Supporting the exploding dimensions of the chemical sciences via global networking
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The Royal Society of Chemistry is building is a comprehensive federated platform for chemical informatics in a Big Research Data world. The resulting platform is a blend of social, informatics and knowledge components which itself produces new dimensions in the chemical sciences to support activities such as Open Innovation and sharing of data. The platform itself would be isolated and insular unless a broad collaboration between societies, industry and universities is created in a federated and open way. In this presentation we will talk about one of these efforts, between RSC and Moscow State University, to facilitate the development, population and use of a global networking platform for the chemical sciences.
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There is an increasing availability of free and open access resources for scientists to use on the internet. Coupled with the increasing availability of Open Source software tools we are in the middle of a revolution in data availability and tools to manipulate these data. ChemSpider is a free access website for chemists built with the intention of providing a structure centric community for chemists. As an aggregator of chemistry related information from many sources, at present over 21.5 million unique chemical entities from over 200 separate data sources, ChemSpider has taken on the task of both robotically and manually curating publicly available data sources. This presentation will provide an overview of the ChemSpider platform and how it is fast becoming the centralized hub for resourcing information about chemical entities. Building an integrated system for chemistry markup and online publishing inte...
Building an integrated system for chemistry markup and online publishing inte...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The extraction of chemical entities from documents such as patents and publications has been pursued for a number of years. We wish to report on ChemMantis, an integrated system for chemistry-based entity extraction and document mark-up enabling access to the rich resource of online chemistry know as ChemSpider. We will discuss the development of the platform from its inception as a series of dictionaries to the integration of an entity extraction algorithm and its expansion to a public deposition and publishing platform for chemistry. Chemistry articles can now be deposited, marked-up and exposed to the public within a few minutes in many cases making it an ideal platform for communicating research and providing integrated access to data sources including PubChem, ChEBI, Wikipedia and Entrez.Citizen Scientists and Their Contributions to Internet Based Chemistry
Citizen Scientists and Their Contributions to Internet Based ChemistryUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
This is a presentation I gave to a group of students at Drexel University via webex and skype. This is the Powerpoint presentation component but it was intersected with a live demo of multiple online databases. The movie of the presentation is online at SciVee here: http://www.scivee.tv/node/14770eScience Resources for the Chemistry Community from the Royal Society of Chem...
eScience Resources for the Chemistry Community from the Royal Society of Chem...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Our access to scientific information has changed in ways that were hardly imagined even by the early pioneers of the internet. The immense quantities of data and the array of tools available to search and analyze online content continues to expand while the pace of change does not appear to be slowing. ChemSpider is one of the chemistry community’s primary online public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data tens of thousands of chemists every day and it serves as the foundation for many important international projects to integrate chemistry and biology data, facilitate drug discovery efforts and help to identify new chemicals from under the ocean. This presentation will provide an overview of the expanding reach of the ChemSpider platform and the nature of the solutions that it helps to enable. We will also discuss the possibilities it offers in the domain of crowdsourcing and open data sharing. The future of scientific information and communication will be underpinned by these efforts, influenced by increasing participation from the scientific community and facilitated collaboration and ultimately accelerate scientific progress.eScience at the Royal Society of Chemistry and our current initiatives
eScience at the Royal Society of Chemistry and our current initiativesUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Access to scientific information has changed in a manner that was likely never even imagined by the early pioneers of the internet. The quantities of data, the array of tools available to search and analyze, the devices and the shift in community participation continues to expand while the pace of change does not appear to be slowing. ChemSpider is one of the chemistry community’s primary online public compound databases. Containing tens of millions of chemical compounds and its associated data ChemSpider serves data tens of thousands of chemists every day and it serves as the foundation for many important international projects to integrate chemistry and biology data, facilitate drug discovery efforts and help to identify new chemicals from under the ocean. This presentation will provide an overview of the expanding reach of this eScience cheminformatics platform and the nature of the solutions that it helps to enable including structure validation, text mining and semantic markup, the National Chemical Database Service for the United Kingdom and the development of a chemistry data repository. We will also discuss the possibilities it offers in the domain of crowdsourcing and open data sharing. The future of scientific information and communication will be underpinned by these efforts, influenced by increasing participation from the scientific community and facilitated collaboration and ultimately accelerate scientific progress.ChemSpider hosting linking and curating chemistry data for the community
ChemSpider hosting linking and curating chemistry data for the communityUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The Internet is the world’s publicly accessible container for a myriad of resources containing chemistry related data. Whether it be collections of millions of chemical compounds with their associated properties, interactive displays for analytical data, access to publications and patents or tapping into the increasing availability of online computational engines, the web has became the primary enabling technology to source information and data. Scientists collectively applaud and utilize the availability of such resources and an increasing proportion of the community are willing to support these resources by contributing both their data and skills to help curate and validate information on the web. This “crowdsourcing” has started to contribute large amounts of data to the commons and serves has a valuable platform for reference and, potentially, discovery.
ChemSpider is one of the chemistry community’s primary online resources and allows scientists to search across 25 million unique chemical compounds linked out to over 400 original data sources and has become a central hub for searching for chemistry-related data. The platform however offers much more to the community and has become a central repository for analytical data, specifically spectra, is a host for community-authored chemical syntheses and facilitates data curation and annotation by any of its users. This presentation will provide an overview of the ChemSpider platform in terms of available data and its efforts to act as a public repository and clearing ground for data curation. We will discuss how such a platform, when coupled with game-based approaches, facilitates both teaching and data validation and will discuss whether public domain resources such as ChemSpider will ultimately become authorities for chemistry.
