dataesr/scientific-paragraphs-categorization huggingface dataset:
Contract Data We collected transaction level data on federal contracts 8 from usaspending. gov, the US government's online repository of federal spending, containing virtually all federal contracts in the United States from 2004 to 2015, inclusive. The source reports individual "actions" on contracts, such as payments or modifications. We aggregated these actions to the contract level, totaling more than 2. 1m contracts. The federal contracting database includes information on all contracts above a mandatory reporting threshold ($25,000 for most of our period) awarded by federal agencies regulated by the Federal Acquisition Regulation (FAR). 9 We followed the protocol outlined in other works on public procurement for data cleaning and coding  (Charron et al. 2017; Dahlström, Fazekas, and Lewis 2021).
Matching Vendors to Political Contributions We also collected campaign contributions data. The Database on Ideology, Money in Politics, and Elections (DIME) includes campaign contributions from individuals from 1979 to 2014 to candidates for federal office in the United States and to political party organizations (i. e. , Democratic and Republican national committees), grouped by congressional term  (Bonica 2016). Data on contributions from individuals includes two fields for employers. We processed these names and linked them to contract supplier names associated to contracts. Our matching procedure looks up each company name appearing on campaign contributions in a list of all aliases observed in the contracting data, improving robustness to alternative representations of companies in contributions.
For each supplier, we record sums of their contributions to Republican and Democratic campaigns in each congressional term from January 2003 to December 2014. At the contract level we note the supplier's total contributions to both parties in the current and previous terms. While political donations recorded in the data come from individuals (and company donations are largely opaque in the United States), it is companies who benefit from government contracts. Hence, we argue that individual donations are a suitable proxy for company political alignment and represent a major channel through which companies seek political favors in the United States. We sum all individual donations to a party on the company level and show that large donations are what matter. It is likely that individual donations are aligned with the company's political preferences and unobserved political party financing.
Favoritism Risk Index Our data do not directly record instances of favoritism in the procurement process. To circumvent this problem, we construct the favoritism risk index (FRI), an index that captures the risk of favoritism in a contract award. To do so, we select 6 There are some agency-specific regulations such as for the Department of Defence. We control for these by using agency-office fixed effects. 7 Again, the existing agency-specific variation in rules is removed by our agency-office fixed effects. 8 This includes so-called indefinite delivery vehicles that are, in essence, multiyear rolling contracts. 9 There are a number of legally mandated exceptions and exchanges with domain experts that suggest that administrative error may bias the database to a small degree. Nevertheless, we assess that our claim to complete representation of federal purchasing is adequate.
17 This formulation of the FRI aligns with our hypothesized impact mechanism of political appointees in federal agencies manipulating tenders and award. Such actors have less control over contract implementation. We check the validity of our construct in several ways: by showing consistency with results in relevant literature on the United States and the OECD; by showing micro-level validity  The following solicitation types were coded as competition-restricting (FPDS-NP database codes in parentheses): Alternative Sources (AS), Simplified Acquisition (SP1), and Only One Source (SSS). Due to the binary nature of our risk indicators, our estimation uses exploratory factor analysis (minimum residual solution) with tetrachoric correlations. Weights are very small and negative for modifications, suggesting that post award manipulation is complementary to tender and award manipulation.
e. , hypotheses 1-3 hold), but that those same firms are awarded less contract value overall. Then it is unclear whether politically connected firms actually benefit from political favors. Clear-cut results should either indicate that (1) hypotheses 1-3 are verified and politically connected firms are awarded more revenue than non-connected firms, or (2) that those hypotheses are not verified and politically connected firms are awarded no more revenue. Lastly, we test our purported mechanism underlying hypotheses 1-3: that political appointees favor politically connected firms. The remainder of this section describes our approach in detail. To assess whether politically connected firms receive higher procurement revenue, we aggregate our data at the firm and congressional term level and consider the total value of contracts awarded to the firm over the period.
Conclusion We hope to have contributed to the long-standing debate on the impact of money in US politics with novel evidence on how campaign contributions can induce favoritism in federal contracting. Based on a principal-agent model, we hypothesized that company donations' impact on favoritism is strongest when political principals have a strong grip over their bureaucratic agents, in particular through appointees. To test our hypotheses, we combined data on federal contracts with registered campaign contributions for 2004-15. Addressing the perennial challenge in the field, that is measuring favoritism, we developed a proxy indicator specific to federal contracting, using factor analysis which captures a host of strategies employed by public buyers to favor firms. In the absence of random assignment or a natural experiment, we developed an elaborate regression model with an extensive range of fixed effects accounting for many unobserved confounders: buyer (buying office within the agency), state of contract performance, main industry of purchased products, and congressional term fixed-effects.
Romain Ferrali  gratefully acknowledges financial support from the  French National Research Agency  Grant  ANR-17-EURE-0020  and the  Excellence Initiative of Aix-Marseille University -A*MIDEX. 1 https://www. reuters. com/article/us%2Dusa%2Dpuertorico%2Dpower/puerto%2Drico%2Dwhitefish%2Ddefend%2Dcontroversial%2Dpower%2 Dcontract%2DidUSKBN1CU020. 2 https://en. wikipedia. org/wiki/Whitefish_Energy. Downloaded from https://academic. oup. donations, and that this is mediated by agency insulation from politics. To fill this gap, we ask the following question: How Do Political Party Contributions Bias the  Award of US Federal Government  Contracts Favoring Donating Firms?https://www. usaspending. gov/. Downloaded from https://academic. oup. com/jpart/advance-article/doi/10. 1093/jopart/muac026/6586849 by guest on 27 January 2023.
Questionnaires to participants and personal data In order not to overly solicit citizens, only one anonymous questionnaire may be submitted to participants at the beginning and one at the end of each session of the Citizens' Convention. Each questionnaire should not exceed 15 minutes in order to fit easily into the planned facilitation process. The wish to submit these questionnaires will be communicated to the participants at the opening of each session. The facilitators will encourage them to answer them on a voluntary basis. Before each session of the Citizens' Convention, the questionnaires to be submitted at the beginning and end of the session will be sent to the Governance Committee for information. The database containing the questionnaires at the end of the Citizens' Convention will be made available to all research teams wishing to analyse them.
Personal data may be requested from participants during the last session of the Citizens' Convention, for the purpose of interviews to be conducted with consenting persons after the end of the Convention. The collection of this data can only be done with the person's written and informed consent. Observers will forward the planned informed consent form to the Governance Committee prior to its release. The database collecting these personal data will be kept by the CESE and may be made available to teams who justify the need for it for research purposes.
Beryllium data were calibrated directly against the STD11 standard [51] with a 10 Be/ 9 Be ratio of (1. 191 ± 0. 013) x 10 -11. Aluminum measurements were performed against an in-house standard called SM-Al-11. with 26 Al/ 27 Al = (7. 401 ± 0. 064) x 10 -12 which has been cross-calibrated against the primary standards certified by a roundrobin exercise  [50]. Analytical uncertainties (reported as 1σ) include uncertainties associated with AMS counting statistics, AMS external error (0. 5% for 10 Be), chemical blank measurement, and, regarding 26 Al, 27 Al measurements. Measurements of chemically processed blank yield ratios on the order of (2. 0 ± 0. 75) x 10 -15 for 10 Be and (2. 0 ± 2. 0) x 10 -15 for 26 Al. A sea level high latitude spallation production rate of 4. 02 ± 0. 32 at. g -1 a -1  [52]  was used and scaled using  [53]  polynomials.
Acknowledgments Darryl Granger  and two anonymous reviewers are thanked for their deep reviews; their comments significantly improve the final version of this manuscript. The ASTER AMS national facility (CEREGE, Aix-en-Provence) is supported by the  INSU/CNRS , the  ANR  through the  "Projets thématiques d'excellence" program for the "Equipements d'excellence" ASTER-CEREGE action  and  IRD. The  Agence Nationale des Parcs Nationaux (ANPN)  is warmly thanked for offering us the opportunity to use their logistics in the  Lopé National Park. ANPN ecoguards are also thanked for their help in the field in opening the path to the sampling site. We would like to thank the  Centre National pour la Recherche Scientifique et Technologique (CENAREST)  for authorizing research in the Gabonese national territory and  Maher ZAWIL  ( Bitar society ) for his logistical help at  Otoumbi.
Missing data Attrition (non-responders) and partial responses are two sources of missing data in a COS development exercise. To take account of attrition bias, distribution of the scores for those who participated only in round 1 will be compared with the equivalent round 1 data for those who went on to complete round 2. Changes in participant scores between rounds will be summarised along with the reasons given. Outcomes with missing data will be considered at the final to ensure inadvertent exclusion is avoided.
