dataesr/scientific-paragraphs-categorization huggingface dataset:
Integrating Prior Knowledge on Noise Strength Through visual analysis we observe that the geodetic noise patterns in the geostrophic currents occur on two scales. Small spatial scale noise appears as a consistent pattern distribution covering the full ocean field including over currents and other oceanographic features. Due to the small tile size of the training dataset, the noise generating networks in Section 2. 5 are trained to focus on this small-scale noise distribution. However, it is observed that the noise also exhibits a deterministic pattern on a larger spatial scale, in which large patches of strong noise occur consistently across different geodetic products i. e. products produced using different geoid models. It is determined that such patches appear in areas associated with steep gradients in the geoid, and thus, occur along coastlines, around islands and along large seamounts.
Training Process The CDAE network is trained on the constructed training dataset, discussed in Section 2, in which a synthesised noise model is applied to a naturally smooth dataset (via Equations 3 and 4) to construct noisy input and clean target pairs. Training images are chosen to be 128×128 pixels (32 • ×32 • for a 1/4 • map). We found that our denoising network performed best with inputs of this size, which may be due to such region size containing sufficient oceanographic information while limiting the distortion due to the equidistant cylindrical projection onto the 2D grid. Furthermore, regions above (below) a latitude of 64 • N (64 • S) are discarded prior to training to avoid the most extreme projection distortion which occurs at the poles. Generated from a set of 8 climate models (Table  1 ), the CDAE training dataset consists of 217,580 (128 × 128) regions which overlap every 32 pixels (8 • ).
The dataset's properties are summarised in Table  4. During training, each time a synthetic region is loaded, a random noise quilt is selected and applied to the sample to create a clean and noisy training pair. Note that, since the number of quilts is less than the number of training regions, the same quilt may be used multiple times over an epoch. However, the combinations of synthetic samples and noise quilts are unique across each epoch. L MSE (𝑥, 𝑥 ′ ) = 1 𝑊 𝐻 𝑊 ∑︁ 𝑖 𝐻 ∑︁ 𝑗 (𝑥 𝑖, 𝑗 -𝑥 ′ 𝑖, 𝑗 ) 2 , ( 5 ) where 𝑥 is the target; 𝑥 ′ is the network output; 𝑊 and 𝐻 represent width and height of the images (resp. ); and 𝑖, 𝑗 denote a pixel's row and column number. The computed error is then used to improve the network's next prediction through back propagation using gradient descent. As the training images include regions over both ocean and land, land values are ignored during loss calculation to avoid irrelevant pixels negatively influencing back propagation.
We use the Adam  (Kingma and Ba , 2014)  optimisation algorithm and a learning rate of 0. 001 with no decay schedule. Data augmentation is implemented to avoid over-fitting, i. e. when a network learns the specific characteristics of a training dataset too well. Data augmentation increases the variation seen by the network which involves synthesising new data by introducing modifications of each training sample into the training dataset. This was achieved by randomly applying a set of geometric transformations to each training pair, including horizontal and vertical flips, and rotations by a multiple of 90 •. As discussed in Section 2. 7, we observe that large-scale noise patterns occur in regions associated with steep geoid gradients. We, therefore, provide the denoising network with the associated geoid gradients of the loaded input region during training.
Ablation Study on Proposed Components We perform an ablation study of the different processes in creating the input data. We evaluate the respective contributions of the proposed processes in creating a quality dataset, both in turn and in combination. The first modification uses the noise strength map to introduce large scale noise variations to the synthetic noise quilts, detailed in Sections 2. 7 and 2. 8. The second modification involves passing the geoid gradients of the considered regions via an extra channel to the denoising network, detailed in Section 3. 2. It is important to note that without the application of the noise strength map to the synthetic noise which emulates large-scale noise in the synthetic training data, the geoid gradients will not have any correlation with synthetic noise regions and the network would not implicitly be guided to learn the relationship between large-scale noise patches and steep geoid gradients.
Thus, this second process cannot be evaluated in isolation and must always be combined with the first. The third process is random data augmentations, detailed in Section 3. 2. We train each configuration over 80 epochs, with batch size 512 and use 5-fold cross validation to determine the best epoch to be applied to the independent testing data. The process for this is as follows: five variations of the dataset are created where, for each version, a different fifth of the dataset is not used for training but instead used for validation. Then the network is trained from scratch over each five dataset variations and performance is measured on the unseen validation portion for every Table  5. Ablation study of the different processes in creating the final training dataset for the denoising network: using the noise strength map; passing geoid gradients as an extra channel and applying data augmentations.
