dataesr/scientific-paragraphs-categorization huggingface dataset:
As we are interested in citations, we focus here on articles with at least 1 citation according to Scite database, that is 45,811 articles (the corpus is avalaible for download  (Bordignon 2020) ). The corpus contains 1. 28% of retracted articles (see Table  2  for distribution by sub-corpus). Scite returns 3 values (number of mentioning, contradicting, and supporting citations); we reduce each of them to the total number of citations found by Scite (not the number of citations identified by Scopus or the Web of Science). PubPeer returns a number of comments, we process them as contradicting citations and thus also reduce them to the number of citations identified by Scite. Our objective here is not to test Scite's accuracy and we must assume that its detection of citation polarity, even if it is perfectible, gives a trend on which we can rely in this study.
Comments on PubPeer Assuming that comments on PubPeer are a form of contestation very close to a negative citation, we tested the articles of our corpora in PubPeer to check if questioning the content of a publication is more frequent outside the usual publication process, and more particularly on a dedicated platform that allows anonymous comments. Table  4  shows the percentage of PubPeer comments received in respect to the total number of citations (in Scite database) and also displays the difference between retracted/corrected articles and the others. Retracted.
Our study deals with a twofold scarce material (retracted/corrected articles and contradicting citations), it is thus also necessary to confirm our results with other corpora and to take into consideration the reason for the retraction or erratum. Indeed, plagiarism will not trigger criticism like data manipulation or scientific mistakes would, and errata sometimes refer to errors that do not affect the soundness of science (e. g. , an erratum published for an error in the bibliography). We, therefore, plan to use the classification of reasons developed by RetractionWatch in their database and to carry out automatic analysis of the texts of the retraction and erratum notices. Lastly, our study is dependent on the accuracy of Scite, a recent tool whose reliability was not the subject of our study but which will have to be measured to go further.
Mean acceleration was computed from two consecutive cell velocity values, which were calculated from 2 consecutive cell position measurements within a time step between frames of 30 min. The absolute value of acceleration/de-acceleration was then averaged cell by cell for the whole migration path (63 h). Nuclei location measurements were performed after binarization preceded by 8-bit conversion, background removal and filtering, with an automatic image processing for the counting of the whole nuclei. The counting of nuclei inside micro-pits was done manually, considering a nucleus inside a pit when half or more of the projected surface of the nucleus was inside. Data corresponding to 300 nuclei analyzed for LP_LIPSS and 494 for LP_spikes was normalized according to the surface proportion of micro-pits.
The proportion of focal adhesion points on polished surfaces was measured with consideration taken to both their number and their surface. The number of focal adhesion points was determined manually whereas the measurement by surface was semi-automatic. Indeed, focal adhesion points were binarized after an image processing following the steps: (i) coarse blurring (Gaussian, radius = 40); (ii) successive subtraction of the blurred image to the raw image followed by local contrast enhancement; (iii) successive slight blurring (median, radius = 1) followed by local contrast enhancement. Moreover, the laser-patterned surface was filtered to identify polished and nanostructured areas. Then, a Boolean operation was used to only measure the surface of focal adhesion points on polished areas. Data were normalized according to the proportion of polished areas.
Cell Adhesion In order to determine hMSC interactions with multiscale patterns, cell adhesion was investigated at the sub-cellular scale. For this, hMSCs were seeded on sterilized laser-patterned samples (LP_LIPSS and LP_spikes) and were analyzed by immunofluorescence assays for vinculin detection, a protein involved in cell adhesion. hMSCs were found to place significantly their focal adhesion points on polished areas rather than on laser-nanostructured ones (Figure  6a ). These observations were confirmed by measurements after image processing (Figure  6b ). The number and area of adhesion plaques were quantified, and data were normalized according to surface proportions. Both number and area measurements led to the finding that focal adhesions were mostly and preferentially located on the polished areas.
