dataesr/scientific-paragraphs-categorization huggingface dataset:
Setup We use the official database of the United Kingdom general elections results, published by the House of Commons  (Audickas et al. , 2020) , as well as results for presidential elections in the United States manually collected from Wikipedia. 1 Each time we are interested in the percentage of popular votes won by the two major parties -Conservative and Labour in the UK, Republicans and Democrats in the US. We assume these quantities correspond to pointwise observations of independent realisations of the voter model. The result of each election can then be forecast via Theorem 1, provided we have an estimate of the quantity of stubborn nodes ps 0 , s 1 q. Thus, our analysis is done in two steps: first we make for each elections an estimate of ps 0 , s 1 q based on previous results, then Equation 4 gives us the expected value for the coming election that we use as a predictor.
In the UK dataset, our quantity of interest is the percentage of popular votes won by the Conservative and Labour parties in each general elections from 1922 onwards. In the US dataset, it is the number of popular votes gathered by Republicans and Democrats in each presidential elections from 1912 onwards. For the sake of clarity we present our method in the UK case, but note that it directly translates to the US case by replacing Conservative and Labour with Republicans and Democrats. Because our model cannot account for decimal values values we round the percentages to the nearest integer. Different parties are present, the two major ones being Conservative 2 and Labour, the rest including Liberal Democrats or Social Nationalists amongst others. Because our model applies to a two-sided situation only, we cannot consider all of them at once.
Conclusion and future work In this paper we proposed a new method for the forecast of elections results. A lot of published work have used Twitter data for this purpose, usually applying machine learning algorithm to extract sentiment from tweets and estimate a candidate's popularity this way. Such methods have been criticised in the past few years, with problem ranging from bot presence to text mining reliability that cast doubt over their reliability. As such, our model does not rely on Twitter data at all. Instead, we used official results of past elections in the United Kingdom and in the United States to try and predict outcomes of future ones. Our method is based on findings from a previous work of ours, where we conducted a theoretical analysis of the voter model with stubborn on strongly-connected graphs.
Long-term and large-scale multispecies dataset tracking population changes of common European breeding birds Around fifteen thousand fieldworkers annually count breeding birds using standardized protocols in 28 European countries. The observations are collected by using country-specific and standardized protocols, validated, summarized and finally used for the production of continent-wide annual and long-term indices of population size changes of 170 species. Here, we present the database and provide a detailed summary of the methodology used for fieldwork and calculation of the relative population size change estimates. We also provide a brief overview of how the data are used in research, conservation and policy. We believe this unique database, based on decades of bird monitoring alongside the comprehensive summary of its methodology, will facilitate and encourage further use of the Pan-European Common Bird Monitoring Scheme results.
Background & Summary Biodiversity declines have multiple negative implications for entire ecosystems. Trophic interactions between organisms may be altered, functional diversity reduced and potential vulnerability to biodiversity loss increased due to these negative changes 1. Despite the importance of biodiversity for human well-being 2 and awareness of this fact by international authorities (https://www. cbd. int/sp/targets/), a recent study shows an ongoing loss of biodiversity in the last few decades 3. Therefore, evaluation of previous conservation efforts as well as the production of robust datasets for future assessments is essential to better understand and reverse these negative trends, and to understand positive trends at the same time. Datasets that cover long term population changes across species distribution ranges are crucial to capture variability in processes behind recent biodiversity losses and to enable precise targeting of any necessary remedial.
These networks of fieldworkers annually collect data on species' presence and abundance, in various habitats across numerous study plots, used for a wide range of applications  10  (Fig. 1 ). The Pan-European Common Bird Monitoring Scheme (PECBMS)  11  uses data from the national bird monitoring schemes in Europe annually to produce a continental dataset on relative changes in breeding bird population sizes. So far, PECBMS has produced 15 annual updates of the between-year and long-term changes of widespread and common bird species breeding in Europe  10, [12] [13] [14]. These data have been used for the evaluation of conservation efforts  15  , investigation of various research questions  12, [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]  and for reporting on international biodiversity targets  28, 29.
In addition, the PECBMS dataset is regularly used for assessments like the Living Planet Index (https://livingplanetindex. org/), helping to shed light on the global biodiversity state of vertebrates;  30, 31  for policy purposes by the European Environmental Agency, European Commission, Eurostat (EU headline indicators)  32  ; and by Forest Europe and the Organisation for Economic Co-operation and Development to report against national and international environmental and biodiversity frameworks and goals. Here, we present the database of the between-year and long-term relative population size changes in 170 bird species collected by thousands of volunteers in 28 European countries over the past decades  8. The publicly available datasets can be used for numerous goals and especially for investigating research questions, conservation efficiency assessments and for policy purposes.
Firstly, the indices are calculated using national TRIM output data, consisting of imputed species counts, standard errors per year and covariance matrices. Secondly, species counts are weighted by the most recent species population size estimates (updated every three years) in a given country derived from national bird atlases, official data reports and national experts (http://datazone. birdlife. org/) to account for the country-specific population sizes and thus the unequal contribution of national indices on the European index. Thirdly, missing national time totals due to different start years of the schemes 8 are imputed using species counts from a set of countries from the same geographical region  6, 11. For this purpose, we divided all national schemes into seven geographic regions -Central & East Europe, East Mediterranean, North Europe, South Europe, Southeast Europe, West Balkan and West Europe  8.
Finally, European species indices and trends are presented only for a group of common and widespread bird species (hereafter 'common bird species') meeting two criteria: 1. The estimated breeding population (http://datazone. birdlife. org/) is at least 50 000 pairs in PECBMS Europe (EU countries, Norway, Switzerland and the United Kingdom; Fig. 1 ). Additionally, Red-billed Chough (Pyrrhocorax pyrrhocorax) and Spotted Redshank (Tringa erythropus) with population sizes below 50 000 pairs are included, as large parts of their breeding populations are covered in the PECBMS Europe. 2. The estimated breeding population in PECBMS countries providing data for a given species 8 covers at least 50% of the whole PECBMS Europe breeding population (http://datazone. birdlife. org/). The resulting datasets of European population size indices and trends consist of relative population changes for 170 common bird species.
Data Records The Pan-European Common Bird Monitoring Scheme database is organised into five datasets: (1) European species indices, (2) European species trends, (3) European species trends for three short periods, (4) a list of details on the national monitoring schemes and (5) a matrix of countries providing data for population size estimates of individual species 8. Individual fields are described in Tables 2-6 and the whole database is freely available at the Zenodo data repository  8. National-level species indices and uncertainty estimates are also available in the PECBMS database. Due to specific privacy ownership rights, the most recent (2016-2017) Spanish  41  and Cypriot  42  data are under Restricted Access and researchers interested in these most recent updates are required to provide a brief description of the data use.
Strong increase A trend slope of >1. 05 (an increase of more than 5% per year), with the lower confidence limit of the slope >1. 05 Table  1. Classification of the European bird species trends based on the magnitude and uncertainty of the estimates (using 95% confidence intervals). researchers using these datasets are kindly requested to notify the national scheme coordinators of their use. A list of regularly updated contacts to all national scheme coordinators is provided at the PECBMs website (https:// pecbms. info/country/).
Rows species Columns country Table  6. Dimensions of the matrix of species and countries providing data to the PECBMS (species_country. csv). or very high (>1000) annual index values, very low (1 individual) or very high species counts (>1 × 10 6 individuals), the number of zero-count sites, the number of sites with missing counts, and the number of sites with more than 10% of the total national species count. The outlier check indicates possible discrepancies in raw data and transcription errors. Secondly, index and trend estimates, species counts, uncertainty estimates and trend classification for a given year are compared to the information from the previous (or any other) year. After evaluating the data credibility, the whole national dataset including indices, trends and uncertainty estimates for each species is transferred to the PECBMS coordination unit.
