Dataset Descriptions in Open PHACTS and HCLS

SensorBench is a benchmark suite for wireless sensor networks. The design of wireless sensor network systems sits within a multi-dimensional design space, where it can be difficult to understand the implications of specific decisions and to identify optimal solutions. SensorBench enables the systematic analysis and comparison of different techniques and platforms, enabling both development and user communities to make well informed choices. The benchmark identifies key variables and performance metrics, and specifies experiments that explore how different types of task perform under different metrics for the controlled variables. The benchmark is demonstrated by its application on representative platforms. Full details of the benchmark are available from http://dl.acm.org/citation.cfm?id=2618252 (DOI: 10.1145/2618243.2618252)

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Linked data relies on instance level links between potentially differing representations of concepts in multiple datasets. However, in large complex domains, such as pharmacology, the inter-relationship of data instances needs to consider the context (e.g. task, role) of the user and the assumptions they want to apply to the data. Such context is not taken into account in most linked data integration procedures. In this paper we argue that dataset links should be stored in a stand-off fashion, thus enabling different assumptions to be applied to the data links during query execution. We present the infrastructure developed for the Open PHACTS Discovery Platform to enable this and show through evaluation that the incurred performance cost is below the threshold of user perception. http://ceur-ws.org/Vol-1034/BrenninkmeijerEtAl_COLD2013.pdf

Including Co-Referent URIs in a SPARQL Query

When are two entries about a concept in different datasets the same? If they have the same name, properties, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied. In this presentation, I will introduce Scientific lenses, an approach that enables applications to vary the equivalence conditions between linked datasets. They have been deployed in the Open PHACTS Discovery Platform – a large scale data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

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The Open PHACTS Discovery Platform aims to provide an integrated information space to advance pharmacological research in the area of drug discovery. Eﬀective drug discovery requires comprehensive data coverage, i.e. integrating all available sources of pharmacology data. While many relevant data sources are available on the linked open data cloud, their content needs to be combined with that of commercial datasets and the licensing of these commercial datasets respected when providing access to the data. Additionally, pharmaceutical companies have built up their own extensive private data collections that they require to be included in their pharmacological dataspace. In this paper we discuss the challenges of incorporating private and commercial data into a linked dataspace: focusing on the modelling of these datasets and their interlinking. We also present the graph-based access control mechanism that ensures commercial and private datasets are only available to authorized users. http://link.springer.com/chapter/10.1007/978-3-642-41338-4_5

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Alice: "What version of ChEMBL are we using?" Bob: "Er…let me check. It's going to take a while, I'll get back to you." This simple question took us the best part of a month to resolve and involved several individuals. Knowing the provenance of your data is essential, especially when using large complex systems that process multiple datasets. The underlying issues of this simple question motivated us to improve the provenance data in the Open PHACTS project. We developed a guideline for dataset descriptions where the metadata is carried with the data. In this talk I will highlight the challenges we faced and give an overview of our metadata guidelines. Presentation given to the W3C Semantic Web for Health Care and Life Sciences Interest Group on 14 January 2013.

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Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting HCLS community profile covers elements of description, identification, attribution, versioning, provenance, and content summarization. The HCLS community profile reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets. The goal of this presentation is to give an overview of the HCLS Community Profile and explain how it extends and builds upon other approaches. Presentation given at SDSVoc (https://www.w3.org/2016/11/sdsvoc/)

The HCLS Community Profile: Describing Datasets, Versions, and Distributions

Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting HCLS community profile covers elements of description, identification, attribution, versioning, provenance, and content summarization. The HCLS community profile reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets. The goal of this tutorial is to explain elements of the HCLS community profile and to enable users to craft and validate descriptions for datasets of interest.