An ensemble of these subsets forms a FS for the "final" manifold. In order to assess the efficiency of the new algorithm, 80 GTMs built on the FSs of different sizes ranging from 10 to 1. 8M compounds selected from the ChEMBL database. Each GTM was challenged to build classification models for 712 biological activities (depending on the FS size). With the novel parallel GTM procedure, we could thus cover the entire spectrum of possible FS sizes, whereas previous studies were forced to rely on the working hypothesis that FS sizes of few thousands of compounds are sufficient to describe the ChEMBL chemical space. In fact, this study formally proves this to be true: a FS containing only 5000 randomly picked compounds is sufficient to represent the entire ChEMBL collection (1. 8 M molecules), in the sense that a further increase of FS compound numbers has no benefice impact on the predictive propensity of the above-mentioned 712 activity classification models.
Introduction Nowadays, public and private chemical databases contain millions of already synthesized compounds (ChEMBL  [1]  , PubChem  [2]  , CAS  [3]  , etc. ) and billions of computer-generated virtual structures (GDB-17  [4]  ). This chemical universe needs to be explored and analyzed. Earlier, Oprea et al. proposed to use geography concepts to represent chemical structures on a map. [5] Several methods designed to visualize and model chemical space are known in the literature: Scaffold-Tree  [6]  , PCA  [7]  , Multi-fusion similarity maps  [8]  , t-SNE  [9]  , UMAP  [10]  , TMAP  [11]  , etc. Generative Topographic Mapping (GTM), introduced by Bishop et al. [12]  , has a particular advantageit is a non-linear probabilistic approach extending the Self-Organizing Maps  [13]. The GTM algorithm considers a 2-dimensional smooth surface (manifold) injected into the highdimensional descriptors space.
The manifold is fitted to data distribution by maximizing the log-likelihood (LLh) of the molecules in the input space defined by molecular descriptors. Once the manifold is fitted, the molecules are projected onto the 2-dimensional latent space superposed with a square grid of k*k (K) nodes. To determine the position of each compound, a vector of posterior probabilities (responsibilities) to be associated with a given node is used. To describe the entire data set, a vector of cumulative responsibilities can be built using responsibility vectors of individual compounds. The latter can be associated with class or property values which leads to GTM Class Landscape or GTM Property Landscape. 5] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]  A map is built on a set of molecules called a Frame set (FS) that spans the given chemical space.
Usually, the FS is taken as a small portion of compounds in comparison to the database size (10K to 30K molecules). The general expectation is that a larger FS should give a "better" map. However, it is not a priori clear how large the FS needs to be. Note that the FS is not, as one might expect, meant to be a representative "core" of a library, i. e. represent one non-redundant example of every analogue contained by the parent library. By analogy to cartography, one should think of the FS as the "satellites" sufficient to ensure a desired resolution of the GPS location system. Most of the mapped molecules do not need to be by any means close analogues to the FSyet, they have to be "surrounded" by several of FS members, in order to ensure the precise "triangulation" and projection on the map.
Apparently, FS sizes of <1% of the final targeted compound collection mayfortunatelybe sufficient, but no rigorous study of the FS size has been conducted so far. To overcome the FS size limitations all while rendering the manifold independent on the order of FS data blocks, we have developed a new parallel GTM (pGTM) algorithm. It was applied to investigate the optimal size of the FS suitable for producing a meaningful map for a large chemical collection, such as the ChEMBL database. In particular, we investigated whether increasing the FS size far beyond the so-far employed 10 3 -10 4 randomly selected compounds would significantly enhance the map quality using the pGTM approach. Different FSs ranging from 10 to 1. 8M compounds were prepared. Their representativity of the entire ChEMBL database was calculated in the initial descriptor space, using the Kullback-Leibler divergence criterion.
Data and Descriptors As a data source, the public chemical database ChEMBL (v. 25) was used in this study. Chemical structures were preprocessed in 7 steps: dearomatization, removal of the explicit hydrogens, removal of the information on isotopes and stereo, stripping salts, aromatization, selection of a major tautomer, and transformation of common functional groups (e. g. nitro group). The compounds possessing less than 5 or more than 100 heavy atoms were discarded. The obtained collection of about 1. 8M compounds was used to prepare 80 FSs of different sizes (10, 50, 100, 500, 1K, 5K, 10K, 20K, 30K, 50K, 100K, 200K, 400K, 750K, 1M, and 1. 8M compounds) with five randomly selected FSs per each size (in the case of 1. 8M compounds, the FS was just reshuff led 5 times). The molecules were encoded by the 10,898 ISIDA fragment descriptors using the IA-FF-FS-AP-2-3 fragmentation scheme (sequences of 2-3 atoms colored by CVFF  [30]  and formal charges).