Results presented are an average over the five folds from the best respective epoch of each model (obtained from the validation results) on the ablation test dataset. To make the ablation study unbiased to a particular data processing method, we require an independent testing dataset to validate results across all trained models. Therefore, to quantitatively assess how well noise is removed and oceanographic features are preserved, we create an ablation testing dataset of noisy input and target pairs. We obtain residual geodetic noise by removing the Gaussian filtered product from the noisy geodetic currents. We then apply this residual geodetic noise onto a subset of the climate model data, kept aside during training. The residual product is useful in this case as it contains realistic noise behaviour such as large scale patches of strong noise which are known to occur.
Results In this section, using the best performing configuration from Section 3. 3, we train the CDAE network over 80 epochs, with a batch size of 512. We use 70% of the synthetic dataset for training, 20% for validation, and set aside 10% as unseen testing data. To facilitate this split, both synthetic clean regions and noise quilts are split to ensure that there is no crossover across datasets. First, we present the training and validation curves and secondly, we demonstrate the ability of the trained CDAE network to denoise unseen synthetic testing data. Thirdly, we assess the performance of the trained CDAE network when applied to real-world geodetic geostrophic currents. Here, a qualitative analysis of the denoised outputs is performed; results are then compared against the conventionally smoothed currents.
For the AC region (Figure  13 : row 3), the network preserves the strength of the AC, while revealing finer details such as the meanders of the retroflection and the intricate current structures to the south, also seen in the synthetic data (although with some differences in details). Furthermore, for the BMCZ region (Figure  13 : row 4), the confluence zone is more clearly resolved after the network has trained for 15 epochs. In particular, the zonal currents along the Sub-Antarctic front of the Antarctic Circumpolar Current are stronger and more detailed (48 • S-58 • S). Across all regions, there is little difference in the current estimates between epochs 15 and 20, indicating that the model has converged and stabilised by epoch 15. Finally, it is worth noting that the success of the CDAE in removing noise without attenuating the underlying currents, indirectly demonstrates the efficacy of using the DCGAN synthesised noise to create a training dataset.
GS Results showed that epoch 16 provided the lowest overall RMSD and MAD for both references, which agrees well with and reinforces the results found on the validation dataset (Section 2. 5), where epoch 15 scored the best. We design a quantitative evaluation metric which focuses on the main components of filtering performance: the preservation of current signal and the level of noise removal. First, an approximate mask outlining the position of each main current is obtained automatically from an MDT. The average velocity is then computed over this mask for each region which we denote as the 'signal-preservation' average. In order to reduce bias towards either filtering method, the recent CNES-CLS18 MDT is used to produce this mask. In practice, geostrophic currents computed from the CNES-CLS18 MDT are thresholded above 0.
Concluding Discussion In this paper, we have presented a deep learning pipeline to remove the noise present in the geostrophic currents computed from a geodetic MDT. A generative network is trained to closely estimate the noise distribution of noisy geodetic data, and then to construct a novel training dataset of contaminated geodetic currents with corresponding noisy-free examples. In order to better represent the real geodetic data in the training data, several techniques have been adopted to prevent overly exaggerated features and to preserve global noise strength patterns. An ablation study on the different data processing methods proposed demonstrated that the use of prior knowledge benefits the network. This strategy of dataset creation is directly applicable to other blind image-denoising problems in the climate sciences in which the clean ground-truth does not exist but model data is available.
Finally, a CDAE network is trained on the constructed training dataset and results are presented on real-world data. Traditional isotropic spatial filtering causes attenuation along steep gradients in the MDT associated with currents of high interest during the noise removal process. It is shown qualitatively that the CDAE method significantly improves upon conventional Gaussian filtering in terms of reducing attenuation. Important oceanographic features are consistently preserved by the CDAE network and much higher velocities are retrieved while levels of noise are kept consistently low across all tested geographical regions. Quantitative analysis, using RMSD and MAD, confirmed these findings, showing that strong velocities are more accurately resolved using the CDAE method than Gaussian filtering and overall that the CDAE's denoised currents more closely matched a reference MDT constructed from various data sources.
A further quantitative evaluation method was designed to assess the level of attenuation of important features in conjunction with the level of noise removal in the case of blind-image denoising, which demonstrated the significant improvement achieved by the CDAE method compared to traditional Gaussian filtering. An important benefit of the deep learning approach is the ability to exploit prior knowledge of the geodetic noise patterns. In turn, this allows the CDAE network to adaptively remove noise within a given region, such that the severe noise which occurs near the coast and around islands (coinciding with steep gradients in the geoid) is significantly reduced without bleeding into nearby areas. A further benefit of the CDAE denoising method is that it can be trained to remove different types of noise depending on the training dataset given.
