The Materials Data Facility (MDF) is a distributed model for the materials data community that aims to make materials data more shareable, open, accessible, computable, and valuable. The MDF indexes over 100 terabytes of materials data from various repositories and facilities. It provides services for data discovery, publication with DOIs, and integrates data with computing resources. The goal is to simplify critical tasks in materials science like finding relevant data, training machine learning models across multiple datasets, and reproducing results.
The document provides guidance on conducting a literature search and review. It outlines the main objectives of a literature search as identifying as many relevant published and unpublished sources as possible on a specific topic. It then describes the key stages of a literature search and review process, including determining information needs, exploring available sources, reading and annotating sources, taking notes, analyzing findings, and writing up the results. A variety of source types and search techniques are also discussed to aid in locating relevant literature.
A Machine Learning Framework for Materials Knowledge Systemsaimsnist
- The document describes a machine learning framework for developing artificial intelligence-based materials knowledge systems (MKS) to support accelerated materials discovery and development.
- The MKS would have main functions of diagnosing materials problems, predicting materials behaviors, and recommending materials selections or process adjustments.
- It would utilize a Bayesian statistical approach to curate process-structure-property linkages for all materials classes and length scales, accounting for uncertainty in the knowledge, and allow continuous updates from new information sources.
This document discusses various biomaterial fabrication techniques. It begins with an introduction to biomaterials and their uses. It then describes common materials used as biomaterials and the evolution of biomaterials. Several scaffold fabrication techniques are outlined, including solvent casting, melt molding, gas foaming, freeze drying, and fiber bonding. Limitations of these techniques are noted. Rapid prototyping and nanofabrication techniques that allow for more control and precision are also summarized.
This PPT presented at State Level FDP on "How to Create Academic & Research Identity" organized by Rishi Bankim Library in collaboration with IQAC of Rishi Bankim Chandra Evening College, Naihati, North 24 Parganas, West Bengal, India on 06th April, 2022.
Artificial intelligence (AI) and machine learning (ML) are undergoing revolutionary changes that will affect wide swaths of our society. And the applications of this technology are increasingly diverse. Join us as we narrow in on how researchers in AL and ML are using AWS to identify and prevent financial market manipulation in a high-volume, high-velocity stock market. We also explore how to use natural language processing to aid emergency response organizations in real time during deadly disasters, such as during hurricanes and catastrophic wildfires.
The document provides guidance on conducting a literature search and review. It outlines the main objectives of a literature search as identifying as many relevant published and unpublished sources as possible on a specific topic. It then describes the key stages of a literature search and review process, including determining information needs, exploring available sources, reading and annotating sources, taking notes, analyzing findings, and writing up the results. A variety of source types and search techniques are also discussed to aid in locating relevant literature.
A Machine Learning Framework for Materials Knowledge Systemsaimsnist
- The document describes a machine learning framework for developing artificial intelligence-based materials knowledge systems (MKS) to support accelerated materials discovery and development.
- The MKS would have main functions of diagnosing materials problems, predicting materials behaviors, and recommending materials selections or process adjustments.
- It would utilize a Bayesian statistical approach to curate process-structure-property linkages for all materials classes and length scales, accounting for uncertainty in the knowledge, and allow continuous updates from new information sources.
This document discusses various biomaterial fabrication techniques. It begins with an introduction to biomaterials and their uses. It then describes common materials used as biomaterials and the evolution of biomaterials. Several scaffold fabrication techniques are outlined, including solvent casting, melt molding, gas foaming, freeze drying, and fiber bonding. Limitations of these techniques are noted. Rapid prototyping and nanofabrication techniques that allow for more control and precision are also summarized.
This PPT presented at State Level FDP on "How to Create Academic & Research Identity" organized by Rishi Bankim Library in collaboration with IQAC of Rishi Bankim Chandra Evening College, Naihati, North 24 Parganas, West Bengal, India on 06th April, 2022.
Artificial intelligence (AI) and machine learning (ML) are undergoing revolutionary changes that will affect wide swaths of our society. And the applications of this technology are increasingly diverse. Join us as we narrow in on how researchers in AL and ML are using AWS to identify and prevent financial market manipulation in a high-volume, high-velocity stock market. We also explore how to use natural language processing to aid emergency response organizations in real time during deadly disasters, such as during hurricanes and catastrophic wildfires.
Deep learning is a type of machine learning that uses neural networks with multiple layers between the input and output layers. It allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Deep learning has achieved great success in computer vision, speech recognition, and natural language processing due to recent advances in algorithms, computing power, and the availability of large datasets. Deep learning models can learn complex patterns directly from large amounts of unlabeled data without relying on human-engineered features.