Our dire need to mandate data standards and expectations for scientific publi...
Our dire need to mandate data standards and expectations for scientific publi...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
This is a presentation that I delivered at the ACS Division of Chemical Information meeting regarding "Reproducibility, Reporting, Sharing & Plagiarism".
I took the opportunity to remove my hat that has me be the VP of Strategic Development at RSC, and a member of the cheminformatics group that built ChemSpider and works on other RSC projects related to it. Instead I presented on how a LACK OF MANDATES from publishers on me in terms of submission of data accompanying articles I am involved with writing is actually weakening my scientific record as data is not getting shared in the most useful forms possible to the benefit of the community. I think there would be benefits for publishers to start pushing me for MORE data, in fairly general standards, and allowing me (and others) to download the data in the form of molecules (and collections), spectral data, CSV files etc.Navigating the Complex Web of Chemistry Using ChemSpider
Navigating the Complex Web of Chemistry Using ChemSpiderUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The internet has revolutionized the sharing of data and information and in the domain of chemistry there are many resources available to help with our research. In recent years various online resources have been introduced that allow users to access information, properties and data associated with chemical entities. At a time when CAS has declared that they now have over 50 million unique chemical entities in the registry the number of chemical structures distributed across the internet also measures in the tens of millions. There are many tens of databases on the internet hosting chemical structures associated with data focused on the specific nature of the collection – metabolic pathways, spectral data collections, chemical vendor collections, biological assay data and crystal structures are examples. Unfortunately there has been no single way to search across all of these resources. ChemSpider has taken on the task of integrating the multiple online resources of information into a single database using the chemical structure as the primary key and retaining the link out and attribution to the original datasource. In this manner ChemSpider intends to become a structure-centric hub for the chemistry community. This talk will provide an overview of the ChemSpider platform, how it is being used as a crowdsourcing platform for community-based curation of the data and the future vision of ChemSpider as one of the pillars of the semantic web of chemistry.Value of the mediawiki platform for providing content to the chemistry community
Value of the mediawiki platform for providing content to the chemistry communityUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
At this time, and in a culture where online access is now an imperative, Wikipedia has become the definitive encyclopedia. In terms of its support for chemistry it is rich in many encyclopedic pages including named reactions, chemical and drug pages, articles about chemists, and many other forms of chemistry related information. Wikipedia is hosted on Mediawiki, an open source platform that can be utilized by anybody as the basis of their own hosted content collection. Mediawiki has been used as a collaborative environment by a number of chemists to create As a general contribution to the community Mediawiki has been used to create a number of resources that have become very popular with the chemistry community. These include VIPEr to support inorganic chemistry, ChemWiki as an online textbook and other educational resources and a Chemical Information Wikibook. Mediawiki has also been used by the author to host open source collections of data including scientists, scientific databases and mobile apps for science: the ScientistsDB, SciDBs and SciMobileApps wikis. This presentation will provide an overview of some of the chemistry resources that presently exist and celebrate the major contributions that Wikipedia and Mediawiki have made to the collaborative dissemination of chemistry. Dealing with the complex challenge of managing diverse analytical chemistry d...
Dealing with the complex challenge of managing diverse analytical chemistry d...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The Royal Society of Chemistry provides open access to data associated with tens of millions of chemical compounds. The richness and complexity of the data has continued to expand dramatically and the original vision for providing an integrated hub for structure-centric data has been delivered across the world to hundreds of thousands of users. With an intention of expanding the reach to cover more diverse aspects of chemistry-related data including compounds, reactions and analytical data, to name just a few data-types, we are in the process delivering a Chemistry Data Repository. The data repository will manage the challenges of associated metadata, the various levels of required security (private, shared and public) and exposing the data as appropriate using semantic web technologies. Ultimately this platform will become the host for all chemicals, reactions and analytical data contained within RSC publications and specifically supplementary information. This presentation will report on the challenges of managing “Big Data” for chemists around the world and providing access to tools for structure dereplication, spectral database searching and the crowdsourcing of the worlds’ largest spectral database.RSC ChemSpider – Building An Internet Based Community For Chemists
RSC ChemSpider – Building An Internet Based Community For ChemistsUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
This is a general presentation about our efforts to build an internet based community for chemists using ChemSpider. A general overview of data quality online, crowdsourced deposition and curation and our progress to deliver a solution to the community for resourcing data.Cheminformatics and the Structure Elucidation of Natural Products
Cheminformatics and the Structure Elucidation of Natural ProductsUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The structure elucidation of natural product structures from analytical data, specifically NMR and MS, remains a major challenge. With an enormous palette of NMR experiments to choose from, and supported by breakthrough technologies in hardware, the generation of high quality data to enable even the most complex of natural product structures to be determined is no longer the major hurdle. The challenge is in the analysis of the data. We are in a new era in terms of approaches to structure elucidation: one where computers, databases, and a synergy between scientists and algorithms can offer an accelerated path forward. Software tools are capable of digesting spectroscopic data to elucidate extremely complex natural products. Scientists can now elucidate chemical structures utilizing multinuclear chemical shift data, correlation data from an array of 2D NMR experiments and utilize existing data sets for the purpose of dereplication and computer-assisted structure elucidation. With the explosion of online data especially, in public databases such as PubChem and ChemSpider, many tens of millions of chemical structures are available to seed fragment databases to include in the elucidation process. This presentation will provide an overview of how cheminformatics and chemical databases have been brought together to assist in the identification of natural products. It will include an examination of the state-of-the-art developments in Computer-Assisted Structure Elucidation. Overview of open resources to support automated structure verification and e...