The quality check at the PECBMS level consists of similar steps. Finally, species recorded at very few study plots (i. e. some nocturnal species, rare species, species with a small distribution ranges) are filtered out from the final PECBMS datasets. The European species' indices and trends are calculated for the period since 1980; however, the start years differ between schemes (see Methods for details). Therefore, we annually prepare a trend dataset using three additional, shorter periods -1990 onwards, 2000 onwards and using the last ten years of the data. This yields more robust trend estimates for those periods and allows the comparison of trend estimates between different periods by omitting the earliest years of counts in which only a limited number of schemes were in place and a limited number of fieldworkers recorded data.
Usage Notes The resulting population indices and trends reflect changes in relative population size, which is our main goal. Our data do not reflect true population estimates because the actual numbers of birds, as assessed at site-level, may be biased in a number of ways. The bias may arise from imperfect detection of birds by fieldworkers, the use of multiple survey protocols in different countries, or the imputation of missing values. But given the fact that within each country the same methods are used at the same sites over time, the data allow us to estimate temporal trends in population sizes. We advise all PECBMS database users to check notes associated with trend estimates. These notes were added when e. g. a high variation in between-year population sizes was detected, or the trend estimates may have been affected by human activities such as hunting or releasing of captive-bred individuals.
For Bluethroat (Luscinia svecica), the population indices and trends represent population change of the subspecies Luscinia svecica svecica only. Raw bird counts as well as additional details on the sampling sites can be requested from national scheme coordinators. The updated list of their contact information is available at the PECBMS website (https://pecbms. info/country/). Finally, we encourage anyone planning to use the PECBMS datasets in their study to contact the PECBMS coordination team (https://pecbms. info/) with any questions, especially on the possibilities and limitations of these datasets in relation to the aims of the planned study.
Acknowledgements We are grateful to the many thousands of volunteer fieldworkers for their invaluable effort in the field collecting the long-term data used for European common bird indices and species trends. Without their endeavour, the essential continent-wide and detailed information on population changes of common bird species in Europe would not exist. We are also grateful to the numerous supporters of individual national monitoring schemes, including national authorities and decision-makers. The  Royal Society for the Protection of Birds  and  EC grant  " Towards new generation of Wild Birds Indicators " (grant no. 07. 0202/2017/755630/SUB/ENV. D2 ) supported work on the manuscript. The manuscript was completed under the Service Contract No  07. 0202/2019/821208/SER/ENV. D. 2  with the  European Union  and the opinions expressed are those of the contractor only and do not represent the contracting authority's official position.
We present the first cluster catalog extracted from combined space-based (Planck) and ground-based (South Pole Telescope; SPT-SZ) millimeter data. We developed and applied a matched multi-filter (MMF) capable of dealing with the different transfer functions and resolutions of the two datasets. We verified that it produces results consistent with publications from Planck and SPT collaborations when applied on the datasets individually. We also verified that Planck and SPT-SZ cluster fluxes are consistent with each other. When applied blindly to the combined dataset, the MMF generated a catalog of 419 detections (S /N > 5), of which 323 are already part of the SPT-SZ or PSZ2 catalogs; 54 are new SZ detections, which have been identified in other catalogs or surveys; and 42 are new unidentified candidates.
The MMF takes advantage of the complementarity of the two datasets, Planck being particularly useful for detecting clusters at a low redshift (z < 0. 3), while SPT is efficient at finding higher redshift (z > 0. 3) sources. This work represents a proof of concept that blind cluster extraction can be performed on combined, inhomogeneous millimeter datasets acquired from space and ground. This result is of prime importance for planned ground-based cosmic microwave background (CMB) experiments (e. g. , Simons Observatory, CMB-S4) and envisaged CMB space missions (e. g. , PICO, Backlight) that will detect hundreds of thousands of clusters in the low mass regime (M 500 10 14 M ), for which the various sources of intra-cluster emission (gas, dust, synchrotron) will be of the same order of magnitude and hence require broad ground and space frequency coverage with a comparable spatial resolution for adequate separation.