Data The survey is organized in two parts. The first part is the experimental module; designed to collect all the information required to estimate the inequality aversion parameter. The randomization is automatically performed by the online survey platform with a uniform probability of being selected for each of the treatment arms (p = 0. 25). In the second part, we collect additional information to help with the interpretation and discussion of our results. We collect data about socioeconomic and demographic characteristics, and we include a set of questions regarding participants' opinions, attitudes and preferences. We also collect information about individual characteristics such as age, gender, and working status (not working, working part-time or working full-time); and household level information such as the number of individuals living in the household, their mother's and father's level of education and household income.
views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the  IZA Guiding Principles of Research Integrity. The  IZA Institute  based policy advice on labor market issues. Supported by the  Deutsche Post Foundation , IZA runs the world's largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. This research was funded by the  Agencia Nacional de Investigación e Inovación (ANII)  -grant  FCE 1 2014 104284. The experiment is registered in the Registry for Randomized Controlled Trials operated by the American Economic Associ-ation:  RCT ID AEARCTR-0002471.
When the glycolytic flux was inhibited, ptsG expression, which controls glucose uptake  (Rohwer et al, 2000) , was 70 % higher at 10 and 50 mM acetate. The expression of most glycolytic genes was 15 to 110 % higher at 50 mM acetate, while the expression of TCA cycle genes remained stable. Regarding acetate metabolism, the expression of pta and ackA was slightly higher at 10 and 50 mM acetate, while alternative acetate utilization via Acs was inhibited by acetate (5-fold reduction in expression at 100 mM). Finally, expression of genes encoding ribosomal proteins (rpl, rpm and rps operons) was increased by 57 % on average at 50 mM acetate and 48 % on average at 100 mM acetate (Dataset S1). These results indicate that the stress response and lower metabolic activity triggered by acetate at high glycolytic flux do not occur at low glycolytic flux.
, 2021). Three independent replicates were analyzed for each condition. The transcriptomics data can be downloaded from the ArrayExpress database (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-11717. Transcriptomics data in the absence of glycolytic inhibition (ArrayExpress database accession number E-MTAB-9086  (Millard et al. , 2021) ) were measured under the exact same conditions but without αMG. Metabolomics experiments. Extracellular concentrations of glucose and acetate were quantified in 180 µL of filtered broth (0. 2 μm syringe filter, Sartorius, Germany) by 1D 1 H-NMR on a Bruker Avance 500 MHz spectrometer equipped with a 5-mm z-gradient BBI probe (Bruker, Germany), as described previously  (Millard et al. , 2021). Flux calculation. Glucose uptake, acetate and growth rates were calculated from glucose, acetate and biomass concentration-time profiles using PhysioFit (v1.
Kinetic modeling. We used a kinetic model of E. coli  (Millard et al. , 2021)  available from the Biomodels database (https://www. ebi. ac. uk/biomodels)  (Chelliah et al, 2015)  under identifier MODEL2005050001. Simulations were carried out as described in the main text, using COPASI  (Hoops et al, 2006) (v4. 34)  with the CoRC package  (Forster et al, 2020)  (COPASI R Connector v0. 11, https://github. com/jpahle/CoRC) in R (v4. 1. 3, https://www. r-project. org). The R notebook used to perform the simulations and to generate the figures is provided at https://github. com/MetaSys-LISBP/glucose_acetate_interplay. Sensitivity analyses. We used a Monte-Carlo approach  (Saa & Nielsen, 2017)  to estimate the robustness of model predictions. Following the procedure detailed in  (Millard et al. , 2021) , 500 simulated sets of calibration data were generated with noise added according to experimental standard deviations.
Data Availability The data used to make the figures can be found in the Source Data files. The data, model and computer code produced in this study are available in the following databases: • Transcriptomics data : ArrayExpress E-MTAB-11717 (https://www. ebi. ac. uk/biostudies/arrayexpress/studies/E-MTAB-11717) • Model: BioModels MODEL2005050001 (https://www. ebi. ac. uk/biomodels/MODEL2005050001) • Code: GitHub (https://github. com/MetaSys-LISBP/glucose_acetate_interplay).
