AiiDAlab – an ecosystem for developing, executing, and sharing scientific workflows

Yakutovich, Aliaksandr V.; Eimre, Kristjan; Schütt, Ole; Talirz, Leopold; Adorf, Carl S.; Andersen, Casper Welzel; Ditler, Edward; Du, Dou; Passerone, Daniele; Smit, Berend; Marzari, Nicola; Pizzi, Giovanni; Pignedoli, Carlo A.

doi:10.1016/j.commatsci.2020.110165

Cited by 59 publications

(49 citation statements)

References 18 publications

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“…2 c, 2 d, 3 i, 3j, and S3b. We used the CP2K code 47 and the AiiDAlab platform 48 . The electronic states were expanded with a TZV2P Gaussian basis set 49 for carbon and hydrogen atoms and a DZVP basis set for gold atoms.…”

Section: Methodsmentioning

confidence: 99%

Lightwave-driven scanning tunnelling spectroscopy of atomically precise graphene nanoribbons

et al. 2021

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Atomically precise electronics operating at optical frequencies require tools that can characterize them on their intrinsic length and time scales to guide device design. Lightwave-driven scanning tunnelling microscopy is a promising technique towards this purpose. It achieves simultaneous sub-ångström and sub-picosecond spatio-temporal resolution through ultrafast coherent control by single-cycle field transients that are coupled to the scanning probe tip from free space. Here, we utilize lightwave-driven terahertz scanning tunnelling microscopy and spectroscopy to investigate atomically precise seven-atom-wide armchair graphene nanoribbons on a gold surface at ultralow tip heights, unveiling highly localized wavefunctions that are inaccessible by conventional scanning tunnelling microscopy. Tomographic imaging of their electron densities reveals vertical decays that depend sensitively on wavefunction and lateral position. Lightwave-driven scanning tunnelling spectroscopy on the ångström scale paves the way for ultrafast measurements of wavefunction dynamics in atomically precise nanostructures and future optoelectronic devices based on locally tailored electronic properties.

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Section: Methodsmentioning

confidence: 99%

Lightwave-driven scanning tunnelling spectroscopy of atomically precise graphene nanoribbons

et al. 2021

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“…Works focusing on provenance per se such as (Alterovitz et al, 2018;Ellison et al, 2020) and the various workflow provenance systems such as (Khan et al, 2019;Papadimitriou et al, 2021;Yakutovich et al, 2021) are primarily concerned with very detailed documentation of each computation on one or more datasets. The W3C PROV model (Gil et al, 2013;Lebo et al, 2013;Moreau et al, 2013) was developed initially to support interoperability across the transformation logs of workflow systems.…”

Section: Related Workmentioning

confidence: 99%

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

et al. 2021

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Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result’s metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

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“…Works focusing on provenance per se such as (Alterovitz et al 2018;Ellison et al 2020) and the various workflow provenance systems such as (Khan et al 2019;Papadimitriou et al 2021;Yakutovich et al 2021) are primarily concerned with very detailed documentation of each computation on one or more datasets. The W3C PROV model (Gil et al 2013;Lebo et al 2013;Moreau et al 2013) was developed initially to support interoperability across the transformation logs of workflow systems.…”

Section: Related Workmentioning

confidence: 99%

FAIRSCAPE: A Framework for FAIR and Reproducible Biomedical Analytics

Levinson

Niestroy

Manir

et al. 2020

Preprint

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Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis consists of accessible data and software with runtime environment and personnel involved. Evidence graphs - a derivation of argumentation frameworks adapted to biological science - can provide this disclosure as machine-readable metadata resolvable from persistent identifiers for computationally generated graphs, images, or tables, that can be archived and cited in a publication including a persistent ID. We have built a cloud-based, computational research commons for predictive analytics on biomedical time series datasets with hundreds of algorithms and thousands of computations using a reusable computational framework we call FAIRSCAPE. FAIRSCAPE computes a complete chain of evidence on every result, including software, computations, and datasets. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves the provenance graph across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software. FAIRSCAPE is a reusable computational framework, enabling simplified access to modern scalable cloud-based components. It fully implements the FAIR data principles and extends them to provide FAIR Evidence, including provenance of datasets, software and computations, as metadata for all computed results

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AiiDAlab – an ecosystem for developing, executing, and sharing scientific workflows

Cited by 59 publications

References 18 publications

Lightwave-driven scanning tunnelling spectroscopy of atomically precise graphene nanoribbons

Lightwave-driven scanning tunnelling spectroscopy of atomically precise graphene nanoribbons

FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

FAIRSCAPE: A Framework for FAIR and Reproducible Biomedical Analytics

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