andreas Raible
Introduction to our Data Platform from capture, processing, analysis and exploration.
The Google Cloud Platform products are based on our internal systems which are powering Google AdWords, Search, YouTube and our leading research in the field of real-time data analysis.
You can get access ($300 for 60 days) to our free trial through google.com/cloud
Here are some of the things our Data Analytics team can doLoren Moss
Using tools like Alteryx, AWS Quicksight, and methods such as RegEx, JSON, Python, SQL and SPARQL we can help extract the knowledge hidden in your data. www.unidodigital.com
New Relic Plugin for Hadoop | Blue MedoraBlue Medora
Monitor the health and performance of your hadoop clusters inside New Relic using this Insights-enabled plugin. Learn more at www.bluemedora.com/newrelic
Our post recession economy is demanding better decision making in a more timely and effective manner. Business Intelligence (BI) software is the next tool you can't do without! From financial reporting to budgeting, company consolidation to sales analysis, we'll show you creative and powerful ways to utilize Sage's BI tools. If you're looking for a software package that is going to provide you the information you need, when you need it, in a format you can understand, then you simply must attend this session.
Elastic APM: amplificação dos seus logs e métricas para proporcionar um panor...Elasticsearch
Não importa onde você esteja em sua jornada rumo à nuvem, o Elastic APM ajuda a oferecer melhores experiências ao cliente, identificando gargalos de desempenho e identificando regressões de novas implantações com mais rapidez.
Combining logs, metrics, and traces for unified observabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Scaling ML-Based Threat Detection For Production Cyber AttacksDatabricks
Vulnerabilities such as Spectre and Meltdown continue to plague many production servers, based on Intel CPUs. Our solution involves software-based monitoring of hardware counters and sending that data to Apache Spark clusters for threat detection. We leverage Spark's support for support vector machine (SVM) inference. Our machine learning models are trained off-line by a data scientist within a Jupyter notebook environment. As new models are validated, they can be easily deployed to the Spark cluster from the notebook. We have standardized model export and import using the ONNX machine learning open file format. In our presentation, we will demo the full pipeline, from model training to deployment. We will discuss the various challenges when deploying ML-based cyber-threat detection at scale using Apache Spark. For example, we found that gaps in detection can occur when Spark models are updated. We will describe a novel data ingestion architecture, based on Apache Kafka, that we developed to deal with this issue.
_Search? Made Simple: Elastic + App SearchElasticsearch
Get an in-depth look at Elastic App Search, the fastest and simplest way to add search to your internal or external application. Learn how to quickly deploy highly relevant and performant search in your app.
Watch this recorded demonstration of SnapLogic from our team of experts who answer your hybrid cloud and big data integration questions.
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Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Google Cloud Data Platform - Why Google for Data Analysis?Andreas Raible
Introduction to our Data Platform from capture, processing, analysis and exploration.
The Google Cloud Platform products are based on our internal systems which are powering Google AdWords, Search, YouTube and our leading research in the field of real-time data analysis.
You can get access ($300 for 60 days) to our free trial through google.com/cloud
Here are some of the things our Data Analytics team can doLoren Moss
Using tools like Alteryx, AWS Quicksight, and methods such as RegEx, JSON, Python, SQL and SPARQL we can help extract the knowledge hidden in your data. www.unidodigital.com
New Relic Plugin for Hadoop | Blue MedoraBlue Medora
Monitor the health and performance of your hadoop clusters inside New Relic using this Insights-enabled plugin. Learn more at www.bluemedora.com/newrelic
Our post recession economy is demanding better decision making in a more timely and effective manner. Business Intelligence (BI) software is the next tool you can't do without! From financial reporting to budgeting, company consolidation to sales analysis, we'll show you creative and powerful ways to utilize Sage's BI tools. If you're looking for a software package that is going to provide you the information you need, when you need it, in a format you can understand, then you simply must attend this session.
Elastic APM: amplificação dos seus logs e métricas para proporcionar um panor...Elasticsearch
Não importa onde você esteja em sua jornada rumo à nuvem, o Elastic APM ajuda a oferecer melhores experiências ao cliente, identificando gargalos de desempenho e identificando regressões de novas implantações com mais rapidez.
Combining logs, metrics, and traces for unified observabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Analytical Innovation: How to Build the Next Generation Data PlatformVMware Tanzu
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Join guest speaker, 451 Research’s Jim Curtis and Pivotal’s Jacque Istok for an interactive discussion about some of the overarching trends affecting the data warehousing market, as well as how to build a next generation data platform to accelerate business innovation. During this webinar you will learn:
- The significance of a multi-cloud, infrastructure-agnostic analytics
- What is working and what isn’t, when it comes to analytics integration
- The importance of seamlessly integrating all your analytics in one platform
- How to innovate faster, taking advantage of open source and agile software
Speakers: James Curtis, Senior Analyst, Data Platforms & Analytics, 451 Research & Jacque Istok, Head of Data, Pivotal
Workshop presented at Webdagene 2013 (http://webdagene.no/en/) September 9, 2013; UX Lisbon (http://www.ux-lx.com), May 12, 2011; UX Hong Kong (http://www.uxhongkong.com/), February 17, 2011.