My keynote talk at San Diego Superdata conference, looking at history and current state of Analytics and Data Mining, and examining the effects of Big Data
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Artificial intelligence tools can help researchers in many ways:
- AI tools can help researchers gather, organize, and analyze large amounts of data from various sources to generate insights and identify gaps or opportunities for further research. This can streamline research processes and accelerate innovation.
- Several AI tools are described that can assist with literature reviews, data analysis, writing and editing assistance, collaboration, and more. Tools like Google Scholar, Wordvice AI, and Typeset.io provide features for searching literature, editing documents, and ensuring academic writing standards are followed.
- Other tools like ChatPDF, Consensus, and OpenRead use AI to summarize and extract key information from documents, help find relevant research, and enhance how
The document discusses artificial intelligence (AI), including its definition as modeling human intelligence using computer systems. It outlines the early history of AI beginning in 1950 with Alan Turing's landmark paper asking if machines can think. The document also describes the Turing Test for intelligence and examples of modern AI like Google Assistant, Siri, Cortana and Sophia, the humanoid robot. The rise of AI presents both opportunities like meeting needs efficiently but also threats if robots gain too much power or capabilities are misused.
This document discusses methods for estimating human pose from images using deep learning. It covers several approaches including SMPLIFY and SCAPE. SMPLIFY uses a CNN to detect 2D joints then fits a statistical body model called SMPL to estimate 3D pose. SCAPE is a graphics model of human shape learned from 3D scans, capturing pose and shape variability. The document reviews similarities and differences between methods, including using priors, image features, and optimization. It also discusses improving methods by making them fully automatic using detected joints rather than manual inputs.
machines will be capable, within 20 years, of doing any work a man can do." Two years later, MIT researcher Marvin Minsky predicted, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
(artificial intelligence innovator Herbert Simon.1965
Deep learning is a type of machine learning that uses multiple processing layers to learn representations of data with features that become more complex at each layer. Deep learning has achieved human-level performance in areas like image recognition by learning from large datasets. In healthcare, deep learning has been applied to tasks like detecting pneumonia from chest X-rays and skin cancer from images with accuracy comparable to doctors. However, challenges remain around data variability, uncertainty, class imbalance, and data annotation. Cross-area collaboration and data sharing are seen as key to realizing the potential of deep learning in healthcare.
3D tumor spheroid models for in vitro therapeutic screening: a systematic app...Arun kumar
This document describes a study that aimed to identify and validate a cytotoxicity test for large tumor spheroids. Several methods were used to culture 3D tumor spheroids from lung cancer cells. Spheroid size, shape, and heterogeneity were characterized using imaging software. Cell viability was assessed using different assays under drug and radiation treatment. The results showed that spheroid shape and volume influence cytotoxicity assay results. Careful selection of homogeneous, spherical spheroids is important for reliable data in drug screening tests using 3D tumor models.
Introduction to Artificial IntelligenceLuca Bianchi
Artificial intelligence has been defined in many ways as our understanding has evolved. Currently, AI is divided into narrow, general and super intelligence based on capabilities. Machine learning is a key approach in AI and involves algorithms that can learn from data to improve performance. Deep learning uses neural networks with many layers to learn representations of data and has achieved success in areas like computer vision and natural language processing.
Michigan State University campus policy, resources and best practices for research data management offered by the MSU Libraries Research Data Management Guidance service. http://www.lib.msu.edu/rdmg/
This document provides an overview and instructions for using the SCImago Journal & Country Rank portal, which includes scientific indicators and rankings of journals and countries derived from the Scopus database. It describes how to search and filter journal and country rankings according to subject area, country, year, and other criteria. It also explains the various bibliometric indicators included in the journal and country profiles and comparison tools, such as the SJR indicator, H-index, citations per document, and more. Bubble charts can also be used to analyze and compare national scientific output based on various performance metrics.
The document discusses data indexing, which is a data structure added to files to provide faster data access. Indexing reduces the number of blocks a database management system must check when performing operations like reading, modifying, updating, and deleting data. An index contains a search key and pointer, where the search key is used to look up records and the pointer contains the address of stored data. Common indexing techniques include ordered/primary indexes that access sorted data and hash indexes that uniformly distribute data across buckets. When choosing an indexing technique, factors like access type, time, space overhead are considered. B-trees are commonly used indexing data structures that can grow and shrink dynamically with root, branch and leaf nodes.
A presentation about Ontology Learning with an overview of the area and some methods used, specially techniques of Ontology Learning from Text. This presentation was part of a seminary in the MSc Course in Computer Science at UFPE - Recife - Brazil.
This is my talk from the PyDataLondon conference in May 2016. I outline some time management techniques and useful learning resources for those interested in transitioning into data science.
The document discusses artificial intelligence (AI), including its definition, history, applications, and future. It defines AI as the study of intelligent behavior in machines and the goal of AI research is to create technology that allows computers and machines to function intelligently. Some current applications of AI discussed are robotics, medical diagnosis, video games, and computer vision. The future of AI could include personal robots or a scenario where robots turn against humans.
