Abstract:Leg ODP started numbering the scientific cruises of the JOIDES Resolution at Leg 101 (Leg 100 was a trial run of the modified drilling ship). Leg duration was nominally 2 months. The Shipboard Science Party typically consisted of 25 scientists drawn from universities, governments, and industry around the world. During the 18+ yr of ODP, there were 110 cruises on the JOIDES Resolution. T1. Prime data types, p. 118. ODP INFORMATION TECHNOLOGY AND DATA SERVICES ODP TECHNICAL NOTE 37 4 Site A site is the location … Show more
“…1 ). Historically the notation preceded the invention of ASCII [ 24 ], with only the upper case alphabetic characters available on the punch card computers of that era. Fig.…”
Purpose
Wiswesser Line Notation (WLN) is a old line notation for encoding chemical compounds for storage and processing by computers. Whilst the notation itself has long since been surpassed by SMILES and InChI, distribution of WLN during its active years was extensive. In the context of modernising chemical data, we present a comprehensive WLN parser developed using the OpenBabel toolkit, capable of translating WLN strings into various formats supported by the library. Furthermore, we have devised a specialised Finite State Machine l, constructed from the rules of WLN, enabling the recognition and extraction of chemical strings out of large bodies of text. Available open-access WLN data with corresponding SMILES or InChI notation is rare, however ChEMBL, ChemSpider and PubChem all contain WLN records which were used for conversion scoring. Our investigation revealed a notable proportion of inaccuracies within the database entries, and we have taken steps to rectify these errors whenever feasible.
Scientific contribution
Tools for both the extraction and conversion of WLN from chemical documents have been successfully developed. Both the Deterministic Finite Automaton (DFA) and parser handle the majority of WLN rules officially endorsed in the three major WLN manuals, with the parser showing a clear jump in accuracy and chemical coverage over previous submissions. The GitHub repository can be found here: https://github.com/Mblakey/wiswesser.
“…1 ). Historically the notation preceded the invention of ASCII [ 24 ], with only the upper case alphabetic characters available on the punch card computers of that era. Fig.…”
Purpose
Wiswesser Line Notation (WLN) is a old line notation for encoding chemical compounds for storage and processing by computers. Whilst the notation itself has long since been surpassed by SMILES and InChI, distribution of WLN during its active years was extensive. In the context of modernising chemical data, we present a comprehensive WLN parser developed using the OpenBabel toolkit, capable of translating WLN strings into various formats supported by the library. Furthermore, we have devised a specialised Finite State Machine l, constructed from the rules of WLN, enabling the recognition and extraction of chemical strings out of large bodies of text. Available open-access WLN data with corresponding SMILES or InChI notation is rare, however ChEMBL, ChemSpider and PubChem all contain WLN records which were used for conversion scoring. Our investigation revealed a notable proportion of inaccuracies within the database entries, and we have taken steps to rectify these errors whenever feasible.
Scientific contribution
Tools for both the extraction and conversion of WLN from chemical documents have been successfully developed. Both the Deterministic Finite Automaton (DFA) and parser handle the majority of WLN rules officially endorsed in the three major WLN manuals, with the parser showing a clear jump in accuracy and chemical coverage over previous submissions. The GitHub repository can be found here: https://github.com/Mblakey/wiswesser.
“…2021. Habitus Lateral View of MCZ:ENT:17219 Nomadopsis puellae (Cockerell, 1933) A signed citation can be generated for the entire collection description by computing the SHA-256 hash of an ASCII encoding (Gorn et al 1963) of the text, then including the hash in the form of a hash URI: Some author, 2021. A collection of signed citations for various images.…”
Section: Example 2 a Basic Form Of A Collection Description May Look ...mentioning
Digital data are a foundation of 21st century science. However, commonly used data citation practices rely on unverifiable retrieval methods which are susceptible to “content drift”, which occurs when the data associated with an identifier have been allowed to change. Based on our earlier work on reliable dataset identifiers, we propose signed citations, i.e., customary data citations extended to also include a standards-based, verifiable, unique, and fixed-length digital content signature. For example, the signature of a dataset could represent the SHA-256 hash of its content. We show that the inclusion of content signatures in citations not only enables independent verification of the cited content, but can also improve the persistence of the citation. Furthermore, if a content signature registry is available which links content signatures to known locations of their associated content, then content signatures can be used as resolvable data identifiers. Because signed citations are location- and storage-medium-agnostic, as many copies of cited data as necessary can be created to ensure content persistence across current and future storage media and data networks. As a result, content signatures can be leveraged to help scalably store, locate, access, and independently verify content across new and existing data repositories, search engines, and registries without requiring any time-sensitive information to be baked into citations. Content signatures can also be embedded inside content to create robust, distributed knowledge graphs that can be cited using a single signed citation. We describe real-world applications of content signatures, including compiling a distributed collection of digitized images of bee specimens from natural history collections, versioning biodiversity datasets containing over a billion records available through biodiversity data aggregators, and stabilizing URLs used as identifiers for taxonomic name resources.
“…The representation of numerical data mainly includes original code representation, inverse code representation, complement code representation, shift code representation, fixed-point representation and floatingpoint representation. The representation of non numeric data mainly includes the representation of characters, such as American standardcode for information interchange (ASCII) code [25]. It represents 128 commonly used symbols, which are represented by 128 binary digital strings respectively.…”
<p>It is not always reasonable to set a fixed weight value to an attribute (or a variable) because this cannot accurately maintain the similarity between users and leads to decrease the performance of collaborative filtering recommendation algorithms. In this paper, a piecewise weighting method is proposed with the hyper-class representation for improving the collaborative filtering recommendation. Specifically, a kernel function is first applied to map the original data into the kernel space, and to learn the weight of each attribute. And then, the hyper-class representation of the data is constructed to learn the weight of the segmented attribute value (hyper-class) for each attribute, so as to construct a piecewise weighting function. In addition, the piecewise weighting function is applied to calculate the similarity between users for a collaborative filtering recommendation. Finally, experiments are conducted to examine the collaborative filtering recommendation algorithm, and show that the proposed algorithm with piecewise weighting functions is more efficient than the compared algorithm with fixed weight values, in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Precision.</p>
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