Research workflow - 4 June 2018

•

0 likes•208 views

Zachary Labe

Discussion of improving open data/code in science. For more info: https://github.com/zmlabe/ClimatePython

Science

RESEARCH
WORKFLOW
https://github.com/zmlabe/ClimatePython
ZACHARY LABE (@ZLABE)

Today’s weather or climate
scientist is far more likely to be
debugging code written in
Python… than to be poring over
satellite images or releasing
radiosondes.
“
D. Irving| Bulletin of the American Meteorological Society| 2016

D. Irving| Bulletin of the American Meteorological Society| 2016

It helps to reduce the shame
many scientists have about the
quality of their code (i.e., they see
that their peers are no ”better” at
coding than they are)…
“
D. Irving| Bulletin of the American Meteorological Society| 2016

WE HAVE ALL DONE IT…
Data is available from
the corresponding author
upon request

Code is available from
the corresponding author
upon request
WE HAVE ALL DONE IT…

Increasing
number of code
and data policies

Advanced analysis
of data using
Python, NCL, etc
Create science
figuresDownload or
conduct climate
model simulation
Download
observational or
reanalysis data
Pre-process data using NCO, CDO, or NCL
Store processed data on remote server
WORKFLOW

Use of GitHub for version control
with 1 repository per project
Create a working directory and
a backup directory for project
Advanced analysis
of data using
Python, NCL, etc
Create science
figuresDownload or
conduct climate
model simulation
Download
observational or
reanalysis data
WORKFLOW

Assess code
documentation
Use Zenodo to
create project
DOIWORKFLOW
Advanced analysis
of data using
Python, NCL, etc
Create science
figuresDownload or
conduct climate
model simulation
Download
observational or
reanalysis data

WHY
Transparency
Reproducibility
Code-sharing
Innovation
Open-science
Climate science
needs this!
Python is a growing
community
Science deserves
to be shared
Big data is the
future
Is there a crisis in
science?

Aparna Radhakrishnan, Engility NOAA/GFDL was founded in 1955 and is still in the forefront of climate research, contributing to the numerous policies and decisions undertaken in this world of evolving responses with respect to climate, which in turn creates an avalanche of effects in various sectors, e.g agriculture, health, GDP. The scale and magnitude of computing and data have proven to increase significantly in the last decade, thus making data delivery methods to the world a herculean research problem by itself. In addition to this, the time and efforts invested by a user in analyzing and peer-reviewing a research article is very laborious. Literature shows numerous outstanding climate studies published in International climate assessment reports, such as the Intergovernmental Panel on Climate Change (IPCC), the United Nations body for assessing the science related to climate change. The need to verify the research and make it reproducible and transparent before it gets translated into major decisions is, now more than ever, one of our most critical challenges. In this presentation, we will paint a picture of the history of climate computing and analytics with significant transformations applied in order to make meaningful, quantifiable, credible, interoperable, accessible and reusable climate research. In other words, we will draw a path towards reproducible research using Docker containers for massive data publishing and climate analytics. This paper will also discuss some of the pioneering efforts from collaborators from other laboratories and organizations (such as ESGF, Google, NASA JPL, Columbia University, PMEL, etc.) in the area of Docker containers in computing and analysis on and off the cloud.

Examples of Applied Semantic Technologies: Application of Semantic Sensor Net...

Artificial Intelligence Institute at UofSC

Genome-scale Big Data Pipelines

Lynn Langit

Automating Environmental Computing Applications with Scientific Workflows

Rafael Ferreira da Silva

Presentation held at the Environmental Computing Workshop on October 23, 2016 Abstract - Computational environmental science applications have evolved and become more complex over the last decade. In order to cope with the needs of such applications, computational methods and technologies have emerged to support the execution of these applications on heterogeneous, distributed systems. Among them are workflow management systems such as Pegasus. Pegasus is being used by researchers to model seismic wave propagation, to discover new celestial objects, to study RNA critical to human brain development, and to investigate other important research questions. This paper provides an introduction to scientific workflows and describes Pegasus and its main features. The paper highlights how the environmental science community has used Pegasus to automate their scientific workflow executions on high performance and high throughput computing systems by presenting three use cases: two Earth science workflows, and a climate science workflow.

April_Byrne_resumeApril Byrne

Big data and analytics—companies everywhere are talking about it, but what are they really delivering? JD Stanley will share his perspective, exploring how emerging tools can reduce inefficiencies and administration in upfront or post algorithm iterative improvement processes. In this session, expect to hear about practical methods of investigative analysis to drive discoveries of attributes and characteristics of the disparate data to achieve quicker business or science efficiencies. JD will highlight machine learning approaches to deriving the propensity of a connected data landscape (or “propensity of connectedness” through co-occurence data scoring) and mixing statistics and modeling into a composition approach to improve the upfront part of analysis and analytics.

iMicrobe_ASLO_2015

Bonnie Hurwitz

OntoSoft: A Distributed Semantic Registry for Scientific Software

dgarijo

Credit to Yolanda Gil. OntoSoft is a distributed semantic registry for scientific software. This paper describes three major novel contributions of OntoSoft: 1) a software metadata registry designed for scientists, 2) a distributed approach to software registries that targets communities of interest, and 3) metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology along six dimensions that matter to scientists: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software. OntoSoft is a distributed registry where each site is owned and maintained by a community of interest, with a distributed semantic query capability that allows users to search across all sites. The registry has metadata crowdsourcing capabilities, supported through access control so that software authors can allow others to expand on specific metadata properties.

Putting Data to Work: Moving science forward together beyond where we thought...

Erin Robinson

Citation: Robinson, Erin. (2020, December). Putting Data to Work: Moving science forward together beyond where we thought possible!. Presented at the 2020 Fall American Geophysical Union Meeting (AGU), Remote: Zenodo. http://doi.org/10.5281/zenodo.4323170 We live in a world rich with data, where use and reuse would benefit not just science but also serve national security and society-at-large. Air quality impacts from forest fires, which are increasing in frequency, is one example of large, data-intensive science with societal impacts. Understanding long-range transport of smoke where I started my career and worked with Dr. Greg Leptoukh, for whom this lecture is named, required a variety of datasets from satellite, surface observations and models. Together with Greg, we formed the ESIP Air Quality Cluster, a community of practice, to determine which and how to use data access standards and metadata standards agreed to best support the broader Air Quality research community. Forest fire smoke analysis was based on datasets not originally intended for our purpose, but because the data was findable, accessible, interoperable and reusable (FAIR) and we were willing to reuse it, we reduced the time to wrangle data and were able to ask and answer new questions about each smoke event. Today, we are seeing more and more examples like mine of science that was not possible without open data, standards and tools. However, our scientific data enterprise is evolving and maturing in an unmanaged fashion and due to insufficient coordination across planning, management, and resources, the potential benefits of all these data and distributed infrastructure are not fully realized. Reliable, long term funding as well as cultural changes including financial incentives and rewards are needed to turn Science Data Infrastructure into a first class citizen equal to Science. This talk will explore what it means to put data to work and explore the relationship between data-intensive science, data management and collaborative community efforts like the Earth Science Information Partners (ESIP) and Openscapes to move science forward beyond where we thought possible!

Big Data Day LA 2016/ Data Science Track - The Evolving Data Science Landscap...

Data Con LA

Finding Emerging Topics Using Chaos and Community Detection in Social Media G...

Paragon_Science_Inc

Software Sustainability: Better Software Better Science

Carole Goble

Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014. Based on the article Carole Goble , Better Software, Better Research Issue No.05 - Sept.-Oct. (2014 vol.18) pp: 4-8 IEEE Computer Society http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88 http://www.software.ac.uk/resources/publications/better-software-better-research

AGU Leptoukh Lecture: Putting Data to Work: Moving science forward together b...

Erin Robinson

Sharing massive data analysis: from provenance to linked experiment reports

Gaignard Alban

Big Data in NATO and Your Role

Jay Gendron

"Big Data" is term heard more and more in industry – but what does it really mean? There is a vagueness to the term reminiscent of that experienced in the early days of cloud computing. This has led to a number of implications for various industries and enterprises. These range from identifying the actual skills needed to recruit talent to articulating the requirements of a "big data" project. Secondary implications include difficulties in finding solutions that are appropriate to the problems at hand – versus solutions looking for problems. This presentation will take a look at Big Data and offer the audience with some considerations they may use immediately to assess the use of analytics in solving their problems. The talk begins with an idea of how big "Big Data" can be. This leads to an appreciation of how important "Management Questions" are to assessing analytic needs. The fields of data and analysis have become extremely important and impact nearly all facets of life and business. During the talk we will look at the two pillars of Big Data – Data Warehousing and Predictive Analytics. Then we will explore the open source tools and datasets available to NATO action officers to work in this domain. Use cases relevant to NATO will be explored with the purpose of show where analytics lies hidden within many of the day-to-day problems of enterprises. The presentation will close with a look at the future. Advances in the area of semantic technologies continue. The much acclaimed consultants at Gartner listed Big Data and Semantic Technologies as the first- and third-ranked top technology trends to modernize information management in the coming decade. They note there is an incredible value "locked inside all this ungoverned and underused information." HQ SACT can leverage this powerful analytic approach to capture requirement trends when establishing acquisition strategies, monitor Priority Shortfall Areas, prepare solicitations, and retrieve meaningful data from archives.