Materials Data Facility: Streamlined and automated data sharing, discovery, ...Ian Foster
Reviews recent results from the Materials Data Facility. Thanks in particular to Ben Blaiszik, Jonathon Goff, and Logan Ward, and the Globus data search team. Some features shown here are still in beta. We are grateful for NIST for their support.
A Data Ecosystem to Support Machine Learning in Materials ScienceGlobus
This presentation was given at the 2019 GlobusWorld Conference in Chicago, IL by Ben Blaiszik from University of Chicago and Argonne National Laboratory Data Science and Learning Division.
Deep learning is a type of machine learning that uses neural networks with multiple layers between the input and output layers. It allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. Deep learning has achieved great success in computer vision, speech recognition, and natural language processing due to recent advances in algorithms, computing power, and the availability of large datasets. Deep learning models can learn complex patterns directly from large amounts of unlabeled data without relying on human-engineered features.
My keynote talk at San Diego Superdata conference, looking at history and current state of Analytics and Data Mining, and examining the effects of Big Data
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Artificial intelligence tools can help researchers in many ways:
- AI tools can help researchers gather, organize, and analyze large amounts of data from various sources to generate insights and identify gaps or opportunities for further research. This can streamline research processes and accelerate innovation.
- Several AI tools are described that can assist with literature reviews, data analysis, writing and editing assistance, collaboration, and more. Tools like Google Scholar, Wordvice AI, and Typeset.io provide features for searching literature, editing documents, and ensuring academic writing standards are followed.
- Other tools like ChatPDF, Consensus, and OpenRead use AI to summarize and extract key information from documents, help find relevant research, and enhance how
The document discusses artificial intelligence (AI), including its definition as modeling human intelligence using computer systems. It outlines the early history of AI beginning in 1950 with Alan Turing's landmark paper asking if machines can think. The document also describes the Turing Test for intelligence and examples of modern AI like Google Assistant, Siri, Cortana and Sophia, the humanoid robot. The rise of AI presents both opportunities like meeting needs efficiently but also threats if robots gain too much power or capabilities are misused.
This document discusses methods for estimating human pose from images using deep learning. It covers several approaches including SMPLIFY and SCAPE. SMPLIFY uses a CNN to detect 2D joints then fits a statistical body model called SMPL to estimate 3D pose. SCAPE is a graphics model of human shape learned from 3D scans, capturing pose and shape variability. The document reviews similarities and differences between methods, including using priors, image features, and optimization. It also discusses improving methods by making them fully automatic using detected joints rather than manual inputs.
machines will be capable, within 20 years, of doing any work a man can do." Two years later, MIT researcher Marvin Minsky predicted, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
(artificial intelligence innovator Herbert Simon.1965
Deep learning is a type of machine learning that uses multiple processing layers to learn representations of data with features that become more complex at each layer. Deep learning has achieved human-level performance in areas like image recognition by learning from large datasets. In healthcare, deep learning has been applied to tasks like detecting pneumonia from chest X-rays and skin cancer from images with accuracy comparable to doctors. However, challenges remain around data variability, uncertainty, class imbalance, and data annotation. Cross-area collaboration and data sharing are seen as key to realizing the potential of deep learning in healthcare.
3D tumor spheroid models for in vitro therapeutic screening: a systematic app...Arun kumar
This document describes a study that aimed to identify and validate a cytotoxicity test for large tumor spheroids. Several methods were used to culture 3D tumor spheroids from lung cancer cells. Spheroid size, shape, and heterogeneity were characterized using imaging software. Cell viability was assessed using different assays under drug and radiation treatment. The results showed that spheroid shape and volume influence cytotoxicity assay results. Careful selection of homogeneous, spherical spheroids is important for reliable data in drug screening tests using 3D tumor models.
Introduction to Artificial IntelligenceLuca Bianchi
Artificial intelligence has been defined in many ways as our understanding has evolved. Currently, AI is divided into narrow, general and super intelligence based on capabilities. Machine learning is a key approach in AI and involves algorithms that can learn from data to improve performance. Deep learning uses neural networks with many layers to learn representations of data and has achieved success in areas like computer vision and natural language processing.
Michigan State University campus policy, resources and best practices for research data management offered by the MSU Libraries Research Data Management Guidance service. http://www.lib.msu.edu/rdmg/
This document provides an overview and instructions for using the SCImago Journal & Country Rank portal, which includes scientific indicators and rankings of journals and countries derived from the Scopus database. It describes how to search and filter journal and country rankings according to subject area, country, year, and other criteria. It also explains the various bibliometric indicators included in the journal and country profiles and comparison tools, such as the SJR indicator, H-index, citations per document, and more. Bubble charts can also be used to analyze and compare national scientific output based on various performance metrics.