Overview of open resources to support automated structure verification and e...US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
Cheminformatics methods form an essential basis for providing analytical scientists with access to data, algorithms and workflows. There are an increasing number of free online databases (compound databases, spectral libraries, data repositories) and a rich collection of software approaches that can be used to support automated structure verification and elucidation, specifically for Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). This presentation will provide an overview of freely available data, tools, databases and approaches available to support chemical structure verification and elucidation and highlight some of the known issues regarding data quality and suggest approaches for resolving some of the issues. The importance of structure and spectral standards for data exchange will be discussed, especially with regard to how spectral data can be made openly available to the community via online tools and through scientific publishing. This work does not necessarily reflect U.S. EPA policy.Crowdsourced Curation of Chemistry Data. How Bad is Online Chemistry Data?
Crowdsourced Curation of Chemistry Data. How Bad is Online Chemistry Data?US Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
This presentation was given at the Wolfram Data Summit in Washington DC on Sept 9th 2010 as part of a panel series of presentations and discussions on crowdsourcing approaches for data. It was a rant by me on the quality of what's online and questioning "who cares".Dealing with the complex challenge of managing diverse chemistry data online
Dealing with the complex challenge of managing diverse chemistry data onlineUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
The Royal Society of Chemistry has provided access to data associated with millions of chemical compounds via our ChemSpider database for over 5 years. During this period the richness and complexity of the data has continued to expand dramatically and the original vision for providing an integrated hub for structure-centric data has been delivered across the world to hundreds of thousands of users. With an intention of expanding the reach to cover more diverse aspects of chemistry-related data including compounds, reactions and analytical data, to name just a few data-types, we are in the process of implementing a new architecture to build a Chemistry Data Repository. The data repository will manage the challenges of associated metadata, the various levels of required security (private, shared and public) and exposing the data as appropriate using semantic web technologies. Ultimately this platform will become the host for all chemicals, reactions and analytical data contained within RSC publications and specifically supplementary information. This presentation will report on how our efforts to manage chemistry related data has impacted chemists and projects across the world and will review specifically our contributions to projects involving natural products for collaborators in Brazil and China, for the Open Source Drug Discovery project in India, and our collaborations with scientists in Russia.
Short Overview of ChemSPider at Drexel University
Short Overview of ChemSPider at Drexel UniversityUS Environmental Protection Agency (EPA), Center for Computational Toxicology and Exposure
This presentation was written to deliver before the live overview of CHemSPider. The live presentation is captured here on video: http://www.chemspider.com/blog/my-full-presentation-at-drexel-university.htmlWhat's hot (20)
Taming The Wild West Of Internet Based Chemistry You Can Help
Taming The Wild West Of Internet Based Chemistry You Can Help
Text Mining for Chemistry and Building a Public Platform for Document Markup
Text Mining for Chemistry and Building a Public Platform for Document Markup
Connecting Chemists to the Internet Through ChemSpider
Connecting Chemists to the Internet Through ChemSpider
How an Online Resource for Chemistry Can Change Our World
How an Online Resource for Chemistry Can Change Our World
Navigating the Complex Web of Chemistry Using ChemSpider
Navigating the Complex Web of Chemistry Using ChemSpider
Building an integrated system for chemistry markup and online publishing inte...
Building an integrated system for chemistry markup and online publishing inte...
Citizen Scientists and Their Contributions to Internet Based Chemistry
Citizen Scientists and Their Contributions to Internet Based Chemistry
eScience Resources for the Chemistry Community from the Royal Society of Chem...
eScience Resources for the Chemistry Community from the Royal Society of Chem...
eScience at the Royal Society of Chemistry and our current initiatives
eScience at the Royal Society of Chemistry and our current initiatives
ChemSpider hosting linking and curating chemistry data for the community
ChemSpider hosting linking and curating chemistry data for the community
Our dire need to mandate data standards and expectations for scientific publi...
Our dire need to mandate data standards and expectations for scientific publi...
Navigating the Complex Web of Chemistry Using ChemSpider
Navigating the Complex Web of Chemistry Using ChemSpider
Value of the mediawiki platform for providing content to the chemistry community
Value of the mediawiki platform for providing content to the chemistry community
Dealing with the complex challenge of managing diverse analytical chemistry d...
Dealing with the complex challenge of managing diverse analytical chemistry d...
RSC ChemSpider – Building An Internet Based Community For Chemists
RSC ChemSpider – Building An Internet Based Community For Chemists
Cheminformatics and the Structure Elucidation of Natural Products
Cheminformatics and the Structure Elucidation of Natural Products
Overview of open resources to support automated structure verification and e...
Overview of open resources to support automated structure verification and e...
