Dataset Information

Crowdsourcing as an Analytical Method: Metrology of Smartphone Measurements in Heritage Science.

ABSTRACT: This research assesses the precision, repeatability, and accuracy of crowdsourced scientific measurements, and whether their quality is sufficient to provide usable results. Measurements of colour and area were chosen because of the possibility of producing them with smartphone cameras. The quality of the measurements was estimated experimentally by comparing data contributed by anonymous participants in heritage sites with reference measurements of known accuracy and precision. Participants performed the measurements by taking photographs with their smartphones, from which colour and dimensional data could be extracted. The results indicate that smartphone measurements provided by citizen scientists can be used to measure changes in colour, but that the performance is strongly dependent on the measured colour coordinate. The same method can be used to measure areas when the difference in colour with the neighbouring areas is large enough. These results render the method useful in some heritage science contexts, but higher precision would be desirable.

SUBMITTER: Brigham R

PROVIDER: S-EPMC6055710 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Crowdsourcing as an Analytical Method: Metrology of Smartphone Measurements in Heritage Science.

Brigham Rosie R Grau-Bové Josep J Rudnicka Anna A Cassar May M Strlic Matija M

Angewandte Chemie (International ed. in English) 20180528 25

This research assesses the precision, repeatability, and accuracy of crowdsourced scientific measurements, and whether their quality is sufficient to provide usable results. Measurements of colour and area were chosen because of the possibility of producing them with smartphone cameras. The quality of the measurements was estimated experimentally by comparing data contributed by anonymous participants in heritage sites with reference measurements of known accuracy and precision. Participants per ...[more]

PMID: 29645325

Similar Datasets

Project description:Crowdsourcing linguistic phenomena with smartphone applications is relatively new. In linguistics, apps have predominantly been developed to create pronunciation dictionaries, to train acoustic models, and to archive endangered languages. This paper presents the first account of how apps can be used to collect data suitable for documenting language change: we created an app, Dialäkt Äpp (DÄ), which predicts users' dialects. For 16 linguistic variables, users select a dialectal variant from a drop-down menu. DÄ then geographically locates the user's dialect by suggesting a list of communes where dialect variants most similar to their choices are used. Underlying this prediction are 16 maps from the historical Linguistic Atlas of German-speaking Switzerland, which documents the linguistic situation around 1950. Where users disagree with the prediction, they can indicate what they consider to be their dialect's location. With this information, the 16 variables can be assessed for language change. Thanks to the playfulness of its functionality, DÄ has reached many users; our linguistic analyses are based on data from nearly 60,000 speakers. Results reveal a relative stability for phonetic variables, while lexical and morphological variables seem more prone to change. Crowdsourcing large amounts of dialect data with smartphone apps has the potential to complement existing data collection techniques and to provide evidence that traditional methods cannot, with normal resources, hope to gather. Nonetheless, it is important to emphasize a range of methodological caveats, including sparse knowledge of users' linguistic backgrounds (users only indicate age, sex) and users' self-declaration of their dialect. These are discussed and evaluated in detail here. Findings remain intriguing nevertheless: as a means of quality control, we report that traditional dialectological methods have revealed trends similar to those found by the app. This underlines the validity of the crowdsourcing method. We are presently extending DÄ architecture to other languages.

Project description:Modern histopathology is built on the cornerstone principle of tissue fixation, however there are currently no analytical methods of detecting fixation and as a result, in clinical practice fixation is highly variable and a persistent source of error. We have previously shown that immersion in cold formalin followed by heated formalin is beneficial for preservation of histomorphology and have combined two-temperature fixation with ultra-sensitive acoustic monitoring technology that can actively detect formalin diffusing into a tissue. Here we expand on our previous work by developing a predictive statistical model to determine when a tissue is properly diffused based on the real-time acoustic signal. We trained the model based on the morphology and characteristic diffusion curves of 30 tonsil cores. To test our model, a set of 87 different tonsil samples were fixed with four different protocols: dynamic fixation according to our predictive algorithm (C/H:Dynamic, N = 18), gold-standard 24 hour room temperature (RT:24hr, N = 24), 6 hours in cold formalin followed by 1 hour in heated formalin (C/H:6+1, N = 21), and 2 hours in cold formalin followed by 1 hour in heated formalin (C/H:2+1, N = 24). Digital pathology analysis revealed that the C/H:Dynamic samples had FOXP3 staining that was spatially uniform and statistically equivalent to RT:24hr and C/H:6+1 fixation protocols. For comparison, the intentionally underfixed C/H:2+1 samples had significantly suppressed FOXP3 staining (p<0.002). Furthermore, our dynamic fixation protocol produced bcl-2 staining concordant with standard fixation techniques. The dynamically fixed samples were on average only submerged in cold formalin for 4.2 hours, representing a significant workflow improvement. We have successfully demonstrated a first-of-its-kind analytical method to assess the quality of fixation in real-time and have confirmed its performance with quantitative analysis of downstream staining. This innovative technology could be used to ensure high-quality and standardized staining as part of an expedited and fully documented preanalytical workflow.

Dataset Information

Crowdsourcing as an Analytical Method: Metrology of Smartphone Measurements in Heritage Science.

Publications

Crowdsourcing as an Analytical Method: Metrology of Smartphone Measurements in Heritage Science.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure