dataesr/scientific-paragraphs-categorization huggingface dataset:
We found that the returned canvas hashed values were different upon every visit. IP address. We run the Initial crawl with an IP address pointing to France. We spoofed the IP address using the VPN add-on Browsec VPN  [71]. We used a static IP address pointing to the Netherlands. Consequently, the spoofed IP address remains constant during the runs of spoofed crawls. We checked that the IP address changed using the whoer website. Time zone. We launched the Initial crawl with Paris UTC/GMT +1 timezone. We spoofed the timezone to America/Adak (UTC-10) using the Chameleon add-on  [16]. Accept-language. The Accept-language header specifies which languages the user prefer. We used English as Accept-language in Initial crawl. We spoofed the Accept-language header using the Chameleon addon  [16]  to Arabic.
Otherwise, if the cookie is set by a third party domain, we perform a google search for the concatenation of the cookie key, the third party domain name, and the string "cookie policy". We then search for a cookie policy in the 5 first google search results pages. We found 24 (17%) cookie purpose descriptions in cookie policies, out of which 12 (8. 45%) cookie purposes infer the collection of user related information. The remaining cookie purposes do not provide any description on the usage of the cookie. We were not able to analyze a remaining 15 website policies because they were writ-ten in a language we do not speak. Overall, none of the cookie purposes mentioned the respawning behavior. To gain insight on the respawned cookies, we analyzed the content of the 142 cookies manually. First, we searched for the 8 studied feature values, we found that 18 cookies included browser and machine features in their value in clear text.
15%) cookies. The second most popular feature to respawn cookies is User-Agent (UA) -it is observed with 486 (34. 10%) cookies. Note that features that can be easily collected passively, like IP  address and UA, are more frequently used than features that can only be accessed actively, such as Canvas. Given that a cookie can be respawned with several features, we consider that a cookie C is respawned with a set of features F if the value of C depends on every feature in F (such detection was done independently for each feature as described in Section 4. 3. 2). We found that cookies are usually respawned with a set of different fingerprinting features. In our dataset, cookies are respawned with 184 distinct sets of feature combinations. Figure  3  shows the sets of features most often used for cookie respawning.
We see that the IP address alone is the most commonly used feature to respawn cookies, and moreover no other set of features is more popular than the IP address alone. The IP address is used alone to respawn 366 (25. 68%) cookies. Mishra et al. [47]  studied the stability and uniqueness of the IP address over a duration of 111 days on a dataset of 2,230 users. They showed that 87% of participants retain at least one IP address for more than a month. Hence, IP addresses are both stable and unique, therefore, they can be used to uniquely identify and track user's activity. Interestingly, the top-2 sets of features, {IP}, and {UA}, contain only passive features that are easier to collect. Active features are rarely used: timezone, the most popular active feature for respawning, is used alone for 46 (3.
Discovering respawned cookies owners Due to the the Same Origin Policy (SOP)  [57] , the domain that is responsible for setting a cookie can be different from the domain that receives it (see Section 2. 1). Therefore, we differentiate two stakeholders: ownerthe domain that is initially responsible for setting the cookie, and host -the domain that has access to the cookie and to whom the cookie is sent by the browser. In the following, we define both owner and host as 2 ndlevel TLD domains (such as tracker. com). It is important to detect the cookie owner -for instance, in order to block its domain via filter lists  [19] [20] [21]  and prevent cookie-based tracking. Indeed, the notion of cookie owner is often overlooked when the reasoning is only based on the cookie host  [12]. When one cookie owner sets a cookie in the context of several websites (the owner's script can be embedded directly on a visited website or in a third-party iframe), the host of this owner's cookie is the context where the cookie is set because of the SOP  [57].
To identify cookie owners, we distinguish two cases, as described below. Cookie set by HTTP(S) header. If the cookie is set by the HTTP(S) Set-Cookie response header, then the owner of the cookie is the same as its host because it corresponds to the 2 nd -level TLD of the server that sets the cookie. Cookie set by a script. Document. cookie property is the standard way for a JavaScript script to set a cookie  [35]  programmatically. To check whether a cookie is set via JavaScript and to extract its owner (the domain who serves the script) when crawling a website, we (1) extract the set of scripts S that set a cookie on the website using document. cookie, (2) for every script in S, we extract the set of cookies C set by this script, and (3) extract the set of respawned cookies identified in Section 5.
1. 1 that are never set by HTTP(S). Then, we check whether there is an overlap between the set of (key,value) of these respawned cookies and the set C. If it is the case, we conclude that the cookies in the overlap are set via JavaScript, and their owner is the 2 nd -level TLD domain that served the script. For each of the 1, 425 respawned cookies, we identified its owner depending on how the cookie was set. Figure  4  shows domains appearing as host only (left blue part), as owner only (yellow part), or both (middle overlap). In total, 1, 425 respawned cookies are labeled with 765 distinct hosts, however they were set by 574 distinct owners. Figure  4  also depicts that 75 domains appear as owners and never as cookies hosts. These domains serve JavaScript scripts that set cookies, but never serve cookies directly via an HTTP(S) response header.
Processing such categories of data is forbidden, unless allowed by the user's explicit consent [66, Article 9(2)]. We studied tracking via the third-party respawned cookies on websites processing sensitive data. As a result, we detected 21 cookies respawned in Adult websites that are set by 19 different owners. The top domain respawning cookies on sensitive websites is adtng. com (no corresponding official website was found for adtng. com). It respawned cookies on 3 different adult websites, and therefore, can track and link user's activity within adult websites in a persistent way, without explicit consent to legitimize such operation, rendering such respawning practise unlawful. Geolocation of websites including respawning and respawned cookies owners. Independently of the country of registration of a website, if a website monitors the behavior of users while they are in the EU, the GDPR applies to such monitoring [66, Article 3(2)(b)].
Respawned cookies purposes In this section, we evaluate the purpose of the respawned cookies. Given that only a small percentage of cookies includes a description of their purposes  [30] , we use the Cookiepedia open database [17] that has been used in prior work  [68]. It is the largest database of precategorized cookies with over 11 million cookies used across 300,000 websites. It uses the classification system developed by "The UK International Chamber of Commerce" (ICC) and relies on four common purposes of cookies: i) Strictly Necessary (which includes authentication and user-security); ii) Performance (also known as analytics, statistics, or measurement cookies); iii) Functionality (includes customization and multimedia content); and iv) Targeting (known as advertising). We found that out of the 1, 425 respawned cookies, 134 (9%) were not found in the cookipedia database, 955 (67%) were categorized as Unknown, and 336 (24%) were successfully categorized.
Cookie respawning with browser fingerprinting beyond deprecation of third-party cookies Web browsers are moving towards the deprecation of third party cookies which are the core of cross-site tracking  [58]. Can this deprecation prevent cross-site tracking? In the following, we show how cookie respawning with browser fingerprinting can overcome browsers preventions. Via persistent tracking with respawned cookies, domains can track users across websites without third-party cookies. Consider the following scenario: example. com and news. com include a fingerprinting script from tracker. com. When the user visits these websites, the script from tracker. com accesses the user's browser and machine features, and sets a corresponding first-party cookie. As a result, two first-party cookies are set in the user's browser and labeled with two different hosts: example.
Acknowledgements The authors acknowledge the ISO 9001 certified  IRD i-Trop HPC (South Green Platform) at IRD Montpellier  for providing HPC resources that have contributed to the research results reported within this paper. We thank all the ETE modelling team for thoughtful discussions. We thank the  French public health agency (Santé Publique France)  and the  French national health insurance  ( Caisse Nationale d'Assurance Maladie ) for providing Covid-19 contact matrices, and especially  Alexandra Mailles  and  Jonathan Bastard. BR is funded by the  Ministère de l'Enseignement Supérieur et de la Recherche.
