dataesr/scientific-paragraphs-categorization huggingface dataset:
Matthew Latimer  for assistance. The authors acknowledge the  Paul Scherrer Institut, Villigen, Switzerland  for provision of synchrotron radiation beamtime at beamline SuperXAS of the  SLS  and would like to thank  Dr. Maarten Nachtegaal  for assistance. The authors acknowledge  DESY (Hamburg, Germany) , a member of the  Helmholtz Association HGF , for the provision of experimental facilities. For parts of this research carried out at PETRA III, the authors would like to thank  Dr. Edmund Welter  for assistance in using beamline P65. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 312284. Furthermore, authors would like to thank  Josef Vaeßen  ( ITMC, RWTH Aachen ) for BET absorption measurements,  Hannelore Eschmann  and  Elke Biener  ( ITMC, RWTH Aachen ) as well as  Annika Gurowski ,  Alina Jakubowski , and  Justus Werkmeister  ( MPI-CEC ) for GC and GC-MS measurements,  Norbert Pfänder  ( MPI-CEC ),  Adrian Schülter ,  Silvia Palm  ( MPI-Kofo ),  Simon Cayez  (LPCNO) and  Maria Teresa Hungria  ( Centre de MicroCaractérisation Raimond Castaing ) for help concerning TEM, STEM-HAADF-EDX and SEM-EDX analyses, and  Claudia Weidenthaler  for XRD measurements.
Database Compilation We compiled a database of 14 C measurements of CO 2 , CH 4 , DOC, and POC from the northern permafrost region. We defined this region based on the permafrost coverage previously outlined  (Brown et al. , 1997). The database included data from 50 studies, mostly published prior to May 2018, identified through Web of Science using the keywords "radiocarbon CO 2 permafrost", "radiocarbon CH 4 permafrost", "radiocarbon DOC permafrost", or "radiocarbon POC permafrost". Additional studies were identified through citations in the reviewed studies (Table  1 ). We focused on 14 C measurements of gaseous soil emissions and waterborne ecosystem C fluxes but the database also included C forms belowground, such as soil gases and pore water DOC. We did not include 14 C measurements of the SOC pool, which can provide estimates of SOC turnover at decadal to millennial time-scales (e.
g. ,  He et al. , 2016)  but provide little information on the form of C release to the atmosphere or aquatic ecosystems. Further, we did not include compound-specific 14 C analysis, such as plant wax lipid compounds, lignin phenols, or hydroxy phenols. Most of the 14 C data were obtained from tables in the studies, or were provided by authors, but some data (143 14 C measurements) were approximated from figures if no other options were available. The data set  (DOI 10. 5281/ zenodo. 3832031 ) is available at https://zenodo. org/record/3832031#. XsJ8vntCSUl and has also been included in the ISRaD repository (http://www. soilradiocarbon. org). We classified 14 C measurements to be associated with five broad ecosystem types: tundra, boreal forest, peatlands, lakes, and rivers/streams. Ecosystem classification was based on descriptions provided in the published studies.
Further, many studies used natural or experimental disturbances to assess vulnerability of thawed SOC, and we classified 14 C data to be associated with either undisturbed or disturbed sites within each ecosystem type. We considered disturbed sites to include active layer thaw gradients (tundra), warming/wetting/drying manipulations (tundra and one peatland), wildfire (tundra and boreal forest), and thermokarst (lakes, peatlands, and streams). Data were further grouped by C form (CO 2 , CH 4 , DOC, and POC) in order to assess the effects of disturbance on 14 C of CO 2 and CH 4 and highlight the 14 C variability of DOC and POC along the soil-stream-river continuum (supporting information Table  S1 ). We grouped gaseous (CO 2 , CH 4 ) measurements by collection method, distinguishing samples released to the atmosphere and those obtained belowground.
Global Biogeochemical Cycles characterize different sources and account for 14 C variability along the soil-stream-river continuum. DOC was defined as organic C passing through a 0. 2-to 1-μm filter (generally 0. 45 μm) depending on the study, whereas POC reflects the size class of organic C that cannot pass through the filter. Soil pore water included in situ sampling in thaw streams (thaw slump rill water), and waters obtained in the laboratory from soil cores and soil leachates. Samples from tributaries were collected from small and large streams as defined by the studies and were classified as either glaciated or unglaciated catchments. We refer to glaciated catchments for data collected in tributaries draining present-day glaciers. For clarity, the few available DOC and POC data from lakes (26 14 C measurements) were considered to be part of the catchment drainage network and, thus, were merged into the nonglaciated tributary category.