Reproducible Workflow with Cytoscape and Jupyter Notebook

Keiichiro Ono

Accelerating Science with Cloud Technologies in the ABoVE Science Cloud

Globus

What's hot

2016 Year In Review

Lawrence Livermore National Laboratory

1Spatial: Cardiff FME World Tour: Saving memory, saving time

1Spatial

GeoComputation Conference - Dallas (2015)

Irene Garcia-Marti

LLNL 2015 Year in Review

Lawrence Livermore National Laboratory

Sensor thingsapi webinar-#3-rest-for-iot-api-20151210

SensorUp

A System for the Automatic Comparison of Machine and Human Geocoded Documents

Ian Turton

What's hot (6)

2016 Year In Review

1Spatial: Cardiff FME World Tour: Saving memory, saving time

GeoComputation Conference - Dallas (2015)

LLNL 2015 Year in Review

Sensor thingsapi webinar-#3-rest-for-iot-api-20151210

A System for the Automatic Comparison of Machine and Human Geocoded Documents

Similar to Research workflow - 4 June 2018

The changing landscape of science

Chelle Gentemann

2015 genome-center

c.titus.brown

2016 07 12_purdue_bigdatainomics_seandavis

Sean Davis

Open Science Data Cloud - CCA 11

Robert Grossman

Data Science Popup Austin: Back to The Future for Data and Analytics

Domino Data Lab

iMicrobe_ASLO_2015

Bonnie Hurwitz

OntoSoft: A Distributed Semantic Registry for Scientific Software

dgarijo

Putting Data to Work: Moving science forward together beyond where we thought...

Erin Robinson

Big Data Day LA 2016/ Data Science Track - The Evolving Data Science Landscap...

Data Con LA

Finding Emerging Topics Using Chaos and Community Detection in Social Media G...

Paragon_Science_Inc

Software Sustainability: Better Software Better Science

Carole Goble

AGU Leptoukh Lecture: Putting Data to Work: Moving science forward together b...

Erin Robinson

Sharing massive data analysis: from provenance to linked experiment reports

Gaignard Alban

Big Data in NATO and Your Role

Jay Gendron

Reproducible Workflow with Cytoscape and Jupyter Notebook

Keiichiro Ono

Accelerating Science with Cloud Technologies in the ABoVE Science Cloud

Globus

Scott Edmunds: Using FAIR principles for more Open & Democratic Science

GigaScience, BGI Hong Kong

Research Data Sharing: A Basic Framework

Paul Groth

Learn to speak open

Lilian Juma

Capturing Context in Scientific Experiments: Towards Computer-Driven Science

dgarijo

Scientists publish computational experiments in ways that do not facilitate reproducibility or reuse. Significant domain expertise, time and effort are required to understand scientific experiments and their research outputs. In order to improve this situation, mechanisms are needed to capture the exact details and the context of computational experiments. Only then, Intelligent Systems would be able help researchers understand, discover, link and reuse products of existing research. In this presentation I will introduce my work and vision towards enabling scientists share, link, curate and reuse their computational experiments and results. In the first part of the talk, I will present my work for capturing and sharing the context of scientific experiments by using scientific workflows and machine readable representations. Thanks to this approach, experiment results are described in an unambiguous manner, have a clear trace of their creation process and include a pointer to the sources used for their generation. In the second part of the talk, I will describe examples on how the context of scientific experiments may be exploited to browse, explore and inspect research results. I will end the talk by presenting new ideas for improving and benefiting from the capture of context of scientific experiments and how to involve scientists in the process of curating and creating abstractions on available research metadata.