Observational data were therefore able to narrow the estimates for the relative GPP increase under a doubling of atmospheric CO 2 concentrations, to 37 ± 9% for high-latitude ecosystems, and 32 ± 9% for extratropical ecosystems. However, the absolute values of the feedback parameters are known to be sensitive to the chosen model ensemble, the simulation scenario, and the mathematical framework  (Boer & Arora, 2009; Gregory et al. , 2009; Hajima et al. , 2014; Zickfeld et al. , 2011) , hence also affecting the emergent relationship. Therefore, it is crucial to investigate previously derived emergent relationships out-of-sample on new model ensembles, such as CMIP6. In this study, we examine whether the previously published emergent constraints on these two carbon cycle feedbacks, originally reported for the CMIP5 ensemble, still hold for the CMIP6 ensemble.
The latest generation of models are developed based on previous versions, which have (at least partly) participated in CMIP5. However, the verification of both previously derived emergent relationships from the CMIP5 models with the new generation of ESMs provides still useful evidence to confirm the existence of the emergent constraints. In order to directly compare to W14 and W16, we use the same methods, but an extended historical time period up to 2014 for the larger ensemble of models in order to quantify both carbon cycle feedback parameters. In this study, we use model output from seven CMIP5 models and nine CMIP6 models (see Table  1 ). Not all models are participating in both analysis sections, due to data availability, which is explicitly indicated in Table  1. Each model performed three different simulation experiments which are listed in Table  2.
The GCP data set covers the period from 1959 to the present, and is updated each year. The most recent changes are listed in the annually updated publications of the GCP project (e. g. ,  Friedlingstein et al. , 2020). The IAV in the tropical near-surface temperature (30°S-30°N) is calculated using annual mean temperatures from the NOAA-National Climate Data Center (NCDC, http://www. esrl. noaa. gov/psd/data/gridded/data. noaamergedtemp. html). This data set covers the period from 1880 to present at a monthly resolution. The observed range in the sensitivity of the CO 2 seasonal cycle amplitude to rising atmospheric CO 2 levels is diagnosed from in situ measurements at Pt. Barrow in Alaska. This data is provided by the National Oceanic and Atmospheric Administration (NOAA)/Earth System Research Laboratory (ESRL, http://www.
Data Availability Statement The corresponding recipe that can be used to reproduce the figures of this paper will be included in the ESMVal-Tool  (Eyring et al. , 2020; Lauer et al. , 2020; Righi et al. , 2020; Weigel et al. , 2021)  as soon as the paper is published. The new extensions described in this paper are available since ESMValTool v2. 6. 0. ESMValTool v2 is released under the Apache License, VERSION 2. 0. The latest release of ESMValTool v2 is publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 3401363 (Andela et al. , 2022a). The source code of the ESMVal-Core package, which is installed as a dependency of ESMValTool v2, is also publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 3387139 (Andela et al. , 2022b). ESMValTool and ESMValCore are developed on the GitHub repositories available at https://github.
g. holes in shallow rocky reefs) and the cryptic or elusive behaviour of some fishes  (Boussarie et al. , 2018; Brandl et al. , 2018). Environmental DNA (eDNA) metabarcoding has proven to offer more accurate and wider biodiversity assessments than classical census methods, particularly for reef fishes  (Aglieri et al. , 2021; Polanco Fernández et al. , 2021; Sigsgaard et al. , 2020) , and to provide a very local signal  (Gold et al. , 2021; Jensen et al. , 2022; Miya, 2022). This non-invasive method is based on retrieving DNA naturally released by organisms in their environment, then amplifying and sequencing a specific marker to identify corresponding species  (Miya, 2022). eDNA metabarcoding then provides a list of molecular operational taxonomic units, whose sequences can then be assigned to species or taxa thanks to a genetic reference database (e.
(2021) , and a well-completed genetic reference database (75% coverage of the regional fish species pool), to test 11 biodiversity indicators based on fish traits, phylogeny and vulnerability, while controlling for habitat and environmental heterogeneity. 4. Synthesis and applications: Our findings highlight the importance of looking beyond the mere number of species to fully depict and understand the effect of marine reserves on biodiversity and evaluate the effectiveness of conservation measures. Rather, we propose a dashboard of three eDNA-based indicators that can provide an early signal of ecosystem deterioration or recovery. eDNA metabarcoding offers a powerful tool to supply site-specific and standardized taxonomic-, phylogenetic-and trait-based biodiversity assessments, in complement to other classical techniques, such as visual censuses or video surveys, able to estimate species abundance but also individual life-stage and size.