The workflow is presented in Figure  3. Within this approach, the parameter matrix W is initialized on the entire FS using the incremental Principal Components Analysis (iPCA). To do so, the covariance matrix is computed incrementally followed by the Eigenvalue decomposition  [38]  (the scikit-cuda library in Python was applied). [39] nce the PCA is done, the FS is split into a series of blocks, and, then, the manifold training procedure is executed to fit each block independently. Each block produces an intermediate GTM manifold fitted on a portion of the data set. Since the same initial position of the manifold in the descriptors space and the same GTM parameters are used to treat the blocks, the intermediate manifolds can be then merged into the final one. For this purpose, simple averaging of the matrices W and the noise variances β can be used: 𝑤 ̅ 𝑚,𝑑 = ∑ 𝑤 𝑚,𝑑,𝑖 𝑁 𝑖=1 𝑁 (9), 𝛽 ̅ = ∑ 𝛽 𝑖 𝑁 𝑖=1 𝑁 (10), where N is the number of data blocks used to train the manifold.
Compound Density Distribution on the Map (in the Latent Space) To measure the uniformity of compound distribution, the normalized Shannon entropy can be used as a metric. For this purpose, the vector of cumulative responsibilities is created using the compounds passed the LLh filtering (the compounds with LLh lower than the threshold were discarded; the LLh threshold is explained in the chapter "2 Data and Descriptors"). The Shannon entropy is computed as E = -∑ CumR k log(CumR k ) k (12), which can be normalized then dividing it by the maximal entropy log(K): E 𝑛𝑜𝑟𝑚 = -∑ CumR k log(CumR k ) k log (𝐾) (13). Here, CumRk is the cumulated responsibility in the node k, and K is the total number of nodes. The Enorm (normalized entropy) ranges within [0;100], where 0 means that all the molecules are mapped into the same node, and 100 means that the molecules cover the chemical space uniformly.
e. gain in physical timenot necessarily a net gain in computational cost. In addition, it is less dependent on the order of compounds in the FS as iGTM. Indeed, the density landscapes built on five reshuffled copies of the ChEMBL database are very similar and their performance characteristics (Entropy and NBA) are also rather close to each other (Figure  6 ). Finally, pGTM can be used to treat FSs containing millions of compounds which is impossible with sGTM and hardly achievable with iGTM algorithms.
8M compounds). This means that we can achieve the best predictive performance with just 5000 compounds which is 0. 003% of the entire database. It is also noteworthy that the maps trained on 500 compounds can already provide good predictive performance. This gives an opportunity to greatly accelerate the construction of GTMs for large data sets: hyperparameter tuning may be run with rather small FSs, while the final manifold at sofar best-found combinations of hyperparameters could be rebuilt on larger FS if needed. In terms of data visualization, the GTM density landscapes obtained with the standard approach (sGTM) support the conclusion made earlier: 5K compounds are already enough to model the entire ChEMBL collection. On the density landscapes (Figures  9a-9f ), the maximal density systematically decreases from 70K structures down to 20K structures and then keeps in this range.
In addition, the data becomes more spread. For instance, about 7-10 clusters with a cumulated density above 10K structures can be found for the map trained on the FS of 5K molecules (Figure  9e ) in contrast to two huge clusters shown on the map which was trained just with 10 molecules (Figure  9a ). Further increasing of the FS size is not needed in this case since it does not bring any new information (Figures  9a -9c ). However, large FSs might be needed in case of huge and/or very diverse chemical collections such as CAS or Zinc where millions and billions of compounds are stored. In this case, sGTM and iGTM cannot be applied, and, therefore, pGTM can be used instead.