Crowdsourced Curation of Chemistry Data. How Bad is Online Chemistry Data?
Crowdsourced Curation of Chemistry Data. How Bad is Online Chemistry Data?
Dealing with the complex challenge of managing diverse chemistry data online
Dealing with the complex challenge of managing diverse chemistry data online
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Introduction to Complex Systems
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How SADI & SHARE help restore the Scientific Method to in silico science
This is my presentation to the Bio Open Source Convention (BOSC) in Boston, July 2010. I start with a brief status-update on the BioMoby project and then launch into a series of demonstrations of it's successor - SADI + SHARE. Rather than discussing how SADI/SHARE work, I focus the discussion on what role I think these technologies can play in bringing the traditional "scientific method" back into in silico biology.
An introduction to support vector machines
These are my slides about SVM's. They are based in the Chapter on SVM's at Simon Haykin's book in Neural Networks 2nd edition. They have been augmented using the idea that Lagrange was a physicist allowing him to create the first intuition about dual problems.
Rapid biomedical search
The ContentMine system (Open Source) can search EuropePMC and download hundreds of articles in seconds. These can be indexed by AMI dictionaries allowing a rapid evaluations and refinement of the search
Science is built on trust.
Science builds on itself, and the enormous scientific progress of the last several centuries has been due largely to the trust that scientists place in one another and the trust the public places in scientists. I explore the responsibility that researchers have toward one another and to the world at large.
How to use science maps to navigate large information spaces? What is the lin...
A. Scharnhorst (2016) Wie können Wissenschaftskarten zur Suche in grossen Informationsräumen eingesetzt werden? How to use science maps to navigate large information spaces? What is the link between science maps and predictive models of science? Invited lecture Fraunhofer-Institut für Naturwissenschaftlich-Technische Trendanalysen, Euskirchen, Germany, December 7, 2016
Brain gut Command Control Center +
It is becoming increasingly evident that bidirectional signalling exists between the gastrointestinal tract and human brain, often involving the gut microbiota.Insights into this gut-brain crosstalk have revealed a complex communication system that not only ensures the proper maintenance of gastrointestinal homeostasis, but is likely to have multiple effects on motivation, and higher cognitive functions & age related afflictions.
ContentMine: Mining the Scientific Literature
In a meeting run by OSC at Cambridge 2017-07-12 I present the current capabilities of contentmine.org. This includes advocacy, tools and community.
Unit 1 - What Is Science?
Unit 1 for the "Values in Science and Technology" module of the "Systems of Knowledge" course.
This unit covers the following topics:
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Uc Admission Essays. Trending Uc Essay Examples 2017 most complete - scholarship
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Politics and Pragmatism in Scientific Ontology Construction
My talk at the Inconsistency Robustness 2011 Workshop...as usual, trying to fit the entire universe into one paper.
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09 Machine Learning - Introduction Support Vector Machines
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How SADI & SHARE help restore the Scientific Method to in silico science
How SADI & SHARE help restore the Scientific Method to in silico science
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Uc Admission Essays. Trending Uc Essay Examples 2017 most complete - scholarship
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Politics and Pragmatism in Scientific Ontology Construction
Politics and Pragmatism in Scientific Ontology Construction
More from Valery Tkachenko
Evolution of public chemistry databases: past and the future
Over the last few years we have seen a tremendous growth in various chemical databases. As a result we have now a variety of scientific resources, combined into a broad network and indexed through the directories like BioSharing and re3data. Such network, while growing quickly, is still in early days of adopting semantic web standards and does not yet support deep data indexing and discoverability, leave alone that mechanisms of intellectual properties protection are as simple as making data public or private at best. The lack of standards and well defined models to describe a scientific information structure even further inhibits free information flow which is essential for scientific discovery.
In this talk we will share our experience spanning through decades of building chemical databases like PubChem, ChemSpider, OpenPHACTS and National Database Services and will outline fundamental problems associated with chemical databases as such as well as data quality and approaches for the modern architecture of the large-scale chemical databases.
In silico design of new functional materials
Materials design is a grand challenge of materials science. And the main approach for solving this problem is still intuition-based. Such a way requires a lot of time and financial resources and months to years of conducting the experiment and doing characterization. Therefore, any kind of model that can be used at the very first stage of materials design and can narrow the selection area is a helpful tool for synthetic chemist. Also, an automated search for materials with human-defined target properties in the entire chemical space, i.e. inverse materials design is a highly desired tool in the exploration of materials design space.
Along with that, de novo design is not a kind of a completely new task in a field of development of new organic molecules with target properties. A lot of different generative approaches are being used along with screening the libraries of existing molecules, searching for drugs for a particular target, or generating new ones based on a very simple initial structure.
Here we would like to present a new approach for generating new materials with desired properties. We used autoencoder neural network architecture to encode materials composition and crystal structure as a vector in a latent space. In such case, any Quantitative Structure-Property Relationship (QSPR) model based on the vector can be interpreted as function in the latent space and can be used to predict property of existing materials as well as for prophetic ones. Such an approach has comparable accuracy with such classic computational methods as DFT in the case of predicting values of energies or charges, but significantly transcends them in terms of computational time.
The proposed method was tested for generating super-firm materials, but can easily be extended to any target properties, granted a database of materials properties can be provided for training.
