dataesr/scientific-paragraphs-categorization huggingface dataset:
The improvement after applying the rolling shutter correction is calculated based on the variation of 3D RMSE. The best configuration for each dataset is highlighted in red. Table  4 , 5: For dataset 30m-nadir and 90m-nadir in block configuration, the addition of affine distortion coefficients improves the accuracy in planimetry, especially with the original datasets. The planimetric accuracy is further improved after the rolling shutter effect correction. The best results are obtained after the correction of rolling shutter effect, both camera calibration models give comparable result with the 10-parameter model outperforming slightly the 8-parameter one. The dataset 2 (90m-nadir) gives better results compared to the dataset 1 (30m-nadir) when the rolling shutter effect is corrected, which is probably due to the fact that the results are less sensitive to the estimation error of camera velocity (due to rounded time information) with a higher surveying speed.
AgiSoft MetaShape There is not much information on how the rolling shutter correction is im-320 plemented in AgiSoft MetaShape. What is known is that the software gives information on rotational and translational velocity for every image. However, it is not clear if the parameters are estimated during the bundle block adjustment or calculated a posteriori. Table  8 , 9: in the case where no rolling shutter correction is performed, the inclusion of affine distortion coefficients improves substantially the planimetric accuracy. The correction of rolling shutter effect does improve the results when processed with the 8-parameter camera model; the error on planimetry is reduced considerably whereas on altimetry the error increases slightly. The best results are obtained in the cases where the original datasets are processed with 330 the 10-parameter camera model, whereas the correction of rolling shutter effect degrades the accuracy.
Conclusion and perspective In this paper, the rolling shutter effect in aerial photogrammetry is addressed. We proposed an approach to calibrate the readout time of rolling shutter cameras as well as a two-step method for the rolling shutter effect cor- The proposed rolling shutter correction method performs under the assumption that the deformation due to camera orientation change is negligible compared to that of camera position change. In our case, the datasets are acquired with a camera which is alleged to provide a stable, smooth footage for image acquisition thanks to a 3-axis gimbal, which conforms this assumption. However, the time related information registered in the image metadata is rounded to second, which can limit the performance of the presented method. Despite this, with a consumer-grade drone/camera and the provided information in metadata, the approach presented in this article is able to improve the accuracy of photogrammetric results with a robust performance.
Several conclusions can be drawn. To begin with, the inclusion of affine distortion coefficients improves the results when the rolling shutter effect is present. It is comprehensive since the deformation introduced by rolling shutter effect is similar to an affine distortion on a flat scene. Pix4D only supports the 8-parameter camera calibration model, no further studies can be carried out. For AgiSoft MetaShape, when applying a rolling shutter effect correction, it is recommended not to include the affine distortion coefficients since there is a risk of over-parameterization. The proposed method works with both camera models without the risk of over-parameterization and gives comparable results to other two software. Secondly, the flight configuration is an essential factor for rolling shutter correction in AgiSoft MetaShape and Pix4D.
For each image, several parameters in addition are estimated during the bundle block adjustment to describe the change of camera pose during acquisition. When a favorable block configuration is presented, such as for instance, nadir images of different flight heights as well as oblique images, both software turn out to work fine. For a less favorable configuration where there is only nadir images with one single flight height, the performance of the rolling shutter correction degrades. In certain cases, AgiSoft MetaShape even shows reduced accuracy after applying the correction. For corridor configuration, none of these two software works as expected. On the contrary, the method proposed in this paper is robust to the flight configuration. To correct the deformation in image space, the estimation of the camera velocity during exposure is carried out after the bundle block adjustment, therefore does not have specific requirements on the flight configuration.
It is the only method that works for both block and corridor configuration datasets. Though the method proposed in this article gives satisfying results in all configurations, in the case where a good flight configuration is present (oblique and nadir images of different flight heights), the other two methods which estimate the camera pose variation show better performance. It is partly due to the compromised time related information explored for camera velocity estimation in this two-step method. A further improvement is expected if an accurate timestamp is provided in the metadata or if the camera positions are directly available with the aid of, for example, GNSS data. Moreover, the assumption of the camera orientation variation being negligible compared to the camera position change can slightly degrade the performance of the proposed method.
datasets are processed, two nadir-looking block configuration datasets at a flight height of 30m and 90m, respectively; one block configuration dataset consisting of the two first datasets and one oblique-looking dataset at 60m; and one corridor configuration dataset conducted at 40m. Each dataset is processed with two camera calibration models: a 8-parameter model and a 10-parameter model which has two affine distortion coefficients in addition. The ground points are separated into two sets to serve as GCPs and CPs; each dataset is processed twice with the 2 point sets as GCPs to eliminate the occasionality. The proposed method improves the accuracy by 30%-60% for block configuration and 15%-25% for corridor configuration. Comparison is conducted with other two commercial software, AgiSoft MetaShape and Pix4D.
