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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<identifier: string, identifierType: string, type: string, title: string, firstPage: string, lastPage: string, volume: string>
to
{'identifier': Value('string'), 'identifierType': Value('string'), 'type': Value('string'), 'title': Value('string'), 'firstPage': Value('string'), 'issue': Value('string'), 'volume': Value('string')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2223, in cast_table_to_schema
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2224, in <listcomp>
                  cast_array_to_feature(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2001, in cast_array_to_feature
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2002, in <listcomp>
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2092, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<identifier: string, identifierType: string, type: string, title: string, firstPage: string, lastPage: string, volume: string>
              to
              {'identifier': Value('string'), 'identifierType': Value('string'), 'type': Value('string'), 'title': Value('string'), 'firstPage': Value('string'), 'issue': Value('string'), 'volume': Value('string')}
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1451, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 994, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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attributes
dict
id
string
relationships
dict
type
string
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10.13140/rg.2.2.32615.83369
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dois
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10.13140/rg.2.2.20185.29289
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Motivated by this, there is a considerable ongoing effort to develop new characterisation tools for quantum many-body systems. Here we demonstrate Matrix Product State (MPS) tomography, which is theoretically proven to allow the states of a broad class of quantum systems to be accurately estimated with an effort that increases efficiently with constituent number. We first prove that this broad class includes the out-of-equilbrium states produced by 1D systems with finite-range interactions, up to any fixed point in time. We then use the technique to reconstruct the dynamical state of a trapped-ion quantum simulator comprising up to 14 entangled spins (qubits): a size far beyond the reach of QST. Our results reveal the dynamical growth of entanglement and description complexity as correlations spread out during a quench: a necessary condition for future beyond-classical performance. 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10.17605/osf.io/zw95e
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10.48550/arxiv.1701.07739
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OW J1753-3107 is the brightest of any of the currently known warm or hot DQ and was discovered in the OmegaWhite Survey as exhibiting optical variations on a period of 35.5452 (2) mins, with no evidence for other periods in its light curves. This period has remained constant over the last two years and a single-period sinusoidal model provides a good fit for all follow-up light curves. The spectrum consists of a very blue continuum with strong absorption lines of neutral and ionised carbon, a broad He I 4471 A line, and possibly weaker hydrogen lines. The C I lines are Zeeman split, and indicate the presence of a strong magnetic field. Using spectral Paschen-Back model descriptions, we determine that OW J1753-3107 exhibits the following physical parameters: T_eff = 15430 K, log(g) = 9.0, log(N(C)/N(He)) = -1.2, and the mean magnetic field strength is B_z =2.1 MG. This relatively low temperature and carbon abundance (compared to the expected properties of hot DQs) is similar to that seen in the other warm DQ SDSS J1036+6522. Although OW J1753-3107 appears to be a twin of SDSS J1036+6522, it exhibits a modulation on a period slightly longer than the dominant period in SDSS J1036+6522 and has a higher carbon abundance. The source of variations is uncertain, but they are believed to originate from the rotation of the magnetic white dwarf.", "descriptionType": "Abstract", "lang": null }, { "description": "11 pages, 8 figures, 7 tables. 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One the main unsolved questions is the geometry of the mass-loss process. Taking advantage of the results from the Herschel Mass loss of Evolved StarS (MESS) programme, we initiated a coordinated effort to characterise the geometry of mass loss from evolved red giants at various spatial scales. For this purpose we used the MID-infrared interferometric Instrument (MIDI) to resolve the inner envelope of 14 asymptotic giant branch stars (AGBs) in the MESS sample. In this contribution we present an overview of the interferometric data collected within the frame of our Large Programme, and we also add archive data for completeness. We studied the geometry of the inner atmosphere by comparing the observations with predictions from different geometric models. Asymmetries are detected for five O-rich and S-type, suggesting that asymmetries in the N band are more common among stars with such chemistry. We speculate that this fact is related to the characteristics of the dust grains. Except for one star, no interferometric variability is detected, i.e. the changes in size of the shells of non-mira stars correspond to changes of the visibility of less than 10%. The observed spectral variability confirms previous findings from the literature. The detection of dust in our sample follows the location of the AGBs in the IRAS colour-colour diagram: more dust is detected around oxygen-rich stars in region II and in the carbon stars in region VII. The SiC dust feature does not appear in the visibility spectrum of UAnt and SSct, which are two carbon stars with detached shells. This finding has implications for the theory of SiC dust formation.", "descriptionType": "Abstract", "lang": null }, { "description": "43 pages, 31 figures; accepted for publication in Astronomy &amp; Astrophysics. Abstract shortened for compilation reasons. 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{ "alternateIdentifiers": [], "citationCount": 0, "citationsOverTime": [], "container": { "identifier": null, "identifierType": null, "type": null, "title": null, "firstPage": null, "issue": null, "volume": null }, "contentUrl": null, "contributors": [], "created": "2023-07-04T12:00:06", "creators": [ { "affiliation": null, "name": "Takamoto, Akira", "nameIdentifiers": null, "familyName": "Takamoto", "givenName": "Akira", "nameType": null }, { "affiliation": null, "name": "Takahashi, Tomoki", "nameIdentifiers": null, "familyName": "Takahashi", "givenName": "Tomoki", "nameType": null }, { "affiliation": null, "name": "Togami, Kazuki", "nameIdentifiers": null, "familyName": "Togami", "givenName": "Kazuki", "nameType": null }, { "affiliation": null, "name": "Hishinuma, Ai", "nameIdentifiers": null, "familyName": "Hishinuma", "givenName": "Ai", "nameType": null } ], "dates": [ { "date": "2023-07-04", "dateType": "Created", "dateInformation": null }, { "date": "2023-08-11", "dateType": "Updated", "dateInformation": null }, { "date": "2023", "dateType": "Issued", "dateInformation": null } ], "descriptions": [ { "description": "Dolomite [CaMg(CO<sub>3</sub>)<sub>2</sub>] is used widely for soybean, <i>Glycine max</i> (L.) Merr. production in Japan to amend soil pH, although magnesium (Mg) in the dolomite can inhibit soybean growth and yield. The aim of this study was to determine the effect of Mg on soybean production in a paddy – upland rotation field in 2019–2020 by comparing the application of dolomite and calcite (CaCO<sub>3</sub>). The field test included three replications with three lime applications: control (no lime), dolomite, and calcite. The amounts of calcite and dolomite applied in each year were 253 and 259 g m<sup>−2</sup>, respectively. The soil pH was almost similar following the applications of calcite and dolomite, whereas the soil exchangeable Mg content was higher following the dolomite application than following the other applications. The shoot weight from the flowering stage to the harvest stage (R8) was significantly higher following the calcite application than following the other applications. However, the shoot weight at only R8 was significantly higher following the dolomite application than following the control application. In 2019–2020, the highest soybean yield was resulted from the calcite application (355 g m<sup>−2</sup>) followed by the dolomite application (312 g m<sup>−2</sup>) and the control application (260 g m<sup>−2</sup>). The severity of soil-borne diseases was lower following the dolomite application than following the control application, and even lower following the calcite application. 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dois