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The global need to securely derive (instant) insights, have motivated data architectures from distributed storage, to data lakes, data warehouses and lake-houses. In this talk we describe Tag.bio, a next generation data mesh platform that embeds vital elements such as domain centricity/ownership, Data as Products, Self-serve architecture, with a federated computational layer. Tag.bio data products combine data sets, smart APIs, statistical and machine learning algorithms into decentralized data products for users to discover insights using FAIR Principles. Researchers can use its point and click (no-code) system to instantly perform analysis and share versioned, reproducible results. The platform combines a dynamic cohort builder with analysis protocols and applications (low-code) to drive complex analysis workflows. Applications within data products are fully customizable via R and Python plugins (pro-code), and the platform supports notebook based developer environments with individual workspaces. Join us for a talk/demo session on Tag.bio data mesh platform and learn how major pharma industries and university health systems are using this technology to promote value based healthcare, precision healthcare, find cures for disease, and promote collaboration (without explicitly moving data around). The talk also outlines Tag.bio secure data exchange features for real world evidence datasets, privacy centric data products (confidential computing) as well as integration with cloud services

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Presented at #SALAD2015 The heterogeneity of methods and technologies to publish open data is still an issue to develop distributed systems on the Web. On the one hand, Web APIs, the most popular approach to offer data services, implement REST principles, which focus on addressing loose coupling and interoperability issues. On the other hand, Linked Data, available through SPARQL endpoints, focus on data integration between distributed data sources. We proposes BASIL, an approach to build Web APIs on top of SPARQL endpoints, in order to benefit of the advantages from both Web APIs and Linked Data approaches. Compared to similar solution, BASIL aims on minimising the learning curve for users to promote its adoption. The main feature of BASIL is a simple API that does not introduce new specifications, formalisms and technologies for users that belong to both Web APIs and Linked Data communities.

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In recent years there has been a reproducibility crisis in science. Computational notebooks, such as Jupyter, have been touted as one solution to this problem. However, when executing analyses over live SPARQL endpoints, we get different answers depending upon when the analysis in the notebook was executed. In this paper, we identify some of the issues discovered in trying to develop a reproducible analysis over a collection of biomedical data sources and suggest some best practice to overcome these issues.

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The Bioschemas community (http://bioschemas.org) is a loose collaboration formed by a wide range of life science resource providers and informaticians. The community is developing profiles over Schema.org to enable life science resources such as data about a specific protein, sample, or training event, to be more discoverable on the web. While the content of well-known resources such as Uniprot (for protein data) are easily discoverable, there is a long tail of specialist resources that would benefit from embedding Schema.org markup in a standardised approach. The community have developed twelve profiles for specific types of life science resources (http://bioschemas.org/specifications/), with another six at an early draft stage. For each profile, a set of use cases have been identified. These typically focus on search, but several facilitate lightweight data exchange to support data aggregators such as Identifiers.org, FAIRsharing.org, and BioSamples. The next stage of the development of a profile consists of mapping the terms used in the use cases to existing properties in Schema.org and domain ontologies. The properties are then prioritised in order to support the use cases, with a minimal set of about six properties identified, along with a larger set of recommended and optional properties. For each property, an expected cardinality is defined and where appropriate, object values are specified from controlled vocabularies. Before a profile is finalised, it must first be demonstrated that resources can deploy the markup. In this talk, we will outline the progress that has been made by the Bioschemas Community in a single year through three hackathon events. We will discuss the processes followed by the Bioschemas Community to foster collaboration, and highlight the benefits and drawbacks of using open Google documents and spreadsheets to support the community develop the profiles. We will conclude by summarising future opportunities and directions for the community.

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The Digitising Scotland project is having the vital records of Scotland transcribed from images of the original handwritten civil registers . Linking the resulting dataset of 24 million vital records covering the lives of 18 million people is a major challenge requiring improved record linkage techniques. Discussions within the multidisciplinary, widely distributed Digitising Scotland project team have been hampered by the teams in each of the institutions using their own identification scheme. To enable fruitful discussions within the Digitising Scotland team, we required a mechanism for uniquely identifying each individual represented on the certificates. From the identifier it should be possible to determine the type of certificate and the role each person played. We have devised a protocol to generate for any individual on the certificate a unique identifier, without using a computer, by exploiting the National Records of Scotland•À_s registration districts. Importantly, the approach does not rely on the handwritten content of the certificates which reduces the risk of the content being misread resulting in an incorrect identifier. The resulting identifier scheme has improved the internal discussions within the project. This paper discusses the rationale behind the chosen identifier scheme, and presents the format of the different identifiers. The work reported in the paper was supported by the British ESRC under grants ES/K00574X/1(Digitising Scotland) and ES/L007487/1 (Administrative Data Research Center - Scotland).