Four datasets are processed, two nadir-looking block configuration datasets at a flight height of 30m and 90m, respectively; one block configuration dataset consisting of the two first datasets and one oblique-looking dataset at 60m; and one corridor configuration dataset conducted at 40m. Each dataset is processed with two camera calibration models: a 8-parameter model and a 10-parameter model which has two affine distortion coefficients in addition. The ground points are separated into two sets to serve as GCPs and CPs; each dataset is processed twice with the 2 point sets as GCPs to eliminate the occasionality. The proposed method improves the accuracy by 30%-60% for block configuration and 15%-25% for corridor configuration. Comparison is conducted with other two commercial software, AgiSoft MetaShape and Pix4D.
(2015)  and  Goldstein et al. (2015). Photometric coverage of DES16C3cje was acquired by DES-SN in griz filters from 2016 October until 2017 February, and then from 2017 August to 2018 February. Between 2017 February and 2017 July, additional photometric data were obtained by the extended Public European Southern Observatory (ESO) Spectroscopic Survey for Transient Objects (ePESSTO;  Smartt et al. 2015)  and other collaborators with the ESO Faint Object Spectrograph and Camera 2 (EFOSC2;  Buzzoni et al. 1984 ) at the 3. 6m ESO New Technology Telescope (NTT), with the FOcal Reducer/low dispersion Spectrograph 2 (FORS2;  Appenzeller et al. 1998 ) at the ESO Very Large Telescope (VLT), with the Low Dispersion Survey Spectrograph 3 (LDSS3;  Osip et al. 2004 ) on the Magellan Clay 6. 5-m telescope, and with the the Gamma-Ray Burst Optical/Near-Infrared Detector (GROND;  Greiner et al.
2008) , at the 2. 2-m MPG telescope at the European Southern Observatory (ESO) La Silla Observatory. The NTT data were reduced using the PESSTO pipeline  (Smartt et al. 2015) , while for the FORS2 images we used the esoreflex pipeline  (Freudling et al. 2013). Reductions for data obtained with LDSS3 were performed with Image Reduction and Analysis Facility (iraf; Tody 1986) using standard routines. Images from the MPG were reduced with the GROND pipeline  (Krühler et al. 2008). The DES photometric measurements were made using the pipeline discussed by  Papadopoulos et al. (2015)  and  Smith et al. (2016) , which has also been extensively used in the literature (e. g. ,  Firth et al. 2015, and references therein). This pipeline subtracts a deep template image from each individual DES image to remove the host-galaxy light using a point-spread-function (PSF) matching routine.
SN photometry is then measured from the difference image using a PSF-fitting technique. The photometry of DES16C3cje is reported in Appendix A1. DES16C3cje was observed spectroscopically on six epochs from +47 to +403 days (throughout the paper, we give all epochs relative to the explosion epoch). These observations were obtained with four different instruments: The AAOmega spectrograph at the Anglo-Australian Telescope (AAT), X-SHOOTER  (Vernet et al. 2011 ) and FORS2 at the VLT, and Gemini Multi-Object Spectrograph (GMOS-S;  Hook et al. 2004 ) at the Gemini Observatory. A log of the spectroscopic observations of DES16C3cje is reported in Table  1. Spectroscopic reductions for X-SHOOTER were performed using the esoreflex pipeline, FORS2 data were reduced with iraf using standard routines, while for GMOS-S we used the Gemini iraf package, combined with idl routines to flux calibrate the data and remove telluric lines.
Part of the funding for GROND (both hardware as well as personnel) was generously granted from the Leibniz-Prize to Prof. G. Hasinger (DFG grant HA 1850/28-1). This paper includes data gathered with the 6. 5 meter Magellan Clay Telescope located at Las Campanas Observatory, Chile under the programme 100type IA Supernovae (100IAS) survey. This work has been partially supported by the Spanish grant PGC2018-095317-B-C21 within the European Funds for Regional Development (FEDER). The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015- This manuscript has been authored by Fermi Research Alliance, LLC under Contract No.