The document discusses data indexing, which is a data structure added to files to provide faster data access. Indexing reduces the number of blocks a database management system must check when performing operations like reading, modifying, updating, and deleting data. An index contains a search key and pointer, where the search key is used to look up records and the pointer contains the address of stored data. Common indexing techniques include ordered/primary indexes that access sorted data and hash indexes that uniformly distribute data across buckets. When choosing an indexing technique, factors like access type, time, space overhead are considered. B-trees are commonly used indexing data structures that can grow and shrink dynamically with root, branch and leaf nodes.
A presentation about Ontology Learning with an overview of the area and some methods used, specially techniques of Ontology Learning from Text. This presentation was part of a seminary in the MSc Course in Computer Science at UFPE - Recife - Brazil.
This is my talk from the PyDataLondon conference in May 2016. I outline some time management techniques and useful learning resources for those interested in transitioning into data science.
The document discusses artificial intelligence (AI), including its definition, history, applications, and future. It defines AI as the study of intelligent behavior in machines and the goal of AI research is to create technology that allows computers and machines to function intelligently. Some current applications of AI discussed are robotics, medical diagnosis, video games, and computer vision. The future of AI could include personal robots or a scenario where robots turn against humans.
Materials Data Facility: Streamlined and automated data sharing, discovery, ...Ian Foster
Reviews recent results from the Materials Data Facility. Thanks in particular to Ben Blaiszik, Jonathon Goff, and Logan Ward, and the Globus data search team. Some features shown here are still in beta. We are grateful for NIST for their support.
A Data Ecosystem to Support Machine Learning in Materials ScienceGlobus
This presentation was given at the 2019 GlobusWorld Conference in Chicago, IL by Ben Blaiszik from University of Chicago and Argonne National Laboratory Data Science and Learning Division.
A Year in Review - Building a Comprehensive Data Management ProgramDataWorks Summit
This document discusses Microsoft Research's efforts to build a centralized data management and processing platform. It provides an overview of big data and its importance to Microsoft. It outlines the vision, principles, goals, and architecture of the platform, which includes Hadoop, GPUs, HPC resources, Azure, and access to datasets like MNIST and Bing data. The platform aims to support research through centralized, compliant data storage and a flexible processing system. It also discusses ensuring data privacy, security, and ethical use of data on the platform.
Changing the Curation Equation: A Data Lifecycle Approach to Lowering Costs a...SEAD
This document discusses the Sustainable Environment Actionable Data (SEAD) project, which aims to lower the costs and increase the value of data curation through a data lifecycle approach. SEAD provides lightweight data services to support sustainability research, including secure project workspaces, active and social curation tools, and integrated lifecycle support for data from ingest to long-term preservation. By leveraging technologies like Web 2.0 and standards, SEAD simplifies and automates curation processes using metadata captured from data producers and users. This allows curation activities to begin earlier in the data lifecycle and be distributed across researchers and curators.
DataCite – Bridging the gap and helping to find, access and reuse data – Herb...OpenAIRE
OpenAIRE Interoperability Workshop (8 Feb. 2013).
DataCite – Bridging the gap and helping to find, access and reuse data – Herbert Gruttemeier, INIST-CNRS
A Keynote at the Web Science Conference, 2018, held at the VU Amsterdam [1]. This describes in the main the output of the Semantic Technology Institute International (STI2) Summit (for senior researchers in the Semantic Web field) held in Crete in September, 2017 [2].
1. https://websci18.webscience.org/
2. https://www.sti2.org/events/2017-sti2-semantic-summit
This presentation was provided by Daniella Lowenberg of the California Digital Library during the NISO Virtual Conference, Advancing Altmetrics, held on Wednesday, December 13, 2017.
FAIR - Working Data - It's not just about FAIR publishing. Presented by John Morrissey from CSIRO at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management 31 may 2018 in Melbourne
1. Metrics are being developed to track downloads and reuse of research data to understand impact and reassure researchers. A new service called IRUS for Data will provide metrics for data repositories across different platforms.
2. There is debate around what data citations mean and how they should be used and understood. Projects are working to develop best practices and encourage responsible use of citation metrics for data.
3. Ensuring research data sharing is recognized in existing systems like journal policies is challenging due to lack of standards. Initiatives are working with publishers and repositories to develop guidance and implement principles for data citation.
The document discusses the impact of Covid-19 on learning and education, including long-term effects on academic setups due to lack of physical access and digital divides. It also discusses the need for and benefits of institutional repositories to manage and provide access to scholarly works. Key benefits include increased visibility, centralized storage, and supporting learning and teaching. Challenges include difficulties generating content and issues around policies, incentives, and costs. The document then focuses on the open-source DSpace software as a tool for creating institutional repositories, covering its features, requirements, structures, workflows, and examples of existing DSpace-based repositories.