1 M diethanolamine (Supelco, 1162051000), 1 mM MgCl 2 , pH 10. 0) and covered in CDP-Star1 Luminescent Substrate (0. 25 mM Ready-To-Use) (Life/ThermoFisher, T2146) before being revealed by chemiluminescence in a Chemidoc Touch (Bio-Rad, 1708370). Membranes were stripped by washing twice in boiling NB strip buffer (0. 1X SSC, 0. 5% SDS) and cooling down, then restarting from prehybridization with another probe. The sequences of the oligonucleotide probes are available in S3 Table. Signals were quantified using the ImageJ software and normalized using Ratio of Multiple Precursors method such as described in  [26] : briefly, the signal of a specific precursor is normalized by the Primary Transcript Plus (PTP), upper band formed by the co-migration of 47S, 46S and 45S rRNA, in control and mutated cells.
Genotypic data analysis The population genetic parameter estimates were computed, and their statistical significance was tested with the FSTAT Version 2. 9. 3. 2 software  (Weir & Cockerham, 1984). The gene flow between populations was also assessed using FSTAT. In addition, data were analysed using a Bayesian model-based approach implemented in STRUCTURE, version 2. 3. 4  (Pritchard et al. , 2000; Souto & Premoli, 2003). STRUCTURE uses Bayesian Monte Carlo Markov Chain sampling to identify the optimal number of clusters K for a given multi-locus dataset, without requiring a priori identification of the population subunits. The K optimal value (i. e. , the optimal number of clusters in the dataset) was calculated using STRUCTURE HARVESTER, web version  (Earl & vonHoldt, 2011). STRUCTURE was also used to identify migrants.
The Simpson diversity index D was determined for each marker and possible markers combination  (Hunter & Gaston, 1988). A minimum-spanning tree (MST) was built to illustrate the relationship between the genotypes with the BioNumerics (version 7. 6) software (Applied Maths, Belgium). The degree of similarity was calculated by applying the Dice coefficient test, and an UPGMA dendrogram was generated, with the NTSYS-PC numerical taxonomy and multivariate analysis system (version2. 1; Exeter Software, Setauket, NY, USA).
Genetic polymorphism Genetic polymorphism was measured using allelic richness (A) and Nei's unbiased estimate of genetic diversity within sub-samples. Wright's F statistics were estimated using the Weir and Cockerham method. F IS measures the relative inbreeding of individuals due to the local non-random union of gametes in each subpopulation. The comparison of the genetic diversity data for the two groups revealed that both allelic richness (A) intraspecific and genetic diversity (Hs) were higher in clinical avian (A = 5. 17; Hs = 0. 711) than in environmental (A = 3. 90; Hs = 0. 396) isolates. These results were confirmed by the inbreeding coefficient (F IS ) values estimated for each locus and in each population (F IS = 0. 642; F IS = 0. 547; P ≤ 0. 05).
In camptothecin biosynthesis, for example, gene expansion more likely occurred in C. acuminata through WGD, while with gene tandem duplications in Ophiorrhiza pumila  [8]. Finally, the wealth of information provided by all these large sequencing projects could benefit from a centralized database, browsable with dedicated tools (homology search, synteny analysis), for instance, to streamline gene identification. Normalized ultra-performance liquid chromatographymass spectrometry and transcriptomics data could also be included in such a platform to potentially highlight common or specific gene contents in relation to metabolic profiles.
Acknowledgments We acknowledge funding from the  EU   Horizon 2020 research and innovation program  ( MIAMi project-Grant  agreement  N°814645 ),  ARD2020 Biopharmaceutical program  of the  Région Centre Val de Loire  ( ETOPOCentre  project ) , and  ANR  (project  MIACYC  - ANR-20-CE43-0010 ). The authors apologize for other excellent studies which have not been cited here due to length restriction.