Many of the compiled DOC and POC data came from the sampling of mainstem locations on the six largest Arctic rivers by the Arctic Great Rivers Observatory (ArcticGRO; https://arcticgreatrivers. org/) (e. g. ,  McClelland et al. , 2016; Raymond et al. , 2007). For all sites, δ 13 C (Figure  S1 ) and other ancillary data were compiled when available (sampling date, coordinates, discharge, concentrations). Data were also grouped by region to characterize the spatial distribution of measurements across the circumpolar North; including Alaska, Western Canada, Eastern Canada, Greenland, Europe, Western Siberia, and Eastern Siberia (Figure  1 ). Western Canada included data from Yukon, Northwest Territories (NT), and Alberta. Eastern Canada included data from Nunavut and the Hudson Bay Lowlands. Europe refers uniquely to data from Sweden.
We used the catchment area of the Yenisey River to differentiate between Western and Eastern Siberia. Data on 14 C of DOC and POC from the main stems of ArcticGRO rivers are included in these regions as follows: Alaska (Yukon River), Western Canada (Mackenzie River), Western Siberia (Ob' and Yenisey Rivers), Eastern Siberia (Lena and Kolyma Rivers). All 14 C data were converted to fraction modern (fM) using defined equations  (Stuiver & Polach, 1977) : Δ 14 C 0 00 to fM fM ¼ Δ14C 0 00 1000 þ 1 e λ 1950 -Ys ð Þ ; (1) (3) where λ equals 1/8,267 year -1 (i. e. , 1/true mean life of radiocarbon), Ys represents the year of sampling (we assume the 14 C analysis was in the same year as sample collection unless stated), and Age represents the uncalibrated age ( 14 C age expressed as years before present where present is CE 1950).
Global Biogeochemical Cycles that extended from west of the Lena River to Alaska and parts of the Yukon Territory  (Grosse et al. , 2013; Schirrmeister et al. , 2013). Such Pleistocene-aged measurements mostly come from terrain underlain by ice-rich loess sediments in Yedoma regions but also include regions where glaciogenic tills are overlain by Holocene-age organic matter in soils  (Littlefair et al. , 2017). While raw 14 C measurements can indicate the relative contribution of "old" SOC to a C flux, it cannot by itself be used to assess the absolute rate of "old" SOC mobilization. Estimating the rate of "old" SOC release requires, at least, both 14 C data as well as a measure of overall C flux rate. Our database does not include information about the ecosystem C fluxes associated with each 14 C measurement, as such paired data were not provided in the majority of studies.
Radiocarbon Data From the Northern Permafrost Region The compiled database included 1,877 14 C measurements from 51 sites, of which 50% were associated with CO 2 , 14% with CH 4 , 27% with DOC, and 9% with POC (Figure  2a ). Measurements of surface-to-atmosphere greenhouse gases fluxes were overall more common than belowground measurements, which accounted for 41% of the total CO 2 data and 12% for CH 4. Similarly, most 14 C measurements of DOC and POC were collected from streams and rivers, while belowground pore water measurements accounted for only 10% of DOC data and 3% of POC data. The distribution of 14 C measurements among ecosystems, regions, and C forms was not even (Figure  2 ). Tundra ecosystems had the most measurements at 700, compared to only 50 from the boreal forest, despite the much greater size of the boreal forest within the permafrost region.
Measurements from tundra and boreal forests were both dominated by CO 2 measurements, while there was a greater diversity in measured C forms for lakes and peatlands (Figure  2a ). In total, 32% of the 14 C data were from Alaska, 24% from Greenland, 16% from Western Canada, 15% from Eastern Siberia, 9% from Western Siberia, 3% from Eastern Canada, and 1% from Europe (Figure  2b ). In our database, 90% of the 700 14 C measurements from tundra ecosystems were from 10. 1029/2020GB006672.