ACKNOWLEDGEMENTS We are grateful to  Pedro Lacerda  for performing some of the observations used in this work. We thank the anonymous referee for useful suggestions. CPG and MS acknowledge support from  EU/FP7-ERC  grant No. [615929]. LG was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No. 839090. TWC acknowledgments the funding provided by the  Alexander von Humboldt Foundation. MF is supported by a  Royal Society -Science Foundation Ireland University Research Fellowship. MG is supported by the  Polish NCN MAESTRO  grant  2014/14/A/ST9/00121. MN is supported by a  Royal Astronomical Society Research Fellowship. This work used data collected at the  European Organisation for Astronomical Research in the Southern Hemisphere, Chile , under program IDs: 299.
Results Validation on simulated datasets. Figure  2  shows the outcome of the proposed three-step reconstruction algorithm for three examples of the synthetic validation set corresponding to different cell densities-low, medium and high. The number of cells contained within the entire 1000 × 1000-pixel image were 1001, 2547 and 4569 corresponding respectively to densities of 358, 909 and 1627 cells/mm 2. Note, the hologram of the high density case is speckle-like. As expected, objects recovered by the first (old) reconstruction, shown in Fig. 2c  are degraded, with the presence of numerous wrapping errors in the OPD map (occurring when L exceeds /2 ). Absorption maps A show cells outlined with absorbing circle artefacts. In the high density case, the first holographic reconstruction results are barely intelligible.
2 ). The first (old) holographic reconstruction results are barely intelligible. In comparison, L and A images predicted by the CNN and obtained by the final (new) reconstruction present clear pictures of rounded cells. All phase wrapping problems are thus efficiently solved. The maximum obtained OPD values are about 1 µm which corresponds to a phase shift of approximately 5π (see Fig. 4h , = 405 nm ). In agreement with the simulated data, the last reconstruction step improves the matching between our model and measured data (see convergence plot in Fig. S1 ). This improvement brought by the third step can also be seen on the resulting image. Figure  4j  points out errors introduced by the CNN but corrected by the third step. Fluorescence microscopy was used to validate the positions and the shapes of the cells in the reconstructed images.
After the first holographic reconstruction, the image appears degraded (Fig. 6a, d, h, l ). The large thickness of the cells results in several phase wrapping artefacts. The neuron heads are reconstructed with negative values (see blue profile in Fig. 6g ). Finer morphological features are not well reconstructed, for instance the neurites which are only 1 to 3 μm wide. The CNN prediction performs well phase unwrapping at the location of the neuron cell body (see orange profile in Fig. 6g ), but the complexity of the scene is lost. The CNN prediction presents only rounded spots with positive phase values (Fig. 6b, e, i, m ). Details such as the neurites are lost in the process (see orange profile in Fig. 6k ). Obviously these details were not present in the training dataset, the CNN output is thus facing here a generalization problem.
In addition, the CNN predicts the presence of rounded spots that are not present in the scene (see orange profile in Fig. 6o ). The CNN output is thus facing here a hallucination problem. Notably, the final reconstruction corrects these CNN predictions (see green profiles in Fig. 6k, o ). This example shows the good complementarity of the two algorithms. The CNN addresses the phase unwrapping and the final reconstruction retrieves the finer details that are lost in the CNN prediction. The PC12 cells could therefore be well reconstructed using the alternation approach whereas the complexity of these images was not accounted in the CNN training data set. As a last example, Fig. S5  depicts a case, namely the culture of adherent fibroblast cells, where the three steps approach was not successful.
Discussion In this article, we introduce a novel reconstruction approach, based on alternations between an inverse problem approach and deep learning using CNN to improve lens-free image reconstruction. We have designed the CNN to perform phase unwrapping on the acquisition of cells in suspension. CNN has been trained on synthetic datasets and we demonstrate the generalization to real samples. On a sample of PC3 cells in suspension, we show an effective phase unwrapping up to phase shift of 5 π. With this method we succeeded in reconstructing cells in suspension at large cell densities of up to ∼ 1000 cells/mm 2. This allows us to observe 35,000 cells simultane- ously in a field of view of 30 mm 2 with our lens-free system. According to simulations, at this concentration, the method is quantitative for the determination of the cell refractive index (real part).
We constructed a simple CNN network consisting of 20 repetitions of a three layers pattern, namely a convolution layer ( 5 × 5 pixel, 32 features), a batch normalization layer and a ReLU activation func- tion (see a sketch of the CNN structure in Fig. 9 ). The final layer before the regression layer was a convolution layer with 2 features (L and A outputs) so as to match the ground truth image dimensions. The network does not perform any dimension changes, allowing images of any size to be used as training input. To train a CNN for unwrapping lens-free reconstructed phase images, the ground truth consisted of a synthetic dataset of 1000 image pairs ( 1000 × 1000 pixels for L and A) of cells in suspension. The CNN input contains the correspond- ing reconstructed images ( L recons and A recons ).