Documentary sources for standard phases Gibbs free energies of formation (in aq, g, l or s phase) for several chemical species mentioned in this publication have been published previously  [40, 41, 50, 54, [64] [65] [66] [67] [68] [69] [70] [71]  and are listed in Table  A. 1 (Appendix A). The standard phase, water solubility, and possible speciation of all chemical species of interest mined from these sources were identified and checked against articles by Lide et al. [72]  and Haynes et al. [61] , and/or against the online PubChem chemistry database (https://p ubchem. ncbi. nlm. nih. gov/) to fulfill the conditions defined above. Selected ΔG 0 f values for CO 2(aq) and CH 4(aq) were those taken from Hanselmann  [50] , and that for water from Thauer et al. [54].
Funding sources This work was financially supported by the  Sino-French IRP 1211 MicrobSea  to  Karine Alain , and  ISblue project ,  Interdisciplinary graduate school  for the blue planet ( ANR-17-EURE-0015 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir" embedded in France 2030 , to  Karine Alain.
Note that LD labelling could also observed with lower concentration (up to 0. 2µM), showing nonetheless lower signalto-noise ratio. We performed photophysical characterization of compound 5m in different media including triacetin and oil in order to mimic the lipid droplet content (Fig. 8c  and 8d ). The best brightness was measured in canola oil. This is coherent with the observations made showing high signal selectively localized in LDs. Moreover, the measurement of the emission spectrum in the living cells LDs shows an emission centred around 580-590 nm which is close to the values measured in canola oil, (Figure  8e ). Altogether, these data emonstrate that CinNapht 5m constitutes a powerful example of an orangeemitting wash-free probe for lipid droplet imaging in living cells that were easily obtained in only one step.
Acknowledgements This project has received funding by the  French National Research Agency  under the program  CHARMMMAT ANR-11-LABX-0039-grant  and with the support of the "Chemistry graduate school of  Université Paris-Saclay. We also thank the  Institut de Chimie des Subtances Naturelles  for their financial support. The present work has benefited from  Imagerie-Gif  light microscopy core facility supported by  French National Research Agency  ( ANR-11-EQPX-0029 /Morphoscope ,  ANR-10-INBS-04 / FranceBioImaging  ;  ANR-11-IDEX-0003-02 / Saclay Plant Sciences ). The  University Paris-Saclay  and the  CNRS  are acknowledged for funding. GP thanks  CEA, SCBM  and the  European Union 's  Horizon 2020 research and innovation program  under the  FET-OPEN Grant  Agreement No. 862179  for funding K.
Procedure To gain insight into the replication target selection process and pilot the preliminary list of items, we constructed an online survey with 11 questions. The aim of the survey was to (1) pilot the considerations included in the preliminary list and those the author group was undecided about, and (2) to capture considerations not mentioned in the preliminary list. The former was achieved through closed questions rated on Likert scales, and the latter through open questions leaving room for suggestions and additional information. The survey questions are detailed in the electronic supplementary material (materials and additional data analysis). First, we asked researchers to identify the psychological field they work in (closed question). We adapted the sub-field choices from  [37, 38]   To gain insight into how replication targets are selected in practice, we asked our participants to illustrate what motivated them to replicate and how they came to pick the particular replication target they chose (open question Q2).
As such, we consider our analysis to be descriptive. -We used an inductive, data-driven approach for identifying themes. To this end, we read and re-read the data for themes related to considerations for replication target selection. We were aware that our previous involvement with the topic might impact the themes identified and aimed to be reflexive during the coding process (cf. [40] ). Reflections and potential sources of bias were documented. Relevant text from these reflections is discussed in the manuscript, while details can be found on OSF. -We were interested in extracting the most frequently mentioned themes (i. e. considerations). Prevalence was counted across and not within individuals. In other words, we counted how many individuals mentioned a certain theme, and not how often the theme was mentioned overall.
e. concrete cases where a given code might not apply). The codebook is openly available on OSF. Once the codebook was established, authors M. -M. P. and S. M. F. each went through the qualitative text in its entirety, and coded it according to the codebook. Our unit of analysis was a sentence. Once each person coded the dataset, interrater reliability (IRR) was calculated. According to Miles & Huberman  [41] , IRR can be calculated as the total number of agreements (between authors M. -M. P. and S. M. F. ) divided by that same numerator, plus the number of disagreements between authors M. -M. P. and S. M. F. Miles and Huberman suggest that an agreement rate between coders of 80% is sufficient, and we used this same threshold. We had planned to consult a third author (i. e. AvtV), if we had not reached the anticipated IRR.
That is, as Syed and Nelson put it, 'one individual's analysis of qualitative data should generally lend itself to be re-captured by another individual who is reasonably familiar with the research question and procedure' ( p. 376)  [42]. Although replicability is arguably difficult to apply in the context of qualitative research, consistency between coders in this case can certainly be validly applied. IRR performed as a measure of our consistency. Final steps in this process revolved around reviewing, defining and naming themes, as Braun & Clarke suggest. Closed-ended items were evaluated using the median rating and interquartile range (IQR), a measure of dispersion around the median capturing the middle 50% of observations  [43]. Old items with a median rating of 3 and an IQR of 2 or lower were excluded from the list, and new items with a median rating of 7 and an IQR of 2 or lower were included.