7). Considering the fact that 0. 003% (5,000 structures) of the chemical collection is already enough to describe (in the framework of the GTM approach) the ChEMBL database of 1. 8M compounds, we can assume that this might be the case as well for larger chemical databases containing millions of synthesized and billions of computer-generated structures. The study suggests that relevant mapping of the billion-sized libraries should by no means require frame sets above a million of compounds, Frame Sets which can be handled, as shown, by the pGTM algorithm.
The structure was determined by powder X-ray diffraction (XRD) measurement. The XRD measurements were carried out on a Bruker D8 Discover with Cu Kα radiation (λ = 1. 54 Å). Step increment and acquisition time were 0. 05°and 1 s, respectively. The element composition was determined by X-ray photoemission spectroscopy (XPS) and inductively coupled plasma mass spectrometer (ICPMS) measurement. XPS measurements were performed in an integrated VG ESCA Lab system using an X-ray source of Magnesium Kα with typical excitation energy output of 1254 eV. The entire experiments were carried out in an ultrahigh vacuum (UHV) system with typical base pressure in the range of ~10 -10 mbar and the whole acquisition data were taken at room temperature. The impinged spot size on the sample is about 1 mm in diameter.
To perform alpha particle spectroscopy, the crystal was mounted on the window of a Hamamatsu R9880U-20 photomultiplier tube (PMT), with a thin layer of silicone grease to provide optical coupling. This PMT has a fast time response (0. 6 ns rise time), a broad spectral sensitivity (230-920 nm), and an 8-mm diameter photocathode sensitive area. The PMT was operated at a voltage of -750 V for these measurements. The PMT anode signal was input directly to a Caen DT5720D digital pulse processing (DPP) unit which integrated the anode charge pulses over a 92 ns gate. Caen's CoMPASS software was used to control the DPP parameters and accumulate pulse-height (proportional to energy) spectra for each radioactive source. These radioactive sources were positioned (in air) at 2 mm distance from the surface of the crystal.
More detail can be found in Supplementary Information. Pulse-shape discrimination (PSD) between alpha particle and gamma ray. The PSD capability of 1:1 Li-(PEA) 2 PbBr 4 crystal was tested under alpha particle and gamma-ray signals using  137  Cs and 241 Am source, respectively. The crystal was mounted on a Hamamatsu multiplier tube R6233-100 and covered with several Teflon layers, except a small hole was opened to enable alpha particle irradiation. The PMT anode signal was digitized directly by a fast analog-to-digital converter (FADC400 Notice Korea) with recording length of 2. 56 μs for each pulse. A ROOT-based C++ program was used to record data to a Linux-based PC used for further analysis. The optimum filter method was employed for PSD study with detail information can be found in this reference  57.
The Retrospective Analysis of Antarctic Tracking Data (RAATD) is a Scientific Committee for Antarctic Research project led jointly by the Expert Groups on Birds and Marine Mammals and Antarctic Biodiversity Informatics, and endorsed by the Commission for the Conservation of Antarctic Marine Living Resources. RAATD consolidated tracking data for multiple species of Antarctic meso-and top-predators to identify Areas of Ecological Significance. These datasets and accompanying syntheses provide a greater understanding of fundamental ecosystem processes in the Southern Ocean, support modelling of predator distributions under future climate scenarios and create inputs that can be incorporated into decision making processes by management authorities. In this data paper, we present the compiled tracking data from research groups that have worked in the Antarctic since the 1990s.
g. 17, 18. Despite the considerable number of tracking studies on the distribution and habitat use patterns of upper trophic level, air breathing vertebrates in parts of the Southern Ocean based on tracking data, e. g. 19, 20 , no circum-Antarctic synthesis yet exists that crosses species boundaries. This deficiency prompted the Expert Group on Birds and Marine Mammals (EG-BAMM) and the Expert Group on Antarctic Biodiversity Informatics (EGABI) of the Scientific Committee on Antarctic Research (SCAR; www. scar. org) to initiate in 2010 the Retrospective Analysis of Antarctic Tracking Data (RAATD). RAATD aims to advance our understanding of fundamental and applied questions in a data-driven way, matching research priorities already identified by the SCAR Horizon Scan  9, 21  and key questions in animal movement ecology  22.