Similar to Research workflow - 4 June 2018 (20)

The changing landscape of science

2015 genome-center

2016 07 12_purdue_bigdatainomics_seandavis

Open Science Data Cloud - CCA 11

Data Science Popup Austin: Back to The Future for Data and Analytics

iMicrobe_ASLO_2015

OntoSoft: A Distributed Semantic Registry for Scientific Software

Putting Data to Work: Moving science forward together beyond where we thought...

Big Data Day LA 2016/ Data Science Track - The Evolving Data Science Landscap...

Finding Emerging Topics Using Chaos and Community Detection in Social Media G...

Software Sustainability: Better Software Better Science

AGU Leptoukh Lecture: Putting Data to Work: Moving science forward together b...

Sharing massive data analysis: from provenance to linked experiment reports

Big Data in NATO and Your Role

Reproducible Workflow with Cytoscape and Jupyter Notebook

Accelerating Science with Cloud Technologies in the ABoVE Science Cloud

Scott Edmunds: Using FAIR principles for more Open & Democratic Science

Research Data Sharing: A Basic Framework

Learn to speak open

Capturing Context in Scientific Experiments: Towards Computer-Driven Science

More from Zachary Labe

Welcome to GFDL for Take Your Child To Work Day

Zachary Labe

Explainable AI for distinguishing future climate change scenarios

Zachary Labe

Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. Explainable AI for distinguishing future climate change scenarios, EGU General Assembly 2024, Vienna, Austria (Apr 2024). Abstract... https://meetingorganizer.copernicus.org/EGU24/EGU24-9110.html References... Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke (2024). Exploring a data-driven approach to identify regions of change associated with future climate scenarios. (submitted; preprint - https://doi.org/10.22541/essoar.171288901.17027965/v1)

Reexamining future projections of Arctic climate linkages

Zachary Labe

10 May 2024… Atmospheric and Oceanic Sciences Student/Postdoc Seminar (Presentation): Reexamining future projections of Arctic climate linkages, Princeton University, USA. References... Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss, Geophysical Research Letters, DOI:10.1029/2018GL078158 Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI:10.1029/2019GL083095 Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583 Labe, Z.M., May 2020: The effects of Arctic sea-ice thickness loss and stratospheric variability on mid-latitude cold spells. University of California, Irvine. Doctoral Dissertation. Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate, DOI:10.1175/JCLI-D-20-0613.1

Techniques and Considerations for Improving Accessibility in Online Media

Zachary Labe

An intro to explainable AI for polar climate science

Zachary Labe

26 March 2024… GFDL Polar Climate Interest Group (Presentation): An intro to explainable AI for polar climate science, NOAA GFDL, Princeton, NJ. References: Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348 Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002464

Using accessible data to communicate global climate change

Zachary Labe

Water in a Frozen Arctic: Cross-Disciplinary Perspectives

Zachary Labe

Explainable AI approach for evaluating climate models in the Arctic

Zachary Labe

27 March 2024… IARPC Collaborations, Modelers’ Community of Practice (Presentation): Explainable AI approach for evaluating climate models in the Arctic. Remote Presentation. References... Labe, Z. M., & Barnes, E. A. (2022). Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, 9(7), e2022EA002348, https://doi.org/10.1029/2022EA002348

Explainable neural networks for evaluating patterns of climate change and var...

Zachary Labe

12 March 2024… Sharing Science – North American Webinar, Young Earth System Scientists (YESS) Community (Presentation): Explainable neural networks for evaluating patterns of climate change and variability. Remote Presentation. References... Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748-9326/acc81a

Applications of machine learning for climate change and variability

Zachary Labe

23 February 2024… Department of Environmental Sciences Seminar (Presentation): Applications of machine learning for climate change and variability, Rutgers University, New Brunswick, NJ. References: Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464 Labe, Z.M. and E.A. Barnes (2022), Predicting slowdowns in decadal climate warming trends with explainable neural networks. Geophysical Research Letters, DOI:10.1029/2022GL098173 Labe, Z. M., Johnson, N. C., & Delworth, T. L. (2024). Changes in United States summer temperatures revealed by explainable neural networks. Earth's Future, DOI:10.1029/2023EF003981

data-driven approach to identifying key regions of change associated with fut...

Zachary Labe

Distinguishing the regional emergence of United States summer temperatures be...

