Evidence map›Paper›PMID 36591387›Full record

ArticleData in brief2023

CIVIT dataset: Integral microscopy with Fourier plane recording.

Sergio Moreschini, Filipe Gama, Robert Bregovic, Atanas Gotchev

Abstract read
In one paragraph

Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Sergio MoreschiniTampere University, Korkeakoulunkatu 1, 33720 Tampere, Finland.
Filipe GamaTampere University, Korkeakoulunkatu 1, 33720 Tampere, Finland.
Robert BregovicTampere University, Korkeakoulunkatu 1, 33720 Tampere, Finland.
Atanas GotchevTampere University, Korkeakoulunkatu 1, 33720 Tampere, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article describes a dataset of synthetic images representing biological scenery as captured by a Fourier Lightfield Microscope (FLMic). It includes 22,416 images related to eight scenes composed of 3D models of objects typical for biological samples, such as red blood cells and bacteria, and categorized into Cells and Filaments groups. For each scene, two types of image data structures are provided: 51 × 51 Elemental Images (EIs) representing Densely Sampled Light Fields (DSLF) and 201 images composing Z-Scans of the scenes. Auxiliary data also includes information about camera intrinsic and extrinsic calibration parameters, object descriptions, and MATLAB scripts for camera pose compensation. The images have been generated using Blender. The dataset can be used to develop and assess methods for volumetric reconstruction from Light Field (LF) images captured by a FLMic.

Indexed as

BlenderCellsDCR, Dynamic Cutting RegionDSFL, Densely Sampled Light FieldEI, Elemental ImageFilamentsFiMic, Fourier Integral MicroscopeFLMic, Fourier Lightfield MicroscopeFourier lightfield microscopyGT, Ground TruthLF, Light FieldLight fieldNA, Numerical AppertureRBC, Red Blood CellRoI, Region of InterestZ-scanZ-stack

Identifiers

PMID36591387
PMCPMC9801074

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.