Data analysis plan Data analysis was performed after each Delphi round. Quantitative items were analysed using medians and IQR and the distribution of ratings was visualized using histograms. Items with a median rating of 6 or more and IQR of 2 or less were included in the final list of considerations. Items with a median rating lower than 6 and an IQR of 2 or less were excluded. Qualitative responses were summarized by M. -M. P. and discussed by the author group. We counted how many individuals mentioned a certain concern or suggestion. The list items were revised based on frequently mentioned suggestions. When registering this project, we consciously refrained from defining frequently a priori to allow us to flexibly respond to concerns and suggestions later on. We anticipated no incomplete data reports as we forced participants to answer every item.
Reporting of results During stage 2, we produced: quantitative data (i. e. importance ratings), qualitative data (i. e. participants' responses and corresponding codes), documents containing reflections and potential sources of bias from coding authors, and an updated list of considerations. Quantitative data were summarized using median ratings and IQR and presented in tabular form. We report identified codes and associated frequencies. Reflections of the coding authors and the updated list of considerations are available at OSF. Reflections and reasoning behind what qualified as a theme are discussed in the manuscript, leading to intermediate conclusions about how psychological researchers select replication targets. During stage 3, we produced quantitative (i. e. importance ratings), and qualitative data (elaborations from participants), feedback reports for each round, and a selection of items, which participants agreed upon (i.
7 Third, we had planned that authors M. -M. P. and S. M. F. would collaborate first with one another, then with the other authors, to determine which codes to include in a codebook. However, the code-book was established by M. -M. P. and S. M. F. without the input of the co-authors. Codes overlapped substantially and disagreements were easily resolved. Lastly, while we meant to exclude all student projects when identifying potential participants, 16 participants indicated that they conducted their replication as student projects. Their responses were included in the analysis as we were committed to use all available data and the respondents were able to describe their decision-making process.
Participants A total of 682 participants were contacted. Of these, 678 individuals were contacted via email on 4 October 2021 using the Google extention GMass. Four additional individuals were contacted by M. -M. P. via LinkedIn on 11 October 2021. Details about the reminders are described in the electronic supplementary material. Data collection was closed four weeks after it had started (i. e. on 1 November 2021). A total of 185 (27%) responses were recorded. Of these, 64 responses were incomplete, leaving a total of 121 (18%) responses. 8 Demographic information of the 121 responders is presented in table 2.
Quantitative analysis We calculated the median and IQR for all quantitative items. Results are presented in table 3 and visualized in figure  3. None of the items reached our pre-specified decision criterion of a median rating no larger than 3 with an IQR no larger than 2 and none of the new items reached our prespecified decision criterion of a median rating no smaller than 7 with an IQR no larger than 2. Consequently, we did not change the preliminary list of considerations based on the quantitative analysis. A second aim of our survey was to examine potential differences in considerations based on the field of expertise and type of replication. To this end, we split the data into different strata and compared the medians and spread of the data (IQR, min and max) for each stratum. The stratified analysis is detailed in the electronic supplementary material (methods and additional material).
Stage 3 4. 1. Deviations from preregistered plan After five weeks of data collection, data of 32 respondents was downloaded. However, five responses were empty, leaving a total of 27 participants. We had initially planned to continue recruitment for three months or until reaching 30 participants. However, in light of the fact that the summer months were coming up, we decided that it was better for the quality of the data to proceed with the Delphi process rather than wait two more months for the last three participants to potentially join. We requested permission for this deviation from the editorial office and received approval on 7 June 2022. royalsocietypublishing. org/journal/rsos R. Soc. Open Sci. 10: 210586 Table  4. Quantitative results of stage 3 checklist development over the course of the three rounds.
the data in half using 60 randomly generated numbers between 1 and 121. S. M. F. and M. -M. P. independently established codebooks based on 60 and 61 responses, respectively. S. M. F. identified 56 codes, and M. -M. P. identified 67. In two consecutive meetings, S. M. F. and M. -M. P. reviewed and compared their codes and collaboratively established a codebook including 73 codes. Lastly, both S. M. F. and M. -M. P. independently re-coded the complete dataset using the established codebook.
First, we split the data in half using 60 randomly generated numbers between 1 and 121. S. M. F. and M. -M. P. independently established codebooks based on 60 and 61 responses, respectively. S. M. F. identified 56 codes, and M. -M. P. identified 67. In two consecutive meetings, S. M. F. and M. -M. P. reviewed and compared their codes and collaboratively established a codebook including 73 codes. Lastly, both S. M. F. and M. -M. P. independently re-coded the complete dataset using the established codebook.