ITANA 2016: API Architecture and ImplementationColin Bell
Presentation w/ Zachary Seguin, Kartik Talwar, and Nate Vexler for ITANA (https://spaces.internet2.edu/display/itana/Home) API Group. Covers the University of Waterloo's development of API capabilities starting with a student led Open Data initiative.
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...Geoffrey Fox
Keynote at Sixth International Workshop on Cloud Data Management CloudDB 2014 Chicago March 31 2014.
Abstract: We introduce the NIST collection of 51 use cases and describe their scope over industry, government and research areas. We look at their structure from several points of view or facets covering problem architecture, analytics kernels, micro-system usage such as flops/bytes, application class (GIS, expectation maximization) and very importantly data source.
We then propose that in many cases it is wise to combine the well known commodity best practice (often Apache) Big Data Stack (with ~120 software subsystems) with high performance computing technologies.
We describe this and give early results based on clustering running with different paradigms.
We identify key layers where HPC Apache integration is particularly important: File systems, Cluster resource management, File and object data management, Inter process and thread communication, Analytics libraries, Workflow and Monitoring.
See
[1] A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures, Shantenu Jha, Judy Qiu, Andre Luckow, Pradeep Mantha and Geoffrey Fox, accepted in IEEE BigData 2014, available at: http://arxiv.org/abs/1403.1528
[2] High Performance High Functionality Big Data Software Stack, G Fox, J Qiu and S Jha, in Big Data and Extreme-scale Computing (BDEC), 2014. Fukuoka, Japan. http://grids.ucs.indiana.edu/ptliupages/publications/HPCandApacheBigDataFinal.pdf
Similar to Science base usage analysis - AGU2016 - in21d08 (20)
USGS research infrastructure - AGU2016 - in13 eSky Bristol
This is a modified talk from the original abstract submitted for the session. It was used to provide some context on the USGS research infrastructure and how we assess progress using the US Government performance management framework and the USGS Fundamental Science Practices.
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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.
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This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
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1. Measuring the impact of an API-
first mentality with ScienceBase
after 4.5 years
Sky Bristol1
Steve Tekell2
U.S. Department of the Interior
U.S. Geological Survey
1. USGS Core Science Analytics,
Synthesis and Libraries
2. USGS Fort Collins Science
Center
2. AGU Fall Meeting 2014
Talking Points
• ScienceBase – brief history
• What does usage tell us about how the system
is doing?
– Live apps
– Usage logs
• Public search observations
• Lessons and Implications
3. AGU Fall Meeting 2014
2006
2007
2009
2011-
2016
myUSGS Data
Explorer/Data
Uploader
Scientific Data
Catalog/Compr
ehensive
Science Catalog
ScienceBase 1.0
and then 2.0
API-driven
design
Collaborative
tools and simple
file upload
Metadata
Cataloging &
Research Item
Concept
Digital Repository
& Research Item
Faceting
API use exceeds
portal traffic with
70+ API-driven
apps
“In the research process, we need more than just a big catalog of data. We need all
of the other important information connected to our work – published papers,
manuscripts, software, and information about people, labs, projects, and others in
our field.”
Inspiration and History
11. AGU Fall Meeting 2014
Access to
ScienceBase via
code libraries is
beginning to
outpace access via
the web portal and
other clients
12. AGU Fall Meeting 2014
API access includes
HTTP REST access to
the ScienceBase
Catalog along with
OGC catalog
requests and OGC
data services (WMS,
WFS, WCS, KML) for
hosted data assets
13. AGU Fall Meeting 2014
Search engine
optimization with
schema.org
metadata resulting
in sometimes better
results than our own
search, easy custom
search apps, and
discovery “in the
wild”
14. AGU Fall Meeting 2014
Full title search
top of the search list
most times
Adding
“sciencebase” will
get there every time
15. AGU Fall Meeting 2014
Simple searches
without trigger
words is still pretty
good
Note here the more
appropriate search
result coming from
the ScienceBase-
driven web app
16. AGU Fall Meeting 2014
Lessons & Implications
• When a data system becomes successful and used, it
becomes really difficult to pay down technical debt and
invest in new capabilities.
• While it is possible to detect a tremendous number of
signals from RESTful request logs, it takes significant
engineering work to bake in useful reporting and analysis
tools.
• Still work to do on semantics, linked data, and knowledge
graph influence.
• API keys are hard to implement once the cat is out of the
bag.
• It’s hard to convince managers that “stealth apps” are the
greatest indicator of success.
17. AGU Fall Meeting 2014
Contacts
www.sciencebase.gov
sciencebase@usgs.gov
www.google.com
Myriad other apps that may or may not indicate
they are powered by ScienceBase