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Machine processable descriptions of datasets can help make data more FAIR; that is Findable, Accessible, Interoperable, and Reusable. However, there are a variety of metadata profiles for describing datasets, some specific to the life sciences and others more generic in their focus. Each profile has its own set of properties and requirements as to which must be provided and which are more optional. Developing a dataset description for a given dataset to conform to a specific metadata profile is a challenging process. In this talk, I will give an overview of some of the dataset description specifications that are available. I will discuss the difficulties in writing a dataset description that conforms to a profile and the tooling that I've developed to support dataset publishers in creating metadata description and validating them against a chosen specification. Seminar talk given at the EBI on 5 April 2017

Supporting Dataset Descriptions in the Life Sciences

Validata (http://hw-swel.github.io/Validata/) is an online web application for validating a dataset description expressed in RDF against a community profile expressed as a Shape Expression (ShEx). Additionally it provides an API for programmatic access to the validator. Validata is capable of being used for multiple community agreed standards, e.g. DCAT, the HCLS community profile, or the Open PHACTS guidelines, and there are currently deployments to support each of these. Validata can be easily repurposed for different deployments by providing it with a new ShEx schema. The Validata code is available from GitHub (https://github.com/HW-SWeL/Validata). Presentation given at SDSVoc https://www.w3.org/2016/11/sdsvoc

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This presentation was prepared for my faculty Christmas conference. Abstract: For the last 11 months I have been working on a top secret project with a world renowned Scandinavian industry partner. We are now moving into the exciting operational phase of this project. I have been granted an early lifting of the embargo that has stopped me talking about this work up until now. I will talk about the data science behind this big data project and how semantic web technology has enabled the delivery of Project X.

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Data is being generated all around us – from our smart phones tracking our movement through a city to the city itself sensing various properties and reacting to various conditions. However, to maximise the potential from all this data, it needs to be combined and coerced into models that enable analysis and interpretation. In this talk I will give an overview of the techniques that I have developed for data integration: integrating streams of sensor data with background contextual data and supporting multiple interpretations of linking data together. At the end of the talk I will overview the work I will be conducting in the Administrative Data Research Centre for Scotland.

Data Integration in a Big Data Context

When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied. In this paper, we present an approach to enable applications to choose the equivalence criteria to apply between datasets. Thus, supporting multiple dynamic views over the Linked Data. For chemical data, we show that multiple sets of links can be automatically generated according to different equivalence criteria and published with semantic descriptions capturing their context and interpretation. This approach has been applied within a large scale public-private data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

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Big Data presents an exciting opportunity to pursue large-scale analyses over collections of data in order to uncover valuable insights across a myriad of fields and disciplines. Yet, as more and more data is made available, researchers are finding it increasingly difficult to discover and reuse these data. One problem is that data are insufficiently described to understand what they are or how they were produced. A second issue is that no single vocabulary provides all key metadata fields required to support basic scientific use cases. A third issue is that data catalogs and data repositories all use different metadata standards, if they use any standard at all, and this prevents easy search and aggregation of data. Therefore, we need a community profile to indicate what are the essential metadata, and the manner in which we can express it. The W3C Health Care and Life Sciences Interest Group have developed such a community profile that defines the required properties to provide high-quality dataset descriptions that support finding, understanding, and reusing scientific data, i.e. making the data FAIR (Findable, Accessible, Interoperable and Re-usable – http://datafairport.org). The specification reuses many notions and vocabulary terms from Dublin Core, DCAT and VoID, with provenance and versioning information being provided by PROV-O and PAV. The community profile is based around a three tier model; the summary description captures catalogue style metadata about the dataset, each version of the dataset is described separately as are the various distribution formats of these versions. The resulting community profile is generic and applicable to a wide variety of scientific data. Tools are being developed to help with the creation and validation of these descriptions. Several datasets including those from Bio2RDF, EBI and IntegBio are already moving to release descriptions conforming to the community profile.

