cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh017303.07fu
title: SH017303.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh017304.07fu
title: SH017304.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh017305.07fu
title: SH017305.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh017306.07fu
title: SH017306.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh017307.07fu
title: SH017307.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh017308.07fu
title: SH017308.07FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5258/soton/d2529
title: Dataset to support the publication: Developing electric barriers for selective-fish guidance: Interspecific variation in response to pulsed direct currents

description: The dataset contains: Tab 1 "CyprinidWaveform". Contains the data relating to threshold field strengths of responses for cyprinids. Sheet includes infomation on TrialID, Species (C- Common carp, G- grass carp), threshold field strengths for twitch [threstwitch], loss of orientation [thresloss] and tetany [threstet], waveform (1,2,3,4), body length: (total, standard and fork length), mass, water temperature (start, end and holding tank). Tab 2 "AnguillidWaveform". Contains the data relating to threshold field strengths of responses for adult silver-phase eel. Sheet includes infomation on TrialID, Species (E-Eel), threshold field strengths for twitch [threstwitch], loss of orientation [thresloss] and tetany [threstet], waveform (1,2), eel length and mass, water temperature (start, end and holding tank).
DOI: 10.5281/zenodo.7606610
title: A benchmark dataset of Solidity smart contracts

description: A benchmark dataset contains 4,364 real-world Solidity smart contracts, which are manually labeled with ten types of vulnerabilities. DC (DelegateCall). The <em>address.delegatecall() </em>function allows a smart contract to dynamically load external contracts from <em>address</em> at runtime. If the attacker can control the external contract and affect the current contract status, the contract is vulnerable to DC. IOU (Arithmetic/Integer Overflow and Underflow). An arithmetic overflow or underflow, often called Integer Overflow or Underflow (IOU), occurs when an arithmetic operation attempts to create a numeric variable value that is larger than the maximum value or smaller than the minimum value of the variable type. If the arithmetic operation may pass a variable type’s maximum or minimum value and is performed without using SafeMath, the contract is vulnerable to IOU. NC (Nested Call). The function containing the loop has a high risk of exceeding its gas limitation and causing an out-of-gas error. If the attacker can control the loop iteration and causes the out-of-gas error, the contract is vulnerable to NC, RE (Reentrancy). The contract vulnerable to RE uses the <em>call()</em> function to transfer ether to an external contract. The external contract can reenter the vulnerable contract by fallback function. If the state variable change is after the <em>call()</em> function, the reentrance will cause status inconsistency. TD (Timestamp Dependency). The contract uses the <em>timestamp</em> as the deciding factor for critical operations, e.g., sending ether. If the attacker can get ether from the contract by manipulating the timestamp or affecting the critical operations, the contract is vulnerable to TD. TO (TxOrigin). If the contract only uses <em>tx.origin</em> to verify the caller's identification for critical operations, it is vulnerable to TO. TOD (Transaction Order Dependency). The contract may send out ether differently according to different values of a global state variable or different balance values of the contract. If the attackers can get ether from the contract by manipulating the transaction sequences, the contract is vulnerable to TOD. UcC (Unchecked Call). The contract uses the function <em>call()</em> or <em>send()</em> without result checking. If the <em>send()</em> or <em>call() </em> function fails and leads to status inconsistency, the contract is vulnerable to UcC. Unprotected Suicide). If an attacker can self-destruct the contract by calling the <em>selfdestruct</em>(address) function, the contract is vulnerable to UpS. FE (Frozen Ether). If the contract can receive ether but cannot transfer it by itself, it is vulnerable to FE. <br> <strong>For the purpose of protection for smart contracts, the dataset can be available after request.</strong>
DOI: 10.5281/zenodo.7606609
title: A benchmark dataset of Solidity smart contracts

description: A benchmark dataset contains 4,364 real-world Solidity smart contracts, which are manually labeled with ten types of vulnerabilities. DC (DelegateCall). The <em>address.delegatecall() </em>function allows a smart contract to dynamically load external contracts from <em>address</em> at runtime. If the attacker can control the external contract and affect the current contract status, the contract is vulnerable to DC. IOU (Arithmetic/Integer Overflow and Underflow). An arithmetic overflow or underflow, often called Integer Overflow or Underflow (IOU), occurs when an arithmetic operation attempts to create a numeric variable value that is larger than the maximum value or smaller than the minimum value of the variable type. If the arithmetic operation may pass a variable type’s maximum or minimum value and is performed without using SafeMath, the contract is vulnerable to IOU. NC (Nested Call). The function containing the loop has a high risk of exceeding its gas limitation and causing an out-of-gas error. If the attacker can control the loop iteration and causes the out-of-gas error, the contract is vulnerable to NC, RE (Reentrancy). The contract vulnerable to RE uses the <em>call()</em> function to transfer ether to an external contract. The external contract can reenter the vulnerable contract by fallback function. If the state variable change is after the <em>call()</em> function, the reentrance will cause status inconsistency. TD (Timestamp Dependency). The contract uses the <em>timestamp</em> as the deciding factor for critical operations, e.g., sending ether. If the attacker can get ether from the contract by manipulating the timestamp or affecting the critical operations, the contract is vulnerable to TD. TO (TxOrigin). If the contract only uses <em>tx.origin</em> to verify the caller's identification for critical operations, it is vulnerable to TO. TOD (Transaction Order Dependency). The contract may send out ether differently according to different values of a global state variable or different balance values of the contract. If the attackers can get ether from the contract by manipulating the transaction sequences, the contract is vulnerable to TOD. UcC (Unchecked Call). The contract uses the function <em>call()</em> or <em>send()</em> without result checking. If the <em>send()</em> or <em>call() </em> function fails and leads to status inconsistency, the contract is vulnerable to UcC. Unprotected Suicide). If an attacker can self-destruct the contract by calling the <em>selfdestruct</em>(address) function, the contract is vulnerable to UpS. FE (Frozen Ether). If the contract can receive ether but cannot transfer it by itself, it is vulnerable to FE. <br> <strong>For the purpose of protection for smart contracts, the dataset can be available after request.</strong>
DOI: 10.20351/29vg20201108_ts
title: Thermosalinograph data, Vendée Globe sailing race 2020-2021

