Evidence map›Paper›PMID 33654094›Full record

ArticleNature communications2021

Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography.

Jana Dambrogio, Amanda Ghassaei, Daniel Starza Smith, Holly Jackson, Martin L Demaine, Graham Davis, David Mills, Rebekah Ahrendt, Nadine Akkerman, David van der Linden and 1 more

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jana Dambrogio *Wunsch Conservation Laboratory, Massachusetts Institute of Technology (MIT) Libraries, Cambridge, MA, USA. jld@mit.edu.ORCID http://orcid.org/0000-0003-1597-5866
Amanda Ghassaei *Adobe Research, San Francisco, CA, USA. amanda.ghassaei@cba.mit.edu.ORCID http://orcid.org/0000-0002-7293-9864
Daniel Starza Smith *King's College London, Department of English, Virginia Woolf Building, London, UK.ORCID http://orcid.org/0000-0002-1582-1202
Holly Jackson *Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-6898-6767
Martin L DemaineComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Graham DavisQueen Mary University of London, Institute of Dentistry, London, UK. g.r.davis@qmul.ac.uk.ORCID http://orcid.org/0000-0001-6673-032X
David MillsQueen Mary University of London, Institute of Dentistry, London, UK.ORCID http://orcid.org/0000-0001-7136-4694
Rebekah AhrendtUtrecht University, Department of Media and Culture Studies, Utrecht, The Netherlands.ORCID http://orcid.org/0000-0003-2857-7832
Nadine AkkermanLeiden University, Leiden University Centre for the Arts in Society (LUCAS), Faculty of Humanities/English Literature, Leiden, The Netherlands.ORCID http://orcid.org/0000-0002-3063-2610
David van der LindenRadboud University, Faculty of Arts, Department of History, Art History and Classics, Nijmegen, The Netherlands.ORCID http://orcid.org/0000-0002-7050-1818
Erik D DemaineComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-3803-5703

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computational flattening algorithms have been successfully applied to X-ray microtomography scans of damaged historical documents, but have so far been limited to scrolls, books, and documents with one or two folds. The challenge tackled here is to reconstruct the intricate folds, tucks, and slits of unopened letters secured shut with "letterlocking," a practice-systematized in this paper-which underpinned global communications security for centuries before modern envelopes. We present a fully automatic computational approach for reconstructing and virtually unfolding volumetric scans of a locked letter with complex internal folding, producing legible images of the letter's contents and crease pattern while preserving letterlocking evidence. We demonstrate our method on four letterpackets from Renaissance Europe, reading the contents of one unopened letter for the first time. Using the results of virtual unfolding, we situate our findings within a novel letterlocking categorization chart based on our study of 250,000 historical letters.

Identifiers

PMID33654094
PMCPMC7925573

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.