This document summarizes Rob Grim's presentation on e-Science, research data, and the role of libraries. It discusses the Open Data Foundation's work in promoting metadata standards like DDI and SDMX. It also outlines the research data lifecycle and how metadata management can help libraries support research through services like data registration, archiving, discovery and access. Finally, it provides examples of how Tilburg University library supports research data through services aligned with data availability, discovery, access and delivery.
Preparing your data for sharing and publishingVarsha Khodiyar
This document provides information on preparing data for sharing and publishing. It discusses organizing data through clear file and folder labeling, including additional context about methods and instruments. It also describes publishing data through journals like Scientific Data, which provide peer review and credit. Sensitive data requires careful handling and may be suitable for controlled access repositories. Overall the document offers guidance on effective data organization, documentation, sharing and receiving credit for shared data.
MUDROD - Mining and Utilizing Dataset Relevancy from Oceanographic Dataset Me...Yongyao Jiang
MUDROD - Mining and Utilizing Dataset Relevancy from Oceanographic Dataset Metadata, Usage Metrics, and User Feedback to Improve Data Discovery and Access
OpenAIRE and Eudat services and tools to support FAIR DMP implementation Research Data Alliance
The document provides an overview of the Open Research Data Pilot, the data management plan, and OPENAIRE tools and services to support implementation of FAIR data management plans. It discusses the aims of the Open Research Data Pilot, which Horizon 2020 projects are required to participate, and the types of data that must be deposited. It also covers topics like creating a data management plan, selecting a repository, making data FAIR, and OPENAIRE support resources like briefing papers, webinars, and the Zenodo repository.
OpenAIRE and Eudat services and tools to support FAIR DMP implementation Research Data Alliance
The document provides an overview of the Open Research Data Pilot, the data management plan, and OPENAIRE tools and services to support implementation of FAIR data management plans. It discusses the aims of the Open Research Data Pilot, which Horizon 2020 projects are required to participate, and the types of data that must be deposited. It also covers topics like creating a data management plan, selecting a repository, making data FAIR, and OPENAIRE support resources like briefing papers, webinars, and the Zenodo repository.
This document provides an overview of where and how artificial intelligence (AI) is used in materials science. It discusses several key areas:
1) Hypothesis generation using archival data and machine learning to predict new materials.
2) Data acquisition, cleaning, and feature identification using AI techniques like denoising and artifact removal from experimental data.
3) Knowledge extraction from large datasets using unsupervised learning methods like non-negative matrix factorization to identify materials phases.
4) Closing the materials discovery loop with demonstrations of autonomous materials research systems that integrate computation, autonomous synthesis and characterization using AI.
Open science, open-source, and open data: Collaboration as an emergent property?Hilmar Lapp
Talk I gave as part of the panel "How will cyberinfrastructure capabilities shape the future of scientific collaboration?" at the Cyberinfrastructure for Collaborative Science workshop, held at the National Evolutionary Synthesis Center (NESCent), May 18-20, 2011.
More information about the workshop at
https://www.nescent.org/wg_collabsci/2011_Workshop
Similar to The Materials Data Facility: A Distributed Model for the Materials Data Community (20)
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Sérgio Sacani
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)eitps1506
Description:
Dive into the fascinating realm of solid-state physics with our meticulously crafted online PowerPoint presentation. This immersive educational resource offers a comprehensive exploration of the fundamental concepts, theories, and applications within the realm of solid-state physics.
From crystalline structures to semiconductor devices, this presentation delves into the intricate principles governing the behavior of solids, providing clear explanations and illustrative examples to enhance understanding. Whether you're a student delving into the subject for the first time or a seasoned researcher seeking to deepen your knowledge, our presentation offers valuable insights and in-depth analyses to cater to various levels of expertise.
Key topics covered include:
Crystal Structures: Unravel the mysteries of crystalline arrangements and their significance in determining material properties.
Band Theory: Explore the electronic band structure of solids and understand how it influences their conductive properties.
Semiconductor Physics: Delve into the behavior of semiconductors, including doping, carrier transport, and device applications.
Magnetic Properties: Investigate the magnetic behavior of solids, including ferromagnetism, antiferromagnetism, and ferrimagnetism.
Optical Properties: Examine the interaction of light with solids, including absorption, reflection, and transmission phenomena.
With visually engaging slides, informative content, and interactive elements, our online PowerPoint presentation serves as a valuable resource for students, educators, and enthusiasts alike, facilitating a deeper understanding of the captivating world of solid-state physics. Explore the intricacies of solid-state materials and unlock the secrets behind their remarkable properties with our comprehensive presentation.