Metal-organic frameworks: from database to supramolecular effects in complexa...
Metal-organic frameworks (MOFs) attract a lot of interest due to their unique structure-dependent properties. Their internal pores comparable to the size of small molecules are naturally refined for various absorbance effects. Possessed properties lie in a foundation of multiple applications, such as catalysis, gas storage/separation and especially – clean energy related ones.
Theoretical calculations are a usual way of decreasing experimental costs while investigating properties of new materials, especially at a design stage. Electronic structure calculations like density functional theory (DFT) in most cases provide an appropriate accuracy in matching experimentally measured data such as adsorbate interaction energies. However, as in the case of experimental studies, large-scale materials screening studies with DFT calculations are rather time-consuming, and it can be carried out only for structures with relatively small unit cell.
Here we would like to present a theoretical and experimental results describing calculation of electron density in metal-organic frameworks. We built a model trained to predict partial charges on MOF atoms based on DFT calculations. The relative error of the model allows us to conclude that models do not decrease the level of accuracy and do not superinduce additional error comparing to DFT. At the same time, computational cost of the model is several orders of magnitude less. Models also demonstrated transferability and allowed to make prediction e.g. for MOFs containing metals not presented in the train set.
We have also built a force-field (FF) of two-centered and three-centered interatomic potentials constructed using predicted charges. The FF proved to reproduce MOF crystal structure. As a final test, we have applied the developed model and FF to a new synthesized lanthanide-containing MOFs to estimate influence of supramolecular effects on metal complexation selectivity.
As a result, we’ve built a model predicting one of basic MOF properties within relatively small computational time and tested it on experimental data, both obtained from literature sources and self-investigated.
Abstract recommendation system: beyond word-level representations
Public repositories containing diverse chemical and biological data are one of the main sources of knowledge for further biomedical research. Unfortunately, extraction and transforming these data into a well-interpretable form is a complex exercise. Ongoing efforts of a community are mainly focused on the analysis of co-occurrences of terms, text annotation based on terms similarity and related tasks [1].
Here we present an approach based on natural-language processing techniques, which is intended to shift the focus of a search for similar texts on chemical topics from word- to document-level. PubMed records were used to implement word2vec and doc2vec models. Generated text representations can be used to search for similar abstracts; the similarity is more dependent on this representation than the co-presence of certain terms (neighbor compounds, similar publication date, etc.).
Document-level clustering was also implemented to provide insight into the PubMed text corpus structure. This approach can serve as an alternative to standard topic modeling techniques for the discovery of hidden semantic features in an unsupervised manner.
Machine learning methods for chemical properties and toxicity based endpoints
In the last decade there is an increasing interest in using in silico tools for potential risk assessment of newly released chemicals due to the large number of chemicals enter the market yearly and the big uncertainty on their possible hazardous effects. Different tools and methods based on machine learning techniques already exist and were used in a wide range of applications starting from quantitative structure-property relationships and expanding into predictive toxicology. There is a lot of historical data accumulated across multiple databases which is publicly available and can be used with novel machine learning methods. Unfortunately, due to different datasets, metrics and validation strategies, the significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. This work is an attempt to develop a multitask system which can serve as searchable curated collections of multiple chemical datasets and ready to use novel machine learning methods solely built using open source frameworks and libraries. We have implemented a set of self-tuned, using grid search and k-fold validation, traditional machine learning methods (shallow methods) such as Naïve Bayes, k-Nearest Neighbors, Random Forest, Boosted Decision Trees, Regularized Logistic Regression, and Support Vector Machines base on open source Scikitlearn (http://scikit-learn.org/stable/). The novel Deep Neural Networks models of different complexity have been also implemented using Keras (https://keras.io/), a deep learning open library, and a Tensorflow (www.tensorflow.org) as a backend. The machine learning models were trained and evaluated to predict measures of toxicity from the physical characteristics of the structure of chemicals using the same datasets as in the Toxicity Estimation Software Tool (https://www.epa.gov/chemical-research/toxicity-estimation-software-tool-test). The Deep Learning models showed very good performance evaluation characteristics and were found to be useful in predicting of toxicological and physicochemical parameter endpoints. The results of this work support an optimistic view that some of current obstacles in cheminformatics can be overcome by using Deep Learning methods.
Chemical workflows supporting automated research data collection
Acquisition of data from public sources is inefficient, time consuming and limited in scope. The NIH has recently posted its intention to financially support data deposition by investigators through the ‘data sharing plan' for each funded proposal. However, this plan also points to a current weakness of the centralized data sharing and acquisition as all laboratories use different data collection and formatting approaches. These inconsistencies in data formatting by individual labs leads to the need to invest significant resources in data curation and interpretation by the technical staff involved in the maintenance of the centralized data collection resource such as CaNanoLab or Nanomaterial Registry. It would be far more efficient and useful if there were a standardized data collection and deposition template with standard key terms (such as Minimal Information about Nanomaterials, MIAN) that could be modified to add new or important additional data or parameters for each investigator. These new features cold be ultimately adopted in the classification scheme and guide the scope of the expanding database. This approach would be a win-win as it would enable structure for the investigators laboratory, consistency in data reporting and a means of transmitting data to the database in parallel to publication to eliminate the acquisition step from the process. In this talk we will outline our experience building Open Science Data Repository, a federated database system for direct acquisition, curation and management of research data, including nanomaterial data capture, transformation, and streamlined submission to nanomaterial knowledgebases. The key part of the system is microservices based architecture which exposes RESTful API suitable for direct integration into Workflow Management Systems as well as built-in modules facilitating and enforcing various lab-specific standard operating procedures.