Global Biogeochemical Cycles only two sites within Alaska and Greenland (Figure  2c ). Data for rivers and streams were more evenly spread across regions because DOC and POC data from ArcticGRO included the largest Arctic Rivers, and the greater number of sites for this "ecosystem" category was due to sampling of high-to middle-order tributaries and smaller fluvial systems to characterize the fluvial network. Data collected between May and September accounted for 95%, 85%, and 82% of all CO 2 , DOC, and POC measurements, respectively. This distribution was more spread over the year for CH 4 with 57% of measurements between May and September, due to more frequent lake measurements collected between fall and early spring.
The simulation boxes We analyse the gravo-magnetohydrodynamical simulation Illustris-TNG  (Nelson et al. 2019). This simulation follows the coupled evolution of DM, gas, stars, and black holes from redshift z = 127 to z = 0. Illustris-TNG is run with the movingmesh code Arepo  (Springel 2010) , and the values of the cosmological parameters are those of the Planck 2015 (Planck  Collaboration et al. 2016 ) results: Ω Λ,0 = 0. 6911, Ω m,0 = 0. 3089, Ω b,0 = 0. 0486, σ 8 = 0. 8159, n s = 0. 9667 and h = 0. 6774. We focus on the simulation box TNG300-1 at a redshift z = 0. It consists in a cube of around 302 Mpc side length with a DM resolution of (m DM = 4. 0×10 7 M /h and N DM = 2500 3 ). We choose to use the largest simulation volume with the highest mass resolution to accurately describe cosmic filaments down to small scales.
Acknowledgements. The authors acknowledge the useful comments of the anonymous referee. This research has been supported by the funding for the  ByoPiC  project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  grant agreement  ERC-2015-AdG 695561. (ByoPiC, https://byopic. eu). The authors acknowledge the very useful comments and discussions with all the members of the ByoPiC team (https://byopic. eu/team/), in particular with Victor Bonjean. We thank  Klaus Dolag  and  Antonio Ragagnin  for making the Magneticum galaxy catalogue publicly available. We also thank the Illustris-TNG team for releasing publicly the full simulated snapshots.
In parallel, and also for more than two decades, new ways of producing and sharing information based on the commons, or commons-based peer production models  (Benkler 2006) , have also been emerging online, with the development of information and digital commons  (Bollier and Pavlovich 2008; Stalder 2010 ; Dulong de Rosnay and Le Crosnier 2012). Free and Open Source software (F/OSS, which started as a movement in 1983), Free Culture, Wikipedia, and the open science and open data movements are providing content with more freedoms to users and authors. Information and digital commons have proved a noteworthy alternative to the business models of the entertainment and knowledge economy built on exclusive rights on software, scientific articles, and cultureall produced within the framework of a strong intellectual property system, crucial for commercial production models.
Data We use estimates of the amplitude of mountain-waveinduced cooling (i. e. maximum cooling) from Atmospheric Infrared Sounder (AIRS) measurements of radiance pertur-bations for a 16-year period from 2002 to 2017  (Hoffmann et al. , 2016 (Hoffmann et al. , , 2017)) , as well as from radiosonde soundings for a 14-year period from 2002 to 2015  (Moffat-Griffin et al. , 2011). The nadir-scanning AIRS instrument is on board NASA's Aqua satellite, which since 2002 has typically made four passes per day over the Antarctic Peninsula, performing an across-track scan covering a distance of 1765 km on the ground. Each scan consists of 90 individual footprints that vary in size between 13. 5 × 13. 5 km 2 at nadir and 41 × 21. 4 km 2 at the scan edges. Here we use the 666. 5 cm -1 radiance channel of AIRS, which peaks in sensitivity to atmospheric temperatures at an altitude of around 22 km and has a full width at half maximum of 9 km, i.
, 2012). The radiosonde soundings were launched around two to four times per week from Rothera Research Station, which is located along the western side of the Antarctic Peninsula. See  Moffat-Griffin et al. (2011)  for more details of the soundings. Figure  1  shows a map of the Antarctic Peninsula, which includes the location of Rothera Research Station, as well as orography from the Bedmap2 dataset  (Fretwell et al. , 2013).