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...Sérgio Sacani
Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
The Materials Data Facility: A Distributed Model for the Materials Data Community
1. Logan Ward1 (loganw@uchicago.edu)
Ben Blaiszik1,2 (blaiszik@uchicago.edu),
Ian Foster (foster@uchicago.edu)1,2, Ryan Chard2
Jonathon Gaff1, Kyle Chard1, Jim Pruyne1,
Rachana Ananthakrishnan1, Steven Tuecke1
Michael Ondrejcek3, Kenton McHenry3, John Towns3
University of Chicago1, Argonne National Laboratory2, University of Illinois at Urbana-Champaign3
materialsdatafacility.org
globus.org
Materials Data Facility:
A Distributed Model for
the Materials Data Community
15 August 2017
2. The Materials Data Facility Team
2
UC/Argonne
Ian Foster (PI) Ben Blaiszik Steve Tuecke
Kyle ChardJim Pruyne
Logan Ward Jonathon Gaff
Illinois (Urbana-Champaign)
Rachana
Ananthakrishnan
John Towns (PI) Kenton McHenry
Michal Ondrejcek
Stephen Rosen
Ryan Chard
3. Data-Intensive Materials Science
3
Materials Databases High-Throughput Screening
Machine Learning Multi-scale Modeling
Kirklin et al. Acta Mat. (2016)
de Jong et al. Sci Rep. (2016) Sparks et al. Scr. Mat. (2015) https://www.mpg.de/
4. Data-Intensive Materials Science
4
Science is becoming limited by the ability to handle data
- Where to get it?
- How to selectively share it?
- Where to store it?
- How do know what it is?
- How to build software that uses it?
- How to get others to share theirs?
- How to keep track of provenance?
- ….?
Our goal is to create easy answers to these questions
5. Why create the MDF?
5
1. Make your data shareable
Custom access control, using institution credentials
2. Make your data open
Access to >100TB of storage space
3. Make your data accessible
Search across distributed resources
Automatic, domain-specific metadata extraction
4. Make your data computable
Tight integration with computing resources
5. Make your data valuable
Citable with DOIs, measured with usage stats
$
EP
6. What is the MDF?
EP
EP
EP
EP
Deep indexing
Query
Browse
Aggregate
Publish
Mint DOIs
Associate
metadata
Databases
Datasets
APIs
LIMS
etc.
Distributed data
storage
Data
publication
service
Data
discovery
service
8. Globus and the research data lifecycle
8
Researcher initiates
transfer request; or
requested automatically
by script, science
gateway
1
Instrument
Compute Facility
Globus transfers files
reliably, securely
2
Globus controls
access to shared
files on existing
storage; no need
to move files to
cloud storage!
4
Curator reviews and
approves; data set
published on campus
or other system
7
Researcher
selects files to
share, selects
user or group,
and sets access
permissions
3
Collaborator logs in to
Globus and accesses
shared files; no local
account required;
download via Globus
5
Researcher
assembles data set;
describes it using
metadata (Datacite
& domain-specific)
6
6
Peers, collaborators
search and discover
datasets; transfer and
share using Globus
8
Publication
Repository
Personal Computer
Transfer
Share
Publish
Discover
• Only a Web browser
required
• Use storage system
of your choice
• Access using your
campus credentials
8
9. Data sharing and Globus
9
Easily control who gains access to your data:
- Globus can use University/Laboratory credentials
- You can establish groups of authorized users
10. Data sharing and Globus
10
Simple to move data to/from any resource
14. What do I mean by “accessibility”?
Need: Simplify finding and acquiring materials data
Major Challenges:
1. Data spread across many resources
§ Have to search each repository individually
§ Different services, different APIs to get data
2. Contents of resources are poorly described
§ Lack domain-specific metadata
Goal: Linking together world’s materials data resources,
with enough metadata to make it useful
14
15. Part 1: Linking with the Data Community
15
Materials Project
Citrination
Materials
Commons
Other Facilities (APS, SNS, NSLS, …), Institutional Repositories,
Publishers!