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This paper presents the rules used within the Open PHACTS (http://www.openphacts.org) Identity Management Service to compute co-reference chains across multiple datasets. The web of (linked) data has encouraged a proliferation of identifiers for the concepts captured in datasets; with each dataset using their own identifier. A key data integration challenge is linking the co-referent identifiers, i.e. identifying and linking the equivalent concept in every dataset. Exacerbating this challenge, the datasets model the data differently, so when is one representation truly the same as another? Finally, different users have their own task and domain specific notions of equivalence that are driven by their operational knowledge. Consumers of the data need to be able to choose the notion of operational equivalence to be applied for the context of their application. We highlight the challenges of automatically computing co-reference and the need for capturing the context of the equivalence. This context is then used to control the co-reference computation. Ultimately, the context will enable data consumers to decide which co-references to include in their applications.

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Botany 4th semester series (krishna).pdf

Sumit Kumar yadav

Mustard, as a crop, is susceptible to a variety of pests that can affect its growth and yield. Here’s a rundown of some common pests that target mustard plants: Aphids: These small, sap-sucking insects can cause significant damage by feeding on the leaves and stems. Aphids also excrete a sticky substance known as honeydew, which can lead to the growth of sooty mold on the plants. Flea Beetles: These tiny beetles jump like fleas when disturbed and chew small holes in the leaves. They are particularly damaging in the early growth stages of the plant. Cabbage Loopers: The larvae of a type of moth, these caterpillars are known for their distinctive looping movement. They chew large holes in the leaves and can defoliate plants if present in large numbers. Diamondback Moth Larvae: Another caterpillar pest, these larvae chew small holes in the leaves and can cause extensive damage, especially when infestations are heavy. Whiteflies: These are tiny, winged insects that feed on plant sap and can quickly become a problem in greenhouse or close planting conditions. Like aphids, they also secrete honeydew. Cutworms: These are the larvae of certain types of moths and are known for cutting young plants at the stem base at ground level. They are most destructive during the night. Root Maggots: The larvae of root maggot flies, these pests attack the roots of mustard plants, causing wilting and potentially killing young plants. Harlequin Bugs: These are colorful stink bugs that suck the sap from mustard plant stems and leaves, causing the leaves to become stippled, wilt, and eventually die if the infestation is severe. Mustard Sawfly: The larvae of the mustard sawfly can cause considerable defoliation, as they feed voraciously on the leaves. Clubroot: Caused by a fungus-like organism, clubroot affects the roots, causing them to swell and distort. While technically a disease, it is often associated with pest management because controlling it involves similar preventative strategies. Control Measures: Managing pests in mustard involves a combination of cultural, biological, and chemical methods. Crop rotation, resistant varieties, timely sowing, maintaining plant health, and using natural predators like ladybugs and parasitic wasps can help keep pest populations under control. Chemical pesticides should be used as a last resort due to their potential impact on the environment and non-target species.

Pests of mustard_Identification_Management_Dr.UPR.pdf

PirithiRaju

HotJupiters are amongthebest-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5–12µ 5–12µ 5–12µm with JWST’s Mid-Infrared Instrument (MIRI). 1524 ±35 1524 ±35 and 863±23 The spectra reveal a large day–night temperature contrast (with average brightness temperatures of 1524 ± 35 863 ±23 863 ±23Kelvin, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase curve shape and emission spectra strongly suggest the presence of nightside clouds which become optically thick to thermal emission at pressures greater than ∼100mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Con3trary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2σ upper limit of 1–6 parts per million, depending on model assumptions).

Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b

Bollworms are among the most damaging pests in cotton cultivation, affecting the bolls where the cotton fibers are formed. There are several species of bollworms, each capable of causing significant yield loss and quality degradation if not effectively managed. Here’s a detailed look at the primary bollworm species affecting cotton: Cotton Bollworm (Helicoverpa armigera): Also known as the corn earworm or the Old World bollworm, this pest is found in many regions around the world. It is highly polyphagous (feeds on many different plants) and poses a threat not only to cotton but also to maize, tomatoes, and legumes. The larvae bore into the cotton bolls, feeding on the developing seeds and fibers, which can lead to boll rot. Pink Bollworm (Pectinophora gossypiella): A significant pest of cotton, the pink bollworm larvae infest the cotton bolls, feeding on the seeds and lint. This can severely damage or destroy the bolls. In regions where pink bollworms are prevalent, they have been a major driver for the adoption of genetically engineered Bt cotton, which expresses a bacterium gene toxic to certain insects. Tobacco Budworm (Heliothis virescens): Closely related to the cotton bollworm, the tobacco budworm primarily attacks tobacco but is also a common pest in cotton. It primarily damages the flowers and bolls of the cotton plant. Differentiating between the tobacco budworm and the cotton bollworm based on appearance can be challenging, but it is crucial for effective management. American Bollworm (Helicoverpa zea): Known in some regions as the corn earworm, it is similar in behavior to Helicoverpa armigera and poses a threat to a variety of crops, including cotton. The larvae attack the cotton bolls, leading to direct damage to the cotton lint and seeds. Management Strategies: Cultural Controls: Crop rotation, destruction of crop residues, and deep plowing can help break the pest’s life cycle. Timing of planting can also be adjusted to avoid peak pest infestation. Biological Controls: Natural enemies like Trichogramma wasps, which parasitize bollworm eggs, and predators such as lacewings and ladybugs can be encouraged. Bacillus thuringiensis (Bt) products can also be sprayed, which are particularly effective against young larvae. Chemical Controls: Insecticides may be required when infestation levels exceed economic thresholds. However, resistance management must be considered, alternating modes of action to avoid developing resistance. Genetic Approaches: Bt cotton, genetically modified to express Bacillus thuringiensis toxin, has been highly effective in controlling bollworms and has dramatically reduced the reliance on chemical insecticides. Monitoring and Scouting: Regular field scouting and using pheromone traps to monitor adult populations can help in timely and targeted application of control measures. The effective management of bollworms often requires an integrated approach

Pests of cotton_Borer_Pests_Binomics_Dr.UPR.pdf

PirithiRaju

We explore different scenarios to explain the chemical difference found in the remarkable giant-giant binary system HD138202+CD−3012303. For the first time, we suggest how to distinguish these scenarios by taking advantage of the extensive convective envelopes of giant stars. Methods. We carried out a high-precision determination of stellar parameters and abundances by applying a full line-by-line differential analysis on GHOST high-resolution spectra. We used the FUNDPAR program with ATLAS12 model atmospheres and specific opacities calculated for an arbitrary composition through a doubly iterated method. Physical parameters were estimated with the isochrones package and evolutionary tracks were calculated via MIST models. Results. We found a significant chemical difference between the two stars (∆[Fe/H]∼0.08dex), which is largely unexpected considering the insensitivity of giant stars to planetary ingestion and diffusion effects. We tested the possibility of engulfment events by using several different combinations of stellar mass, ingested mass, metallicity of the engulfed object and different convective envelopes. However, the planetary ingestion scenario does not seem to explain the observed differences. For the first time, we distinguished the source of chemical differences using a giant-giant binary system. By ruling out other possible scenarios such as planet formation and evolutionary effects between the two stars, we suggest that primordial inhomogeneities might explain the observed differences. This remarkable result implies that the metallicity differences that were observed in at least some main-sequence binary systems might be related to primordial inhomogeneities rather than engulfment events. We also discuss the important implications of finding primordial inhomogeneities, which affect chemical tagging and other fields such as planet formation. We strongly encourage the use of giantgiant pairs. They are a relevant complement to main-sequence pairs for determining the origin of the observed chemical differences in multiple systems.

Disentangling the origin of chemical differences using GHOST