Deep learning methods applied to physicochemical and toxicological endpoints
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
Deep Learning on nVidia GPUs for QSAR, QSPR and QNAR predictions
While we have seen a tremendous growth in machine learning methods over the last two decades there is still no one fits all solution. The next era of cheminformatics and pharmaceutical research in general is focused on mining the heterogeneous big data, which is accumulating at ever growing pace, and this will likely use more sophisticated algorithms such as Deep Learning (DL). There has been increasing use of DL recently which has shown powerful advantages in learning from images and languages as well as many other areas. However the accessibly of this technique for cheminformatics is hindered as it is not available readily to non-experts. It was therefore our goal to develop a DL framework embedded into a general research data management platform (Open Science Data Repository) which can be used as an API, standalone tool or integrated in new software as an autonomous module. In this poster we will present results of comparing performance of classic machine learning methods (Naïve Bayes, logistic regression, Support Vector Machines etc.) with Deep Learning and will discuss challenges associated with Ddeep Learning Neural Networks (DNN). The DNN learning models of different complexity (up to 6 hidden layers) were built and tuned (different number of hidden units per layer, multiple activation functions, optimizers, drop out fraction, regularization parameters, and learning rate) using Keras (https://keras.io/) and Tensorflow (www.tensorflow.org) and applied to various use cases connected to prediction of physicochemical properties, ADME, toxicity and calculating properties of materials. It was also shown that using nVidia GPUs significantly accelerates calculations, although memory consumption puts some limits on performance and applicability of standard toolkits 'as is'.
Using publicly available resources to build a comprehensive knowledgebase of ...
There is a variety of public resources on the Internet which contain information about various aspects of chemical, biological and pharmaceutical domains. The quality, maturity, hosting organizations, team sizes behind these data resources vary wildly and as a consequence content cannot be always trusted and the effort of extracting information and preparing it for reuse is repeated again and again at various levels. This problem is especially serious in applications for QSAR, QSPR and QNAR modeling. On the other hand authors of this poster believe, based on their own extensive experience building various types of chemical, analytical and biological databases for decades, that the process of building such knowledgebase can be systematically described and automated. This poster will outline the work performed on text and data-mining various public resources on the Web, data curation process and making this information publicly available through a portal and a RESTful API. We will also demonstrate how such knowledgebase can be used for real-time QSAR and QSPR predictions.
Need and benefits for structure standardization to facilitate integration and...
There are a large number of US government databases housing diverse collections of chemical data including bioassay data (PubChem), toxicity data (CompTox Chemistry Dashboard) and environmental data (a large collection of EPA databases), to name just a few. In many cases integration between the databases, at the chemical structure level, is via alphanumeric text identifiers such as CAS Numbers, or via InChI (International Chemical Identifiers). Structure-based integration is hyper-dependent on the initial inputs providing the chemical structures to the InChI generation algorithm. To ensure optimal integration between various databases, community standards and agreement regarding standardization of chemical structures would be beneficial, not only to integration of US government databases and resources but also to the international scientific community and hosts of online databases. This presentation will discuss our progress to deliver a fully Open Source chemical standardization platform as an exemplar for the community to build on and enhance. The system utilizes the CDK (Chemistry Development Kit), RD Kit and other open source components. The resource expands on our previous work regarding the Chemical Validation and Standardization Platform and has been tested using the open data collection provided by the EPA Comptox Chemistry Dashboard.
Development and comparison of deep learning toolkit with other machine learni...
The next era of cheminformatics and pharmaceutical research in general is focused on mining the heterogeneous big data, which is accumulating at ever growing pace, and this will likely use more sophisticated algorithms such as deep learning. There has been increasing use of deep learning which has shown powerful advantages in learning from images and languages as well as many other areas. However the accessibly of this technique for cheminformatics is hindered as it is not available readily to non-experts, it is currently not in any of the major cheminformatics tools. It is therefore our goal to develop a deep learning algorithm and toolkit which can be used as a standalone or integrated in new software being developed by us such as the Open Science Data Repository (OSDR). We will show how classic machine learning (CML) methods (Naïve Bayes, logistic regression, Support Vector Machines etc.) compares to cutting edge deep learning and talk about challenges associated with deep neural networks (DNN) learning models. The open source Scikit-learn (http://scikit-learn.org/stable/) ML python library was used for building, tuning, and validating all CML models. The DNN learning models of different complexity (up to 6 hidden layers) were built and tuned (different number of hidden units per layer, multiple activation functions, optimizers, drop out fraction, regularization parameters, and learning rate) using Keras (https://keras.io/), a deep learning library, and Tensorflow (www.tensorflow.org) as a backend. All the developed pipelines consist of stratified splitting of the input dataset into train (80%) and test (20%) datasets. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were computed for each model for ADME/Tox and other physicochemical properties. DNN learning models were found to be very good in predicting activities and can outperform most of the CML models.
Living in a world of federated knowledge challenges, principles, tools and ...
