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scida: scalable analysis for scientific big data

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Chris Byrohl, Dylan Nelson

Instrumentation and Methods for Astrophysics · Submitted 27 Feb 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim scida is a Python package for reading and analyzing large scientific data sets with support for various cosmological and galaxy formation simulations out-of-the-box.

how it is stated · asserted 1.00 read the record →

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arXiv ID2402.17818 (version 1)
Pith IDVKIXXGO5, Pith's permanent record of this paper and its review
CitationChris Byrohl, Dylan Nelson. "scida: scalable analysis for scientific big data." arXiv:2402.17818 (2024). https://pith.science/paper/VKIXXGO5
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,161 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.08 · human participants 0.02 · controlled comparison 0.01 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.04 · mentions code or data availability 0.41.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

scida is a Python package for reading and analyzing large scientific data sets with support for various cosmological and galaxy formation simulations out-of-the-box. Data access is provided through a hierarchical dictionary-like data structure after a simple load() function. Using the dask library for scalable, parallel and out-of-core computation, all computation requests from a user session are first collected in a task graph. Arbitrary custom analysis, as well as all available dask (array) operations, can be performed. The subsequent computation is executed only upon request, on a target resource (e.g. a HPC cluster).

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 scida is a Python package for reading and analyzing large scientific data sets with support for various cosmological and galaxy formation simulations out-of-the-box. contribution 1.00 0.56 0.15 asserted 1.00
s1 Data access is provided through a hierarchical dictionary-like data structure after a simple load() function. method 0.93 0.58 0.04 asserted 1.00
s2 Using the dask library for scalable, parallel and out-of-core computation, all computation requests from a user session are first collected in a task graph. method 0.99 0.54 0.05 asserted 1.00
s3 Arbitrary custom analysis, as well as all available dask (array) operations, can be performed. method 0.85 0.44 0.06 asserted 0.97
s4 The subsequent computation is executed only upon request, on a target resource (e.g. a HPC cluster). method 0.99 0.49 0.08 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.51.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

scida is a Python package for reading and analyzing large scientific data sets with support for various cosmological and galaxy formation simulations out-of-the-box. Data access is provided through a hierarchical dictionary-like data structure after a simple load() function. Using the dask library for scalable, parallel and out-of-core computation, all computation requests from a user session are first collected in a task graph. Arbitrary custom analysis, as well as all available dask (array) operations, can be performed. The subsequent computation is executed only upon request, on a target resource (e.g. a HPC cluster).

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