We ran 12 replicate PCRs per sample, and prepared nine libraries using the MetaFast protocol. We used the MiSeq Flow Cell Kit Version3 to perform paired-end sequencing (2 × 125 bp). Paired-end sequencing outputs were handled using the Obitools toolkit  (Boyer et al. , 2016). First, reads were merged using illuminapairedend, trimmed and demultiplexed using ngsfilter, dereplicated using obiuniq, and reads identified as errors were discarded using obiclean with default settings. We discarded observations with less than 10 reads and accounted for tag-jumps and index-bleeding  (Marques et al. , 2021). Taxonomic assignments were performed using ecotag on a combination of publicly available sequences from ENA (downloaded in June 2021) and of our Mediterranean database, comprising 386 sequences from 156 species.
Indicator We then used generalized linear models (GLMs) to test the effect of protection on each of the 11 indicators while accounting for habitat and environmental heterogeneity between sites. Distribution of the indicators was checked using histograms (Figure  S1 ). Accordingly, we modelled the proportion of Red-Listed species and the proportion of chondrichthyans using a quasi-Poisson distribution, best suited for zero-inflated distributions, whereas all other indicators were modelled using a Gaussian distribution. We used a total of nine variables to account for habitat and environmental variability between samples (see Appendix S1 and Table  S1 ). We performed a principal component analysis (Figures  S2-S4 ) on the eight habitat and environmental variables to reduce the dimensionality of the dataset and build models with independent variables.
We used the first four principal components which explained 80% of the variance between samples (Figure  S2 ) as explanatory variables in our GLMs. To account for spatial autocorrelation due to potentially overlooked factors, we estimated a spatial autocovariate on each initial GLM residuals, using the function autocov_dist of package spdep  (Bivand & Wong, 2018) , and included it as an additional predictor in the final models. We calculated the R 2 for each model to determine their accuracy, and we estimated 'partial effects' of the protection variable (i. e. its contribution conditional to habitat and environmental variables) using the margin function of the R package margins version 0. 3. 26 (Leeper, 2021). The conditional predicted values of the indicators inside vs. outside reserves and the test of marginal effects are given in Figure  2.
On the other hand, total species richness significantly decreased from an average of 30 (±11) species per sample in fished areas to 23 (±11) species inside marine reserves (Figure  2 ). We did not detect any significant effect of protection on the other seven biodiversity indicators (Figures  S3-S6 ). Across all final models, we did not detect any significant spatial autocorrelation in the residuals with Moran's I ranging from 0. 00 to 0. 27 and p-value ranging from 0. 05 to 0. 88 (Figure  S7 ). The results obtained with the balanced dataset (i. e. with the same number of samples inside and outside reserves) were similar to the results found using the full dataset. We did not find any significant effect of reserve size or age on any of the indicator (Figures  S8  and S9 ), except on the Crypto indicator for which reserve age had a slightly positive effect (average marginal effect ME = 0.
DATA AVA I L A B I L I T Y S TAT E M E N T The bioinformatic pipeline is freely available in Gitlab at the following link: https://gitlab. mbb. univ-montp2. fr/edna/snake make_rapid run_obitools. All data and code are available in a GitHub repository at the following link: https://github. com/Alici aDalo ngevi lle/eDNA_ indic ators_med. git.
All the code as well as the pretrained model is available on GitHub and a plugin for the image analysis tool Napari, is also distributed https://github. com/cbib/DeepSpot. DeepSpot gives a new twist to the ResNet network architecture and learns to automatically enhance the mRNA spots, bringing them all to the same intensity. In parallel, a multi-network architecture is integrated, trained by minimizing the binary cross-entropy (BCE) while providing context for mRNA spots thanks to the atrous convolutions. We evaluated the impact of the spot enhancement on the downstream mRNA spot detection, by performing spot detection using ICY with fixed parameters on both simulated images and experimental images manually annotated. Moreover, we compared the quality of mRNA spot detection from images enhanced by DeepSpot with deepBlink, and have shown that our method achieves greater generalization to effectively handle full variability of smFISH data.