These works showed, for the first time, the significant gain for cluster science when combining space-and ground-based data in the ACT footprint. Aghanim et al. (2019)  also extracted clusters using a match multi-filter (MMF), but they did not publish the associated catalog. Here, we focus on the Planck and SPT-SZ datasets. We propose a practical implementation of a blind MMF extraction algorithm working on space-and ground-based data jointly, and we publish the associated catalog: PSZSPT. Cross-matches of the PSZSPT detections and external catalogs are included. We did not use the Planck & SPT-SZ combined maps proposed by  Chown et al. (2018)  to produce the PSZSPT catalog because they are not optimized for cluster extraction. We used the SPT maps instead, which are provided in that publication, and we thoroughly tested them against quantities published with the official SPT-SZ catalogs; only then did we combine them with the public Planck maps.
The study presented here is expected to be important for the forthcoming space-and ground-based experiments. The future CMB experiments will detect low mass clusters (M 500 10 14 M ) for which the SZ signal is expected to be of the same order as the other sources of emission from clusters, in particular the radio and infrared emission of galaxies (e. g. ,  Melin et al. 2018). Combining ground-based (ν < 300 GHz) and spacebased (ν > 300 GHz) experiments would help disentangle the various sources of emission [e. g. , Simons Observatory  (Ade et al. 2019 ) & Planck or CMB-S4  (Abazajian et al. 2019 ) & PICO  (Hanany et al. 2019)  or Backlight  (Basu et al. 2019; Delabrouille et al. 2019) ]. We first present the two datasets used in our analysis in Sect. 2. We then recall the characteristics of the MMF in Sect.
3. We apply the MMF on the SPT-SZ and Planck data independently to test the consistency of our results with results published by the two collaborations in Sect. 4. The in-depth work related to Sect. 4 is presented in Appendix A, B, and C. In Sect. 5, we test the consistency between the SPT-SZ and the Planck datasets. We detail the construction of the PSZSPT catalog in Sect. 6 and how we characterize it. We provide a comparison between recovered and published masses in Appendix D. We give the names of the SPT and PSZ2 clusters missed in the PSZSPT catalog in Appendix E. The description of the PSZSPT catalog is given in Appendix F. Finally, we summarize and discuss our findings, and look to future work in Sect. 7.
SIMBAD and NED We searched for counterparts in SIMBAD and NED for the remaining 48 detections, which are not matched to any of the clusters in the studied catalogs. We set the search radius to 20 arcmin for the two databases and we looked for galaxy cluster-type objects. We did not find a cluster in the search radius for 34 detections. We set rank=0 (meaning unidentified) for these 34 detections. For the other 48-34=14 detections, we found two obvious bright and large clusters (ACO S 1063 and ACO 3911) close to the two rank=3 detections, confirming that these two detections are multiple (therefore false) detections produced by the algorithm. We also found found SPT clusters that are not included in the cluster catalog provided by  Bleem et al. (2015b) : Three were excluded from the official SPT catalog because they are close to a bright point source (Table  3 of   We set rank=1 for these four detections.
1 < z < 0. 5. A&A proofs: manuscript no. pszspt We expect the unidentified detections to mainly populate this specific range. We note that (1b) is the "mass bias factor," which relates the true mass M 500 to the XMM-Newton-like mass M 500,X = (1b)M 500. The scaling laws from Eq. 7 and Eq. 9 in Planck  Collaboration et al. (2014b)  are based on XMM-Newton masses, and we decided to leave this parameter free in this left panel to aid comparison with other works. From the completeness, we can predict the expected cluster counts from a given mass function. We chose the Tinker et al. (  2008   2019 ) (see their Table  1 ) on an extended dataset. The predicted redshift distribution is given in the right panel of Fig. 12  for the SPT-SZ footprint. As expected from the completeness, the overall Planck+SPT-SZ cluster count (thick red line) is dominated by the SPT-SZ dataset, except at a very low redshift where Planck provides the information.