Two sets of vignettes (size 121 × 121 ) were extracted randomly from the reconstructed images and used during the CNN training. CNN training was implemented with the Matlab deep learning library using the Adam optimizer. We used 12,800 sets of vignettes per epoch (a set being a vignette couple (L recons , A recons ) as input and a corresponding vignette couple as ground truth). Learning rate was set to 0. 0001 and training was conducted over 10 epochs, lasting 10 h on a PC workstation with a single NVIDIA GTX Titan GPU. Evaluation of the results obtained on synthetic datasets. The simulated reconstructions have been quantitatively compared to the known ground truth, considering the cell relative refractive index values. From the reconstruction (L recons , A recons ) , it is possible to retrieve for each cell the real imaginary parts of the relative refractive index according to the formula:  where k is the index of the cell, S (k) the integration domain calculated knowing the position and radius of the cell, and V (k) the cell volume.
The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it.
Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.
Example study purposes and designs to create data related to health and health endpoints (left) and physicochemical or other performance (right) for evaluations of the intrinsic properties and conditional behaviours of nanomaterials  [30]. Reused with permission from Elsevier, License Number: 4926970512437. Indexing the metadata makes them findable (much like the table of contents or index in a textbook). Probably, the most well-known metadata capture template is the investigation/study/assay (ISA) tab-delimited (TAB) format, which has been extended to nano also (ISA tab nano)  [31, 32] , although this is not widely implemented by researchers or nanosafety databases in practice. Metadata tools span data warehouses or data upload templates, as well as tools to make data more findable and accessible.
Given that tools exist but are not implemented, we will concentrate here on the human factor, either as the contribution of an individual representing a role in the data lifecycle as defined below, or as a community activity or consensus. We hope to highlight that improving the quality of data, handling, and sharing is not the task of just a few individuals Example study purposes and designs to create data related to health and health endpoints (left) and physicochemical or other performance (right) for evaluations of the intrinsic properties and conditional behaviours of nanomaterials  [30]. Reused with permission from Elsevier, License Number: 4926970512437. Indexing the metadata makes them findable (much like the table of contents or index in a textbook). Probably, the most well-known metadata capture template is the investigation/study/assay (ISA) tab-delimited (TAB) format, which has been extended to nano also (ISA tab nano)  [31, 32] , although this is not widely implemented by researchers or nanosafety databases in practice.
e. , from the data producer via the data curators, analysts and data managers to the data user or customers. Another way to frame these questions is how rich metadata, i. e. , data describing the data and thus making it understandable to and re-usable by others, should be collected to achieve the goal of providing findable, accessible, interoperable and reusable (FAIR) high-quality data to the community. A general description of how to improve data quality and FAIRness based on a review of literature and emerging best practices is presented in the first part of the paper. This is complemented by a comprehensive report on the current metadata capturing and reporting practices in nanotechnology and nanosafety research based on responses to a survey distributed amongst stakeholders associated with a range of nanomaterial data resources.
Adopting this workflow within the nanosafety community will strengthen an ability to combine and create bigger, enriched datasets, link together different existing data sources and provide common search, browsing and accessing functionality, as showcased in the Horizon 2020 NanoCommons project. This will finally facilitate the creation of a scientific digital single market as envisioned, for example, by the European Open Science Cloud (EOSC), by removing scientific and technical barriers hindering data access and re-use, which in turn will maximise the added value of the produced data, reduce the need for new (animal) experiments, promote data-driven innovation, speed up the development of novel safer nano-enabled products, and increase the positive socio-economic impact from scientific research.
FAIRification: Science, Library Science or Both? This paper is the latest in a series  [26] [27] [28] [29]  of articles planned by the National Cancer Institute (NCI) Nanotechnology Working Group (Nano WG) Nanomaterial Data Curation Initiative (NDCI) dealing with the emerging field of nanoinformatics and the need for high quality data. The overall objectives of the NDCI include: • capturing a snapshot of current NM data and metadata curation practices and issues, • the development of recommendations for moving the nanoinformatics community toward increasingly standardised curation practices, and • the facilitation of collaborations between researchers, product developers, and others working with NMs that establish and utilise common datasets for cross-boundary work (e. g. , application of data from academic institutions to NM product development in industry).