In particular, ImageJ/Fiji  (11, 12)  is widely used, largely due to the plugin-based architecture, recordable macro language and programmable Java API. Another popular tool is CellProfiler  (13)  , based on similar paradigms and FISH-Quant  (14). A more recent platform, ICY  (15)  , also provides the possibility to develop new algorithms as well as a user interface for image analysis, including the ICY spot detector for the detection of mRNA spots, method based on wavelet transform decomposition  (10). Deep-learning networks have been introduced for mRNA spot detection  (16, 17)  , and more recently deepBlink  (7). The latter focuses on a fully convolutional neural network based on the U-Net architecture. deepBlink not only provides the code, but also annotated smFISH data and implements a threshold-independent localization of spots.
In the microscopy field, Deep-STORM  (27)  propose to enhance the resolution of Single Molecule Localization Microscopy (SMLM) mRNA spots, with deep learning. However, these images don't have the same characteristics as smFISH data in terms of noise or signal. For example, human nodules are much larger objects than typical mRNA spots. As for the star enhancement method proposed by  (25)  , it is not suited for low intensity spots  (28)  , which is a major concern in spot detection for smFISH images.
smFISH datasets with alternatively established spots' localization We constructed a dataset DS exp of 1553 images from the experimental smFISH data acquired in  (29)  , by applying 256 × 256 pixels patches to better fit in the GPU memory (see Table  1 ). The authors have performed spot detection using image analysis techniques such as local maximum detection, implemented in the BIG-FISH pipeline  (30). Along with the scripts, the authors provided a list of 57 parameter combinations that were used to detect the spots in 57 different image acquisition series of 32 genes used in their study. We ran the pipeline with these parameters and performed an additional manual curation to keep patches with number of spots between 10 and 150 and remove those with a visually obvious overor under-identification of spots.
The resulting DS exp dataset of 1553 images representing 27 different genes is thus experimentally generated and guarantees high confidence in the ground truth annotation of mRNA spot coordinates. We have also downloaded the dataset DS dB from the deepBlink publication  (7)  , composed of 129 smFISH images acquired from 4 different cell culture conditions of the HeLa 11ht cell line. The authors provided their annotation of mRNA spots' locations which was performed using TrackMate and curated by experts. To estimate the variability of spot intensity, we also calculated the Coefficient of Variation (CV) of spot intensities for each gene of DS exp as well as for the entire DS exp and DS dB datasets (Supplementary Figure  S1 ). While for most images and genes, the CV ranges from 0. 25 to 0. 50, some have a very high CV.
Novel smFISH dataset from a wound healing assay To evaluate whether DeepSpot enables precise mRNA spot detection in a biological context, we made use of the wound healing assay  (31)  to generate a second experimental dataset DS wound. In the wound healing assay, migrating cells, such as fibroblasts, are grown on a coverslip and serum starved for synchronization. A scratch in the middle of the coverslip is then generated, mimicking a wound, followed by induction of the cells to polarize and migrate towards the wound, to generate wound closure, done using replacement of serum starved medium with 10 % FBS-containing medium. We used 3T3 mouse fibroblasts in a wound healing assay, followed by cell fixation. Fixed samples were taken for single molecule FISH experiments to visualize and quantify -Actin mRNA and imaged on a custom built Nikon Ti Eclipse widefield TIRF microscope.
-Actin has been previously shown to be enriched in neuronal growth cones of extending axons, as well as the leading edges of migrating cells and this enrichment has typically been associated with cell polarity and neuronal plasticity  (32) (33) (34). Based on this, we hypothesized that -Actin would be enriched in the leading edge of migrating 3T3 fibroblasts. The dataset is composed of 96 images, 48 images of non migrating cells (control) and 48 images of migrating cells. Each image was divided into 4 patches, yielding a total of 384 patches of 256 × 256 pixels size.