The increase in the number counts from the joint catalog with respect to the union of the space-and ground-based catalog is moderate, as shown in Fig. 12  (difference between the red thin and thick lines). The majority of new detections are expected to be in the redshift range [0. 1,0. 6] around the location where the Planck and SPT-SZ completenesses cross. The most interesting application of the space-and ground-based joint cluster analyses with current data sets may thus be astrophysical studies and, in particular, cluster profiles, the space-based (ground-based) data providing the large-scale (small-scale) information, respectively. We leave this work for a future article. This first proof of concept of joint cluster extraction with space-and ground-based data opens the path to other possible catalogs when new data will be publicly available, for example Planck and ACT  (Naess et al.
2020)  or Planck and SPT-ECS  (Bleem et al. 2020). But the approach would be most useful in the case of the longer term ground-based Simons Observatory  (Ade et al. 2019)  or CMB-S4  (Abazajian et al. 2019 ) and proposed space mission such as PICO  (Hanany et al. 2019)  or Backlight  (Basu et al. 2019; Delabrouille et al. 2019) , which have resolutions matching between space and ground (1 arcmin FWHM at 300 GHz) and together cover a large frequency range from a few tens of gigahertz to terahertz. The space and ground approach will be crucial to disentangle the various emissions in clusters: the different SZ effects as well as the radio and infrared emission of galaxies hosted by clusters. The SPT-SZ filter transfer function is provided with the public data for each SPT frequency. We tested it by comparing point source fluxes extracted from the public data (using the provided transfer function) and the fluxes published for the same sources by the SPT collaboration.
The left-hand panel shows that our S/N is in global agreement with the published SPT signal-to-noise, although it is systematically lower. The right-hand panel shows the ratio of the two signal-to-noise values. Our S/N is, on average, 0. 9 times ξ for ξ > 5. The SPT public maps  (Chown et al. 2018)  are based on the same 2008-2011 observation cuts as  Mocanu et al. (2019) , except for the 150 GHz field based on the observation cuts from  Story et al. (2013). Thus, they are shallower than the maps used in  Bleem et al. (2015b)  (see Table  1  therein). We suspect that the S/N bias to lower values is due to the difference in map depths. We would expect this bias to disappear if the MMFs were applied to the same SPT data as used in  Bleem et al. (2015b). The residuals are significant. In particular, the stack does not show the negative tails before and after the central maximum in right ascension, which can be seen in black in the middle image.
) mass function, and we fixed (1-b)=0. 63 to adjust the Planck MMF3 cosmological cluster counts to the Planck Collaboration et al. (2018) CMB cosmology. This value of (1-b) is very close to the value (1b) = 0. 62 ± 0. 03 obtained by Planck Collaboration et al. (2018) (see their Eq. 34) using the same dataset, and to the value (1b) = 0. 622 ± 0. 033 found by Salvati et al. (.
Acknowledgements. We would like to thank the anonymous referee for useful comments which helped to clarify this work. We acknowledge the use of the Legacy Archive for Microwave Background Data Analysis (LAMBDA), part of the  High Energy Astrophysics Science Archive Center (HEASARC). HEASARC/LAMBDA is a service of the  Astrophysics Science Division at the NASA Goddard Space Flight Center. We also used the HEALPix software (Górski et al. 2005) available at https://healpix. sourceforge. ioand the WebPlotDigitizer by  Ankit Rohatgi. This research has also made use of the M2C Galaxy Cluster Database, constructed as part of the  ERC  project  M2C  (The Most Massive Clusters across cosmic time,  ERC-Adv  grant No. 340519 ), the SIMBAD database operated at  CDS, Strasbourg, France  (Wenger et al. 2000), and the  NASA/IPAC Extragalactic Database (NED) , which is funded by the  National Aeronautics and Space Administration  and operated by the  California Institute of Technology.