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Three sets of on-farm experiments – namely, trials of (1) nitrogen (N) fertilizer use (replicated trials and surveys of farmer fields, including plots receiving zero N), (2) resistant varieties, and (3) shallow irrigation management – were conducted from 2015 to 2017 in three communes in Nam Dinh province in the RRD, to assess technologies adaptable by farmers. Yield response to N fertilizer in the farmer fields was generally low and greatly variable, associated with variable N recovery efficiency and internal efficiency, particularly in fields at risk of high salinity in one commune during summer 2015. In the replicated trials, N fertilizer applied at 120–180 kg N ha<sup>−1</sup> in spring and 100–150 kg N ha<sup>−1</sup> in summer generally produced better yields with higher N use efficiencies than the current high N application rates (219 and 189 kg N ha<sup>−1</sup>, respectively). Recently developed salinity-resistant varieties M2 and M14 produced almost 50% higher yield than the conventional varieties in very saline at-risk fields. Shallow water-depth management (&lt;5 cm) resulted in grain yields similar to the higher conventional-depth management in the less saline fields, but significantly reduced yields in the more saline fields. These results indicate potential resource-use efficiencies in rice production to better cope with coastal salinity in the RRD.", "descriptionType": "Abstract", "lang": null } ], "doi": "10.6084/m9.figshare.23211348.v1", "downloadCount": 0, "downloadsOverTime": [], "formats": [], "fundingReferences": [], "geoLocations": [], "identifiers": [], "isActive": true, "language": null, "metadataVersion": 2, "partCount": 0, "partOfCount": 0, "prefix": "10.6084", "publicationYear": 2023, "published": "2023", "publisher": { "name": "Taylor & Francis" }, "reason": null, "referenceCount": 0, "registered": "2023-05-26T06:20:05", "relatedIdentifiers": [ { "relatedIdentifier": "10.6084/m9.figshare.23211348", "relatedIdentifierType": "DOI", "relationType": "IsIdenticalTo", "resourceTypeGeneral": null, "relatedMetadataScheme": null, "schemeType": null, "schemeUri": null }, { "relatedIdentifier": "10.1080/1343943x.2023.2215448", "relatedIdentifierType": "DOI", "relationType": "IsSupplementTo", "resourceTypeGeneral": null, "relatedMetadataScheme": null, "schemeType": null, "schemeUri": null } ], "relatedItems": [], "rightsList": [ { "rights": "Creative Commons Attribution 4.0 International", "rightsUri": "https://creativecommons.org/licenses/by/4.0/legalcode", "rightsIdentifier": "cc-by-4.0", "rightsIdentifierScheme": "SPDX", "schemeUri": "https://spdx.org/licenses/" } ], "schemaVersion": "http://datacite.org/schema/kernel-4", "sizes": [ "170788 Bytes" ], "source": "mds", "state": "findable", "subjects": [ { "lang": null, "subject": "Pharmacology", "subjectScheme": null, "schemeUri": null, "valueUri": null, "classificationCode": null }, { "lang": null, "subject": "Environmental Sciences not elsewhere classified", "subjectScheme": null, "schemeUri": null, "valueUri": null, "classificationCode": null }, { "lang": null, "subject": "Ecology", "subjectScheme": null, "schemeUri": null, "valueUri": null, "classificationCode": null }, { "lang": null, "subject": "FOS: Biological sciences", "subjectScheme": "Fields of Science and Technology (FOS)", "schemeUri": "http://www.oecd.org/science/inno/38235147.pdf", "valueUri": null, "classificationCode": null } ], "suffix": "m9.figshare.23211348.v1", "titles": [ { "title": "On-farm agronomic manipulations to improve rice (<i>Oryza sativa</i> L.) production in the saline coastal zone of the Red River Delta in Vietnam", "lang": null, "titleType": null } ], "types": { "bibtex": "article", "citeproc": "article-journal", "resourceType": "Journal contribution", "resourceTypeGeneral": "Text", "ris": "RPRT", "schemaOrg": "ScholarlyArticle" }, "updated": "2023-08-11T11:20:16", "url": "https://tandf.figshare.com/articles/journal_contribution/On-farm_agronomic_manipulations_to_improve_rice_i_Oryza_sativa_i_L_production_in_the_saline_coastal_zone_of_the_Red_River_Delta_in_Vietnam/23211348/1", "version": null, "versionCount": 0, "versionOfCount": 0, "viewCount": 0, "viewsOverTime": [] }
10.6084/m9.figshare.23211348.v1
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dois
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DataCite Resource Type General "Text/Other" Reclassification Records

Overview

This dataset contains DataCite metadata records that were originally classified with resourceTypeGeneral values of "Text" or "Other."

Dataset Structure

Each record is a JSON object containing the record:

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