Zachary Labe

Researching and Communicating Our Changing Climate

Zachary Labe

Revisiting projections of Arctic climate change linkages

Zachary Labe

16 November 2023… Department Seminar (Presentation): Revisiting projections of Arctic climate change linkages, Tongji University, Shanghai, China. Remote Presentation. References: Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss, Geophysical Research Letters, DOI: 10.1029/2018GL078158 Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI: 10.1029/2019GL083095 Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI: 10.1029/2020GL088583 Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate, DOI: 10.1175/JCLI-D-20-0613.1 Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI: 10.1029/2022EA002348

Visualizing climate change through data

Zachary Labe

18 November 2023… NJ State Museum Planetarium (Presentation): Visualizing climate change through data, Trenton, NJ. References... Eischeid, J.K., M.P. Hoerling, X.-W. Quan, A. Kumar, J. Barsugli, Z.M. Labe, K.E. Kunkel, C.J. Schreck III, D.R. Easterling, T. Zhang, J. Uehling, and X. Zhang (2023). Why has the summertime central U.S. warming hole not disappeared? Journal of Climate, DOI:10.1175/JCLI-D-22-0716.1, https://journals.ametsoc.org/view/journals/clim/36/20/JCLI-D-22-0716.1.xml

Using explainable machine learning to evaluate climate change projections

Zachary Labe

5 October 2023… Atmosphere and Ocean Climate Dynamics Seminar (Presentation): Using explainable machine learning to evaluate climate change projections, Yale University, New Haven, CT. Remote Presentation. References... Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748-9326/acc81a, https://iopscience.iop.org/article/10.1088/1748-9326/acc81a

Contrasting polar climate change in the past, present, and future

Zachary Labe

Climate change extremes by season in the United States

Zachary Labe

11 September 2023… Hershey Horticulture Society (Presentation): Climate change extremes by season in the United States, Hershey, PA, USA. References... Eischeid, J.K., M.P. Hoerling, X.-W. Quan, A. Kumar, J. Barsugli, Z.M. Labe, K.E. Kunkel, C.J. Schreck III, D.R. Easterling, T. Zhang, J. Uehling, and X. Zhang (2023). Why has the summertime central U.S. warming hole not disappeared? Journal of Climate, DOI:10.1175/JCLI-D-22-0716.1 Labe, Z.M., T.R. Ault, and R. Zurita-Milla (2016), Identifying Anomalously Early Spring Onsets in the CESM Large Ensemble Project, R. Clim Dyn, DOI:10.1007/s00382-016-3313-2 Labe, Z.M., N.C. Johnson, and T.L Delworth (2023). Changes in United States summer temperatures revealed by explainable neural networks. Preprint. DOI: 10.22541/essoar.168987129.98069596/v1

Guest Lecture: Our changing Arctic in the past and future

Zachary Labe

22 August 2023… Guest lecture for “Introduction to Global Climate Change (ESS 15)” (Invited): Our changing Arctic in the past and future, University of California, Irvine, CA. Remote Presentation. References... Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J. H., Dunne, K. A., ... & Zhao, M. (2020). SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001895, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS001895 Labe, Z.M. and E.A. Barnes (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348, https://doi.org/10.1029/2022EA002348 Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL088583

Climate Projections - What Really is Business as Usual?

Zachary Labe

More from Zachary Labe (20)

Welcome to GFDL for Take Your Child To Work Day

Explainable AI for distinguishing future climate change scenarios

Reexamining future projections of Arctic climate linkages

Techniques and Considerations for Improving Accessibility in Online Media

An intro to explainable AI for polar climate science

Using accessible data to communicate global climate change

Water in a Frozen Arctic: Cross-Disciplinary Perspectives

Explainable AI approach for evaluating climate models in the Arctic

Explainable neural networks for evaluating patterns of climate change and var...

Applications of machine learning for climate change and variability

data-driven approach to identifying key regions of change associated with fut...

Distinguishing the regional emergence of United States summer temperatures be...

Researching and Communicating Our Changing Climate

Revisiting projections of Arctic climate change linkages

Visualizing climate change through data

Using explainable machine learning to evaluate climate change projections

Contrasting polar climate change in the past, present, and future

Climate change extremes by season in the United States

Guest Lecture: Our changing Arctic in the past and future

Climate Projections - What Really is Business as Usual?

Recently uploaded

Introduction to Mean Field Theory(MFT).pptx

zeex60

Phenomics assisted breeding in crop improvement

IshaGoswami9

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.