For these reasons, we worked on the collation, validation and preparation of tracking data collected south of 45 °S. Data from over seventy contributors (Data Contacts and Citations 23 ) were collated. This database includes information from seventeen predator species, 4,060 individuals and over 2. 9 million at-sea locations. To exploit this unique dataset, RAATD is undertaking a multi-species assessment of habitat use for higher predators in the Southern Ocean  24. RAATD will provide a greater understanding of predator distributions under varying climate regimes, and provide outputs that can inform spatial management and planning decisions by management authorities such as the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR; www. ccamlr. org). Our synopsis and analysis of multi-predator tracking data will also highlight regional or seasonal data-gaps.
Modes of attachment on the back varied from using harnesses, glue or marine tape. For whales, transmitters with subcutaneous anchors were attached to the back, using poles, cross bows or air guns. 3] [34] [35] [36] [37]  , following established animal handling guidelines that meet ethical reviews. However, it should be noted that the RAATD dataset contains tracking data that span almost three decades, during which time substantial progress has been made in terms of miniaturization and advances in electronic components. Any adverse effects of devices on animals are therefore likely to be less acute in recent years compared to the earlier years of tracking.
Data assemblage. Step 1. data collection. Starting from 2010, RAATD compiled a catalogue of existing (both published and unpublished) tracking data by contacting international experts that held data. Data were also harvested from existing repositories, including the Australian Antarctic Data Center (https://data. aad. gov. au/), the Integrated Marine Observing System (http://imos. org. au/), PANGAEA (https://www. pangaea. de/), BirdLife International (http://www. seabirdtracking. org/), the Antarctic Biodiversity Portal (http://www. biodiversity. aq/), Ocean Biogeographic Information System (http://www. iobis. org/), and the Global Biodiversity Information Facility (http://www. gbif. org/). The data-collection phase ended in 2016. Step 2. associated metadata. Where available, information on the deployment site and relevant characteristics of the animal at the time of deployment was standardized.
Where age class and sex were known, this information was included in the metadata. Step 3. data standardization. Location dates and times were converted to UTC (Coordinated Universal Time). Records with missing latitude or longitude values were removed, and all longitudes were transformed to lie between 180 °W and 180 °E. Data files were row-ordered by individual, with rows within an individual in their correct temporal sequence. Near-duplicate positions, defined as animal positions that occurred three seconds or less after an existing position fix from the same animal, and which had identical longitude and latitude values (for GPS devices) or longitude and latitude values that differed by less than 1 -05 and which had the same location quality value (for PTT devices), were removed. Entries in the age class, breeding stage, device type, location quality, scientific, common, and abbreviated name, sex, and deployment site columns were validated against controlled vocabularies.
Mandatory entries (e. g. , deployment date, device type, individual animal identifier) were checked for missing values. When the data contributors could not provide missing deployment dates, the first data point of the track was used as a reference point for deployment. Where animal identifiers were missing, they were created from the the tag identifier or file name. Deployment locations were recorded by the original field team either at the individual-animal level (using e. g. , a hand-held GPS device) or at the deployment-site level (i. e. , one deployment location per group of animals). The latter was common for deployments at colonies, whereas the former was most common for non-colony deployments (e. g. , seals and whales). Where deployment locations were not recorded by the field team, the first location estimate(s) in the tracking data were used.
Deployment site names were standardized to colony names wherever possible (e. g. , to the beach-on-island level). Periods at the start or end of deployments were identified and discarded if there was evidence that location data during these periods did not represent the animals' at-sea movement. For example, tags may have been turned on early (thereby recording locations prior to their deployment on animals) or animals may have remained at the deployment site, e. g. the breeding colony, for an extended period at the start or end of the tag deployment. Some tracks also showed a marked deterioration in the frequency and quality (for PTTs) of location estimates near the end of a track. Such locations were visually identified based on maps of each track in conjunction with plots of location distance from deployment site against time.