Metadata
Publishing
MetadataMD,
Pub., Compute
Metadata
Publishing
NCSA-PIREHV/TMSMBDH
16. MDF data discovery ecosystem
EP
NIST
MRR
Data
discovery
service
Harvest
Deep index
Register / Sync
Services
Bots
MDF
Pub
Service
Automate
Process
Refine
Analyze
Data Output
Data Input
EP
Data Sources
Query
Browse
Aggregate
User Interfaces
Identify resources for indexing
16
17. MDF + NIST Database Tools
17
Data
discovery
service
MDCS
NIST
MRR
Ref: Dima, et al. JOM. 68 (2016), 2053. doi: 10.1007/s11837-016-2000-4
18. MDF + NIST Database Tools
18
Data
discovery
service
MDCS
NIST
MRR
MDF automates publicizing data
and provides a uniform search interface
19. Piping DFT data from MDF to Citrine
{ "category": "system.chemical",
"chemicalFormula": "MgO2",
"properties": {
"units": "eV", "name": "Band gap",
"scalars": [ { "value": 7.8 } ] } }
2. Bot requests open DFT data periodically
3. Bot accesses data, runs DFT parser to refine data
4. Push metadata to Citrine
1. User publishes DFT dataset
5. Ingest DFT data quality report
…
Our datasets are discoverable through many tools
19
20. Part 2: A Materials Data Search Engine
Goal: Simplify finding useful data
Key Issue: Lack of metadata
Approaches:
1. Simplifying metadata capture from the source
2. Extracting useful information from dataset
20
21. Route 1: Integrating with LIMS/Workflow
Tools
21
MAST
Materials Commons (MC)
T2C2 (4CeeD)
• Build connections to international materials
efforts and registries (e.g., NIMS, RDA, NIST,
EUDAT, NDS)
• Promote IMaD data services, tools, and
accomplishments to the community
• Develop video tutorials, webinars, and shared
code repositories
• Interface with the Materials Accelerator
Network (MAN)
• Engage with colleges, industry, and
consortiums
• (Wisconsin) Regional Materials and
Manufacturing Network (RM2N)
• (Illinois) Digital Manufacturing and
Design Innovation Institute DMDII
• (Michigan) LIFT consortium
Engagement
Linking Software and Services
PIs: I. Foster1,2, J. Allison3, D. Morgan4, D. Trinkle5, P. Voorhees6
1 University of Chicago 2 Argonne National Laboratory 3 University of Michigan 4 University of Wisconsin-Madison 5
University of Illinois at Urbana-Champaign, 6 Northwestern University
Overview
• NSF Midwest Big Data Spoke
22. • Argonne Leadership Computing Facility (>1000 users/year)
§ Working with datasets that comprise ~300M core hours, with 200M
more identified for near term
§ New joint effort to roll out MDF-like capabilities to ALCF users
• Advanced Photon Source (>5000 users/year)
• Building pipelines and procedures to index and publish data from
15 beamlines (~1/3 of the facility) in conjunction with the APS
software team (Schwartz)
• Advanced Light Source (>2000 users/year)
• Integration with CAMERA project and associated tomography
beamlines
Linking Data from Major Facilities
22Working with user facilities to facilitate capturing data/metadata
23. Ripple: Home automation for research data
Doi:10.1109/ICDCSW.2017.30 23
Procedure for automating tomography experiments:
At ALS: Detect new beamline data,
and transfer it to NERSC
At NERSC: Submit, run jobs on Edison,
transfer data back to ALS
At ALS: Create a shared endpoint,
notify collaborators of result via email
Automate capturing results and metadata
Ryan Chard
24. Route 2: Deep Indexing Materials Data
MDF
Index Data resources
indexed
116
Records
>3.4M
Repositories harvested
• MDF
• NIST MML Repo
• MATIN
• Materials
Commons
• CXIDB
• NIST Materials
Resource
Registry
6
~200 Datasets
~260 TB
Made
discoverable
24
25. Adding More Metadata to NIST MatDL
Dataset As Published
Limited Metadata
Querying Difficult
25
26. Adding More Metadata to NIST MatDL
Deep-Indexed into the MDF
Data Available Programmatically
26
27. Adding More Metadata to NIST MatDL
Deep-Indexed into the MDF
Can be used for scripting
27
28. Another benefit: domain-specific querying
Example service possible with DFT
data files
Answer questions like:
“Do we have any data about
anatase-TiO2?”
“Who else has studied Li-MnO3
batteries with DFT?”
Crystal Structure File
.cif, VASP, etc.