Over years a multitude of chemical formats and approaches were created to address various aspects of handling chemical information and building databases of chemical knowledge. As a result the current state of this landscape is severely affected by the lack of well-accepted and community-recognized formats, protocols, metadata standards, validation routines and standards in handling, storing and representation, lack of open toolkits which conform to the same standards as well as the lack of platforms which allow interactive and collaborative work to solve all the above problems. While such organizations as RDA and IUPAC as well as some government agencies and institutes are concerned and trying to address the problem it is still a severe pain point. In this presentation we will talk about our experience of building a federated knowledgebase called Open Science Data Repository which supports deposition of raw and structured chemical and analytical data in various formats, runs validation and standardization protocols, is build in a highly modular way that allows using both its API and its components in a Cloud or to be deployed on premises behind firewalls and supports a variety of use cases including collaborative data curation, rich analytics and visualization, real-time machine learning, formats conversion and preparing depositions into PubChem and ChemSpider from a variety of sources and fully supports FAIR principles for research data.
Open chemistry registry and mapping platform based on open source cheminforma...
The Open PHACTS project (openphacts.org) is a European initiative, constituting a public–private partnership to enable easier, cheaper and faster drug discovery. The project is supported by the OpenPHACTS Foundation (www.openphactsfoundation.org) and funded by contributions from several pharmaceutical companies. As part of Open PHACTS, a 'Chemical Registration Service” was created to register chemicals of interest to the project, allowing compound linkage between data sets. A key concept is the support for 'scientific lenses,' which allows hierarchical mapping of chemical entities, including supporting characteristics such as charge state, tautomerism and stereochemistry. Open PHACTS aggregated various databases, including ChEMBL, ChEBI, HMDB, DrugBank, PDB, MeSH, and WikiPathways. A new project builds on the Chemical Registration Service to establish an open chemistry registry and mapping service for general data set linkage. This expansion requires the support of multiple cheminformatics formats, the conversion and mapping of various identifiers, harmonized but configurable standardization, validation of the chemical structures, and the creation of new identifiers, to produce scientific lenses, or 'link sets'. Furthermore, these identifiers will be related to the compounds chemical names (IUPAC and trivial) and related chemical structures. This presentation will describe our ongoing work to create a fully open source, easy to install platform, which supports the ideas introduced by the Open PHACTS project and expands it with community data including, for example, the data now available from the EPA CompTox Chemistry Dashboard (comptox.epa.gov). This new platform supports chemical formats and provides for identifier conversion and cross-validation between datasets. The project is completely based on open source cheminformatics toolkits and available as a set of libraries, docker images and a web frontend based on FAIR and Open Data principles. The openness of this platform will allow for scientists to process their own datasets, and make them interoperable with other online chemical databases.
Using the structured product labeling format to index versatile chemical data
Structured Product Labeling (SPL) is a document markup standard approved by the Health Level Seven (HL7) standards organization and adopted by the FDA as a mechanism for exchanging product and facility information. Product information provided by companies in SPL format may be accessed from the FDA Online Label Repository (labels.fda.gov) and the National Library of Medicine DailyMed web site (dailymed.nlm.nih.gov). FDA also maintains and publishes SPL Indexing Files for Pharmacologic Class, Substance, Product Concept, Biological Drug Substance, and Billing Units. Data from the Indexing Files can be linked to data in both SPL resources and external resources via chemical and non-chemical identifiers. In this talk we will present on the latest addition to SPL which allows indexing data on proteins, polymers and structurally diverse substances. We will also discuss the potential value of SPL to the integration between public chemistry databases, especially those hosted by the United States Government.
Tools and approaches for data deposition into nanomaterial databases
Sustainable research progress in many scientific disciplines critically depends on the existence of robust specialized databases that integrate and structure all available experimental information in the respective fields. The need for such reference database is especially critical for nanoscience and nanomaterial research given the significant diversity of shapes, sizes, and properties of engineered nanomaterials and the difficulty of synthesizing engineered nanoparticles with controlled properties. The acquisition of data from public sources is inefficient, time consuming and limited in scope. Moreover, it is not clear where the resources come from to support this activity on a perpetual basis. The NIH has recently posted its intention to provide special funds toward data deposition by the experimental investigators through the ‘data sharing plan’ for each proposal. However, this points to a current weakness which is that all laboratories use different data collection approaches each of which requires interpretation by staff hosting the database. It would be far more efficient and useful if a template with key terms that could be modified to add new or important additional data or parameters for each investigator. We will discuss tools and approaches to facilitate collection and direct deposition of experimental data into Nanomaterial Registry (https://www.nanomaterialregistry.org/) - a versatile semantically enriched templates-based platform for registering diverse data pertaining to nanomaterials research.
Chemistry Validation and Standardization Platform v2.0
In recent years there has been explosive growth in the number of public chemical databases available online, a number of these containing 10s of millions of chemical structures. Examples include PubChem, ChemSpider and ChEMBL and users of these databases have become increasingly aware of the issue of data quality associated with these public resources. Seamless integration and mapping between databases, even for some common chemicals, is challenged by differing approaches to chemical standardization prior to registration into a database. The lack of standards in representing and handling chemical information certainly contributes to aspects of this problem. The Chemistry Validation and Standardization Platform (CVSP), originally developed to support the European Innovative Medicines Initiative project known as OpenPHACTS, was developed with the intention of providing an open platform for processing and standardizing chemical compounds. The system has been used to process millions of chemical compounds for dissemination through public websites and, unlike other validation and standardization systems, the system provides support for both standard and custom rulesets. We will provide an overview of CVSP 2.0, the next generation of the platform extending support to new cheminformatics toolkits and additional capabilities such as collaborative rules authoring.