Toxic effects of heavy metals : Lead and Arsenic

sanjana502982

Orion Air Quality Monitoring Systems - CWS

Columbia Weather Systems

Eukaryotic Transcription Presentation.pptx

RitabrataSarkar3

Deep Software Variability and Frictionless Reproducibility

University of Rennes, INSA Rennes, Inria/IRISA, CNRS

The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions. Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability. Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields. I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating). I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems. Exposé invité Journées Nationales du GDR GPL 2024

Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...

Travis Hills MN

Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.

Lateral Ventricles.pdf very easy good diagrams comprehensive

silvermistyshot

DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...

Wasswaderrick3

In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.

The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx

MAGOTI ERNEST

Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024). Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).

ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptx

RASHMI M G

NuGOweek 2024 Ghent programme overview flyer

pablovgd

bordetella pertussis.................................ppt

kejapriya1

DMARDs Pharmacolgy Pharm D 5th Semester.pdf

fafyfskhan251kmf

SAR of Medicinal Chemistry 1st by dk.pdf

KrushnaDarade1

Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...

University of Maribor

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.

Topic: SICKLE CELL DISEASE IN CHILDREN-3.pdf

TinyAnderson

Richard's aventures in two entangled wonderlands

Richard Gill

Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.

如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样

yqqaatn0

原版纸张【微信：741003700 】【(uvic毕业证书)维多利亚大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

Recently uploaded (20)

Introduction to Mean Field Theory(MFT).pptx

Phenomics assisted breeding in crop improvement

Toxic effects of heavy metals : Lead and Arsenic

Orion Air Quality Monitoring Systems - CWS

Eukaryotic Transcription Presentation.pptx

Deep Software Variability and Frictionless Reproducibility

Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...

Lateral Ventricles.pdf very easy good diagrams comprehensive

DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...

The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx

ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptx

NuGOweek 2024 Ghent programme overview flyer

bordetella pertussis.................................ppt

DMARDs Pharmacolgy Pharm D 5th Semester.pdf

SAR of Medicinal Chemistry 1st by dk.pdf

Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...

Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...

Topic: SICKLE CELL DISEASE IN CHILDREN-3.pdf

Richard's aventures in two entangled wonderlands

如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样

Research workflow - 4 June 2018

1. RESEARCH WORKFLOW https://github.com/zmlabe/ClimatePython ZACHARY LABE (@ZLABE)

2. BAKER,2016;NATURENEWS

3. Today’s weather or climate scientist is far more likely to be debugging code written in Python… than to be poring over satellite images or releasing radiosondes. “ D. Irving| Bulletin of the American Meteorological Society| 2016

4. D. Irving| Bulletin of the American Meteorological Society| 2016

5. GROWING DATA

7. GROWING TOOLS

9. It helps to reduce the shame many scientists have about the quality of their code (i.e., they see that their peers are no ”better” at coding than they are)… “ D. Irving| Bulletin of the American Meteorological Society| 2016

10. ADAPTED FROM EYRING ET AL. 2016 CMIP6

11. WE HAVE ALL DONE IT… Data is available from the corresponding author upon request

12. Code is available from the corresponding author upon request WE HAVE ALL DONE IT…

13. Increasing number of code and data policies

14.

15. This is a good thing

16. So what can we do?

17. Advanced analysis of data using Python, NCL, etc Create science figuresDownload or conduct climate model simulation Download observational or reanalysis data Pre-process data using NCO, CDO, or NCL Store processed data on remote server WORKFLOW

18. Use of GitHub for version control with 1 repository per project Create a working directory and a backup directory for project Advanced analysis of data using Python, NCL, etc Create science figuresDownload or conduct climate model simulation Download observational or reanalysis data WORKFLOW

19. Assess code documentation Use Zenodo to create project DOIWORKFLOW Advanced analysis of data using Python, NCL, etc Create science figuresDownload or conduct climate model simulation Download observational or reanalysis data

20. Behind each library

21.

22. WHY Transparency Reproducibility Code-sharing Innovation Open-science Climate science needs this! Python is a growing community Science deserves to be shared Big data is the future Is there a crisis in science?

Research workflow - 4 June 2018

Recommended

Recommended

More Related Content

What's hot

What's hot (6)

Similar to Research workflow - 4 June 2018

Similar to Research workflow - 4 June 2018 (20)

More from Zachary Labe

More from Zachary Labe (20)

Recently uploaded

Recently uploaded (20)

Research workflow - 4 June 2018