description: Sea surface salinity and temperature acquired on board the One Planet One Ocean sailboat skipped by Didac Costa during the Vendée Globe (Edition 2020-2021) sailing race.
DOI: 10.5281/zenodo.5845412
title: SciQA benchmark: Dataset and RDF dump

description: SciQA benchmark of questions and queries. The data dump is in NTriples format (RDF NT) taken from the orkg system on 13.01.2022 at 12:03PM.<br> The dump can be imported into a virtuoso endpoint or any RDF engine so it can be queried. The questions/queries are provided as spread sheets (Excel format & OpenDocument format)
DOI: 10.5281/zenodo.6517080
title: SciQA benchmark: Dataset and RDF dump

description: SciQA benchmark of questions and queries. The data dump is in NTriples format (RDF NT) taken from the orkg system on 13.01.2022 at 12:03PM.<br> The dump can be imported into a virtuoso endpoint or any RDF engine so it can be queried. The questions/queries are provided as spread sheets (Excel format & OpenDocument format) <strong>More details</strong> on certain columns:<br> "Classification rationale" It may contain the following values: Nested facts in the question Sorting, sum, average, minimum, maximum or count calculation required Filter used Mappings of Asking Point in the question to the ORKG ontology <strong>Explanation of Rationale for Non-factoid</strong>: Nested facts in the question. An entity (e.g., a system or a paper) or predicate is requested that is not explicitly stated in the question text and must be inferred while searching for an answer. Sorting, sum, average, minimum, maximum or count calculation required. To get the answer to the question it is necessary to make an aggregation of the query results. Filter used. To get the answer to the question it is necessary to use filtering of the query results by some conditions.<br>
DOI: 10.5281/zenodo.7707888
title: SciQA benchmark: Dataset and RDF dump

description: SciQA benchmark of questions and queries. The data dump is in NTriples format (RDF NT) taken from the orkg system on 14.03.2023 at 02:04PM.<br> The dump can be imported into a virtuoso endpoint or any RDF engine so it can be queried. The questions/queries are provided as spread sheets (Excel format & CSV format), also train and test files are provided. <strong>Types</strong> of questions and queries: Handcrafted set of 100 questions Auto-generated set of 2465 questions <strong>More details</strong> on certain columns:<br> "Classification rationale" It may contain the following values: Nested facts in the question Sorting, sum, average, minimum, maximum or count calculation required Filter used Mappings of Asking Point in the question to the ORKG ontology <strong>Explanation of Rationale for Non-factoid</strong>: Nested facts in the question. An entity (e.g., a system or a paper) or predicate is requested that is not explicitly stated in the question text and must be inferred while searching for an answer. Sorting, sum, average, minimum, maximum or count calculation required. To get the answer to the question it is necessary to make an aggregation of the query results. Filter used. To get the answer to the question it is necessary to use filtering of the query results by some conditions.<br>
DOI: 10.5281/zenodo.5845197
title: SciQA benchmark: Dataset and RDF dump

description: SciQA benchmark of questions and queries. The data dump is in NTriples format (RDF NT) taken from the ORKG system on 14.02.2023 at 02:04PM.<br> The dump can be imported into a virtuoso endpoint or any RDF engine so it can be queried. The questions/queries are provided as JSON files, also train and test files are provided for each of the sets. <strong>Types</strong> of questions and queries: Handcrafted set of 100 questions Auto-generated set of 2465 questions <strong>More details</strong> on certain columns:<br> "Classification rationale" It may contain the following values: Nested facts in the question Sorting, sum, average, minimum, maximum or count calculation required Filter used Mappings of Asking Point in the question to the ORKG ontology <strong>Explanation of Rationale for Non-factoid</strong>: Nested facts in the question. An entity (e.g., a system or a paper) or predicate is requested that is not explicitly stated in the question text and must be inferred while searching for an answer. Sorting, sum, average, minimum, maximum or count calculation required. To get the answer to the question it is necessary to make an aggregation of the query results. Filter used. To get the answer to the question it is necessary to use filtering of the query results by some conditions.<br>
DOI: 10.5281/zenodo.7727922
title: SciQA benchmark: Dataset and RDF dump

description: SciQA benchmark of questions and queries. The data dump is in NTriples format (RDF NT) taken from the ORKG system on 14.02.2023 at 02:04PM.<br> The dump can be imported into a virtuoso endpoint or any RDF engine so it can be queried. The questions/queries are provided as spread sheets (Excel format & CSV format), also train and test files are provided for each of the sets. <strong>Types</strong> of questions and queries: Handcrafted set of 100 questions Auto-generated set of 2465 questions <strong>More details</strong> on certain columns:<br> "Classification rationale" It may contain the following values: Nested facts in the question Sorting, sum, average, minimum, maximum or count calculation required Filter used Mappings of Asking Point in the question to the ORKG ontology <strong>Explanation of Rationale for Non-factoid</strong>: Nested facts in the question. An entity (e.g., a system or a paper) or predicate is requested that is not explicitly stated in the question text and must be inferred while searching for an answer. Sorting, sum, average, minimum, maximum or count calculation required. To get the answer to the question it is necessary to make an aggregation of the query results. Filter used. To get the answer to the question it is necessary to use filtering of the query results by some conditions.<br>
DOI: 10.5281/zenodo.7729011
title: SciQA benchmark: Dataset and RDF dump

description: SciQA benchmark of questions and queries. The data dump is in NTriples format (RDF NT) taken from the ORKG system on 14.02.2023 at 02:04PM.<br> The dump can be imported into a virtuoso endpoint or any RDF engine so it can be queried. The questions/queries are provided as spread sheets (Excel format & CSV format), also train and test files are provided for each of the sets. HF datasets are also attached in the archive. <strong>Types</strong> of questions and queries: Handcrafted set of 100 questions Auto-generated set of 2465 questions <strong>More details</strong> on certain columns:<br> "Classification rationale" It may contain the following values: Nested facts in the question Sorting, sum, average, minimum, maximum or count calculation required Filter used Mappings of Asking Point in the question to the ORKG ontology <strong>Explanation of Rationale for Non-factoid</strong>: Nested facts in the question. An entity (e.g., a system or a paper) or predicate is requested that is not explicitly stated in the question text and must be inferred while searching for an answer. Sorting, sum, average, minimum, maximum or count calculation required. To get the answer to the question it is necessary to make an aggregation of the query results. Filter used. To get the answer to the question it is necessary to use filtering of the query results by some conditions.<br>
DOI: 10.5281/zenodo.7729047
title: SciQA benchmark: Dataset and RDF dump

description: SciQA benchmark of questions and queries. The data dump is in NTriples format (RDF NT) taken from the ORKG system on 14.02.2023 at 02:04PM.<br> The dump can be imported into a virtuoso endpoint or any RDF engine so it can be queried. The questions/queries are provided as spread sheets (Excel format & CSV format), also train and test files are provided for each of the sets. Huggingface datasets are also attached in the archive to make it easy to integrate with existing workflows and to enable the automated evaluation of SciQA within challenges. <strong>Types</strong> of questions and queries: Handcrafted set of 100 questions Auto-generated set of 2465 questions <strong>More details</strong> on certain columns:<br> "Classification rationale" It may contain the following values: Nested facts in the question Sorting, sum, average, minimum, maximum or count calculation required Filter used Mappings of Asking Point in the question to the ORKG ontology <strong>Explanation of Rationale for Non-factoid</strong>: Nested facts in the question. An entity (e.g., a system or a paper) or predicate is requested that is not explicitly stated in the question text and must be inferred while searching for an answer. Sorting, sum, average, minimum, maximum or count calculation required. To get the answer to the question it is necessary to make an aggregation of the query results. Filter used. To get the answer to the question it is necessary to use filtering of the query results by some conditions.<br>
DOI: 10.5281/zenodo.7741904
title: SciQA benchmark: Dataset and RDF dump

description: SciQA benchmark of questions and queries. The data dump is in NTriples format (RDF NT) taken from the ORKG system on 14.02.2023 at 02:04PM.<br> The dump can be imported into a virtuoso endpoint or any RDF engine so it can be queried. The questions/queries are provided as JSON files, also train and test files are provided for each of the sets. <strong>Types</strong> of questions and queries: Handcrafted set of 100 questions Auto-generated set of 2465 questions <strong>More details</strong> on certain columns:<br> "Classification rationale" It may contain the following values: Nested facts in the question Sorting, sum, average, minimum, maximum or count calculation required Filter used Mappings of Asking Point in the question to the ORKG ontology <strong>Explanation of Rationale for Non-factoid</strong>: Nested facts in the question. An entity (e.g., a system or a paper) or predicate is requested that is not explicitly stated in the question text and must be inferred while searching for an answer. Sorting, sum, average, minimum, maximum or count calculation required. To get the answer to the question it is necessary to make an aggregation of the query results. Filter used. To get the answer to the question it is necessary to use filtering of the query results by some conditions.<br>