Open Science Data Repository - the platform for materials research
Over the last few years we have seen a tremendous growth in various data repositories pushed and supported by funding bodies and various data preservation initiatives. As a result we have now a variety of scientific resources, combined into a broad network and indexed through the directories like BioSharing and re3data. Such network, while growing quickly, is still in early days of adopting semantic web standards and does not yet support deep data indexing and discoverability, leave alone that mechanisms of intellectual properties protection are as simple as making data public or private at best. The lack of standards and well defined models to describe a scientific information structure even further inhibits free information flow which is essential for scientific discovery. One of the most affected areas is not surprisingly materials sciences where due to the inherent complexity of the field of study the situation is even more severe. In this talk we present a chemistry information platform designed to support a variety of data formats along with metadata, sophisticated ways of collaboration and secure data exchanges. We will discuss challenges that we have faced developing such platform as well as solutions that we have came with.
Opportunities in chemical structure standardization
This talk was given at EBI's Wellcome Trust Genome Campus and is dedicated to outlining problems with chemical information standardization and various efforts to tackle this problem.
OpenPHACTS - Chemistry Platform Update and Learnings
OpenPHACTS - Chemistry Platform Update and Learnings
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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
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。
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of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
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Nutraceutical market, scope and growth: Herbal drug technology
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Orion Air Quality Monitoring Systems - CWS
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Nucleophilic Addition of carbonyl compounds.pptx
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
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The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
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).
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Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity 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
Richard's aventures in two entangled wonderlands
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.
原版制作(carleton毕业证书)卡尔顿大学毕业证硕士文凭原版一模一样
原版纸张【微信：741003700 】【(carleton毕业证书)卡尔顿大学毕业证】【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
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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
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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
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Supporting the exploding dimensions of the chemical sciences via global networking
- 1. Supporting the exploding dimensions of the chemical sciences via global networking Valery Tkachenko, Antony Williams, Sergey Vatsadze ACS, 248th National Meeting San Francisco, CA August 11th 2014
- 3. Maps…
- 4. Dimensions and complexity of science
- 5. RSC Archive – since 1841
- 6. Handling complex content What’s the structure? What’s the structure? Are they in our file? Are they in our file? What’s similar? What’s similar? What’s the target? What’s the target?Pharmacology data? Pharmacology data? Known Pathways? Known Pathways? Working On Now? Working On Now?Connections to disease? Connections to disease? Expressed in right cell type? Expressed in right cell type? Competitors?Competitors? IP?IP?
- 8. RSC/Rewards and Recognition Congratulations! Your 1st CSSP article has been published. Philosopher Lao Tzu said “A journey of a thousand miles begins with a single step”. In the same way we hope that this will be the first of many submissions that you make to CSSP. The First Step badge is awarded when a user submits (& has published) their 1st CSSP article.
- 10. Technical view - unification
- 11. Micro-article Compounds Reaction Analytical Data Text and References
- 12. Thesis abstract
- 13. RSC Databases RSC Compounds RSC Reactions RSC Spectra RSC Crystals RSC Polymers RSC Materials RSC Assays RSC Algorithms RSC Models …and on…
- 14. Input pipeline
- 15. Output pipeline
- 16. Compounds domain
- 17. Chemistry Validation and Standardization Platform
- 18. Chemistry Validation and Standardization Platform
- 19. Reactions domain
- 20. Reactions domain • ChemSpider Synthetic Pages • Methods in Organic Synthesis • Catalysts and Catalyzed Reactions • USPTO
- 21. Reactions domain
- 25. APIs, endpoints and widgets
- 26. Share in a “proper way”
- 27. Standards
- 28. Who is involved? 29 partners
- 31. Extending PharmaSea Site • PharmaSea website will be extended • Spectral data handling: Support Dereplication
- 33. OSDD
- 34. New Repository Architecture doi: 10.1007/s10822-014-9784-5
- 35. Federated repositories • Privacy • Security • Authenticity • Safety • Deployment • Access • Etc
- 36. International collaboration - MSU
- 37. N H O O t-BuO OMe N O MeO N O O OH OMe i) ii) R1 R2 R2 R1 1 2 34 5 R1 R2 R3 iii) N O O N H OMe N N O O N N iv) v) R1 R2 R3 R4 R1 R4 R2 R3 R1 R4 R2 R3 7 5 1 1a-h (±)-2a-h (±)-3a-h (±)-4a-l (±)-5a-l (±)-6a
- 39. Thank you Email: tkachenkov@rsc.org Slides: http://www.slideshare.net/valerytkachenko16
Editor's Notes
- That’s the world we live in
- What about science and chemistry in particular?
- What data publishers have and how they manage with the challenges of the time?
- Data curation as a necessary step in data sharing process
- Using data
- Typical example(s) of laboratory data entry. Add Will Dichtel’s image here?
- Change to add more database, rearrange
- Information typically associated with reactions
- ISO 11238