Data analysis of 4sU RNA-seq Reads were preprocessed using CUTADAPT 1. 10 (33) in order to remove adaptors and low-quality sequences and reads shorter than 40 bp. rRNA sequences were removed for further analysis. For samples VQFR188, VQFR189, VQFR191, VQFR192, the remaining reads were aligned to a hybrid genome composed of hg38 and ASM294v2 assemblies of H. sapiens and S. pombe genomes, respectively, and for mESC samples VQFR25, VQFR26, VQFR29, VQFR30 and SNVT220, SNVT221, SNVT222, SNVT223, the remaining reads were aligned to a hybrid genome composed of mm10 and ASM294v2 assemblies of M. musculus and S. pombe genomes respectively with STAR 2. 5. 3a (34). Gene quantification was performed with htseq-count 0. 6. 1p1 (35) , using 'union' mode and Ensembl 93 annotations for all organisms except for S.
pombe where Ensembl Fungi 41 annotations were used. For 4sU-seq data, 'type' option was set to 'gene' in order to take also into account reads aligned onto introns. Differential gene expression analysis was performed using DESeq2 1. 16. 1  (36)  Bioconductor R package on H. sapiens or M. musculus counts normalized with size factors computed by the median-ofratios method proposed by Anders and Huber  (37)  based on the S. pombe spike-in counts (using the following options: cooksCutoff = TRUE, independentFiltering = TRUE, alpha = 0. 05). P-values were adjusted for multiple testing using the Benjamini and Hochberg method  (38). For subse-Nucleic Acids Research, 2022 5 quent data analyses and visualization, only protein-coding genes were considered. Further, a threshold of 2 reads per kb gene length was used to define expressed genes in the U2OS and mESC datasets.
Genome coverage files used for the UCSC genome browser snapshots were generated as follow. For each condition, the BAM files for the two replicates were merged, sampled, sorted and indexed using samtools 1. 9  (39). Big-Wig files were generated using the deeptools 2. 5. 3 bamCoverage command  (40). For TATA-less and TATA-box promoter analysis (violin plots in Figures 2D, 5F and 6F), TATA-box containing promoters were extracted for the human (hg38) and mouse (mm10) genomes using the Eukaryotic Promoter Database (EPD, https://epd. epfl. ch/index. php, (  41 )).
Dynamic exclusion was used. Exclusion duration was set to 20 s, repeat count was set to 1 and exclusion mass width was ±10 ppm. Proteins were identified by database searching using SequestHT (Thermo Fisher Scientific) with Proteome Discoverer 2. 4 software (PD2. 4, Thermo Fisher Scientific) on Homo sapiens database (UniProt, reviewed, release 2020 11 27, 20 309 entries) and Mus musculus database (UniProt, non-reviewed, release 2020 07 13, 55 428 entries). Precursor and fragment mass tolerances were set at 10 ppm and 0. 6 Da respectively, and up to two missed cleavages were allowed. Oxidation (M) was set as variable modification, and carbamidomethylation (C) as fixed modification. Peptides were filtered with a false discovery rate (FDR) at 1%, rank 1 and proteins were identified with 1 unique peptide.
Human U2OS cells do not express the SUPT3H subunit of the SAGA complex When analysing RNA expression data available at the Human Protein Atlas (HPA) database for mRNAs expression of different SAGA subunits in a panel of 64 human cell lines, we noticed that SUPT3H is not expressed in U2OS cells, a human osteosarcoma cell line, while SUPT3H was expressed in all other cell lines analysed (Supplementary Figure  S1A , S1B, S1C). Absence of active transcription was supported by the lack of H3K4me3 and H3K27ac marks as well as Pol II accumulation at the SUPT3H promoter, as compared to the neighbouring RUNX2 gene expressed on the opposite strand (Supplementary Figure  S1D ). To verify this observation, we designed primer pairs to amplify the whole coding sequence (CDS) of SUPT3H from cDNA prepared from U2OS and HeLa cells.
This experiment confirmed the lack of SUPT3H expression in U2OS cells (Figure  1A , Supplementary Figure  S2A ). Similar results were obtained when amplifying smaller fragments of the SUPT3H mRNA (Supplementary Figure  S2A  and S2B ). As an additional control, we generated an U2OS cell line stably expressing FLAG-tagged wild type (WT) SUPT3H protein (Figure  1B , Supplementary Figure  S2C ), which we refer to as U2OS Fl-SUPT3H. Growth curve analyses revealed that U2OS Fl-SUPT3H cells proliferate at a comparable rate than the parental U2OS cells (Supplementary Figure  S2D ). In agreement with the HPA data, we could not detect the SUPT3H mRNA in U2OS cells, while it was clearly detectable in HeLa and U2OS Fl-SUPT3H cells (Figure  1A ). Together these data indicate that the human U2OS osteosarcoma cell line does not express the SAGA subunit SUPT3H.