Entries from MDF that
are structurally-similar
28
29. Skluma: A Statistical Learning Pipeline
for Taming Unkempt Data Repositories
29
doi:10.1145/3085504.3091116
Goal: Build intelligent search indexes
with minimal human effort
Method: Employ machine learning
to extract metadata from file
repositories
- Classify data files
- Detect file types
Tyler Skluzacek
Search Otherwise-Unusable Data Repositories
30. MDF Forge python package (under development)
• Interface to MDF services
• Helper functions for common tasks
APIs, Automation, and Examples
https://github.com/materials-data-facility/forge
30
Tools for using these capabilities will be available soon
32. Computable Data
Reproducing data-driven science should be trivial
It often is not. Common problems:
§ If available, datasets lack documentation
§ Algorithms/methods are not open sourced
§ Models rarely published
§ Software installation/configuration require expertise
Our goal: Simplify publishing data-driven science
- Storing software and models
- Integrating them with compute resources
32
33. Integrating analytics tools with MDF
33
MATIN (GT)
~ 10 datasets
Used in
education
Result: Scientists connected with data, analytics tools,
and compute capability
MDF Data
Publication
MATIN (GT)
MML
Repository
(NIST)
Materials
Commons
(UM
PRISMS) Coherent X-Ray
Tomography
Database (LNL)
To End UsersTo End UsersTo Compute ResourcesFrom Data Repositories
Jetstream is a self-provisioned, scalable science and engineering cloud environment
operated by Indiana University for the National Science Foundation: jetstream-cloud.org
34. Building a machine learning model using MDF
A simple web service to train ML forcefields
34
36. Example: Building force-field potentials from different datasets
Data resources: 3 DFT datasets with Aluminum data
1 dataset from khazana.uconn.edu, 2 datasets from materialsdata.nist.gov
Result: Improved performance by integrating data sources
36
Building a machine learning model using MDF
Method: Botu et al. JPCC. (2017)
Using only original data
Training SetHoldout Set
37. Example: Building force-field potentials from different datasets
Data resources: 3 DFT datasets with Aluminum data
1 dataset from khazana.uconn.edu, 2 datasets from materialsdata.nist.gov
Result: Improved performance by integrating data sources
37
Building a machine learning model using MDF
Method: Botu et al. JPCC. (2017)
Using only original data
Training SetHoldout Set
Including Diffusion Dataset
38. Example: Building force-field potentials from different datasets
Data resources: 3 DFT datasets with Aluminum data
1 dataset from khazana.uconn.edu, 2 datasets from materialsdata.nist.gov
Result: Improved performance by integrating data sources
38
Building a machine learning model using MDF
Method: Botu et al. JPCC. (2017)
Using only original data
Training SetHoldout Set
Including Diffusion DatasetIncluding 𝐷 + 𝑇# Dataset
Better performance in original application: No new DFT calculations
39. • Summer Intern (Jiming Chen) reproducing and
extending materials and ML papers with the MDF
• Joined our team with the NSF WholeTale project
Reproducing data-driven MSE with MDF
Users publish data
to the MDF…
… and code to
WholeTale
Long-term goals:
- Assemble community-driven resource for ML tools/examples
- Use MDF/WholeTale to create benchmark challenges
Jiming Chen (UIUC)
39
41. • Publish and share models and code linked with full
training datasets
• Link database with HPC/Cloud computing resources
• Provide uniform interface for training, running models
DLHub: Advancing Deep Learning Adoption
43. What is the MDF?
EP
EP
EP
EP
Deep indexing
Query
Browse
Aggregate
Publish
Mint DOIs
Associate
metadata
Databases
Datasets
APIs
LIMS
etc.
Distributed data
storage
Data
publication
service
Data
discovery
service
43
44. Data publication service
44
• Mechanisms to create and enforce
schemas and logical collections
• Web UI to create datasets and manage
curation and admin tasks
• Tools to automate publication process
• Dataset record permanent landing page
for DOI link
• Record shows some metadata links to
the rest
• Direct link to underlying files
• Download statistics
45. Published Data Highlights
45
~ 30 datasets
~ 6.5 TB
MATIN (GT)
~ 10 datasets
Used in
education
X-ray Scattering Image Classification
Using Deep Learning
http://dx.doi.org/10.18126/M2Z30Z
Electron Backscattering and
Diffraction Datasets for Ni, Mg, Fe, Si
Yager et al.Marc De Graef et al.
Phase Field Benchmark I Dataset
Jokisaari et al.
Grain Structure, Grain-averaged Lattice Strains, and
Macro-scale Strain Data for Superelastic Nickel-
Titanium Shape Memory Alloy Polycrystal Loaded in
Tension
Paranjape et al.
• Largest dataset to date (>1.5 TB). Showcases MDF unique
capabilities and makes a unique dataset discoverable for code
development, analysis, and benchmarking
47. Streamline & automate data publication
12.5 TB
12.4 TB out
Data
Volumes
Publication
Authors
94
Institutions
14
Accesses
>1000
Total
datasets
50
CHiMaD
datasets
16
Pipeline CHiMaD
datasets
+14
Total
datasets
+30
48. Advantages of Globus Publish
Capable of handling large datasets
§ Publish data in place
§ Integration with Globus Transfer/HTTPS
Deep indexing of materials-specific metadata
§ Parse common materials data types
§ Make data searchable on the file-level
Automatically re-publishing data elsewhere
§ Publishing dataset metadata to MRR, Google Scholar, etc.
§ Sending fine-grained metadata to other databases (e.g., Citrine)
In Progress: Know how often your data is used
§ Track when it is used in analytics tools
48
All of these capabilities increase the value of your data
49. Why create the MDF?
http://materialsdatafacility.org 49
1. Make your data shareable
Custom access control, using institution credentials
2. Make your data open
Access to >100TB of storage space
3. Make your data accessible
Search across distributed resources
Automatic, domain-specific metadata extraction
4. Make your data computable
Tight integration with computing resources
5. Make your data valuable
Citable with DOIs, measured with usage stats
$
EP
50. Thanks to our sponsors!
50
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