Acknowledgements. We thank the two anonymous reviewers who provided helpful comments on an earlier version of this paper. This work used the  ARCHER UK National Supercomputing Service. We acknowledge the hard work of the  Rothera  field staff, who made the radiosonde measurements. Financial support. This work was undertaken as part of the  Polar Science for Planet Earth Programme  of the  British Antarctic Survey  and funded by the  Natural Environment Research Council (NERC). James Keeble  and  Nathan Luke Abraham  thank  NERC  through  NCAS  for financial support. Review statement. This paper was edited by  Michael Pitts  and reviewed by two anonymous referees.
Code and data availability. The AIRS measurements of brightness temperature perturbations used in this study can be downloaded from https://datapub. fz-juelich. de/slcs/airs/gravity_waves (re3data. org, 2020). The high-resolution radiosonde data from Rothera Research Station can be downloaded from https: //catalogue. ceda. ac. uk/uuid/37f2bef57e28bcd780a5cbfe077f4bf8 (British Antarctic Survey, 2008). Please contact the lead author if you would like access to the UM-UKCA output.
We next provided the mosquitoes with a choice of PCC 6506 against PCC 8913, the latter spiked with geosmin, at an amount (~10 -7 ) roughly equivalent to the release of geosmin from PCC 6506 as determined by SPME/GC-MS (data not shown). Mosquitoes confronted with this choice showed no preference either way (Figure  2E ). We then examined how larvae react to the presence of cyanobacteria. Larvae screened in the same two-choice larval assay as before showed an overall preference to the side baited with PCC 6506 (Figure  2F ). Similar to the egg laying behavior of the adults, the larval positional preference was also dependent upon Orco-expressing neurons (Figure  2F ). We conclude that mosquitoes preferentially lay eggs in water containing cyanobacteria-producing geosmin. This preference is also observed in larvae, which presumably associate geosmin with the presence of food.
Respective RI values were compared with each other and analyzed for statistical significance. Electrophysiology-Electroantennogram (EAG) recordings were performed using Ag-AgCl electrodes and glass capillaries filled with ringer solution (137mM NaCl; 3. 6 mM CaCl 2 ). Female Ae. aegypti were cold anesthetized for one minute before securing the body between sticky tape and dental wax. The glass capillary connected to the indifferent electrode was placed in the eye, whereas the glass capillary connected to the recording electrode was placed over the tip of the antennae. The signals were passed through a high impedance amplifier (IDAC-4, Syntech, the Netherlands) and analyzed using a customized software package (Syntech EAG-Pro 4. 6, Syntech, the Netherlands). 10 μL aliquots of each dose of geosmin (diluted in water from 10 -1 geosmin, Pell Wall Perfumes, UK) (10 -2 , 10 -3 , 10 -4 , 10 -5 , 10 -6 ) was added onto a pre-cut Whatman filter paper (WHAT1001500, Sigma Aldrich) which was inserted into a sterilized Pasteur pipette.
Experiments were performed at different depths from the ventral surface of the AL (at 15, 30, 50, 75 and 90 μm), allowing characterization of glomerular responses to geosmin across the AL and allowing these glomeruli to be repeatedly imaged across preparations. The z-plane depths were selected to maximize the number of imaged glomeruli, while keeping repeated stimulation of the same odors to a minimum. To record odor-evoked responses, images were collected from a 110 μm × 83 μm plane at 2 Hz (line period of 1 ms), and for each odor stimulus images were acquired for 35 s, starting 10 s before the stimulus onset. Image data were imported into MATLAB (v2017; Mathworks, USA) for Gaussian filtering (2 × 2 pixel; σ = 1. 5-3) and alignment using a single frame as the reference at a given imaging depth and subsequently registered to every frame to within ¼ pixel.
DATA AND CODE AVAILABILITY This study did not generate or analyze code. Raw data are available upon request from the corresponding author Marcus Stensmyr (marcus. stensmyr@biol. lu. se). (F) Responses to geosmin across all sampled glomeruli; only the PD3 glomerulus (bar in green) showed significant calcium dynamics to geosmin compared to the solvent control (Kruskal-Wallis test: p < 0. 05). Glomerular nos. 1-27 were tentatively mapped to PM1, PM2, V1-3, AM2-5, AL3, LC2, LC1, AC1, AL1, AL2, PL2, MC2, PL4, PD1, PD3, PC1, MD1-3, PD4, PD2, and AD1, respectively. Bars represent the mean ± SEM. (G) Dynamics of the calcium response to geosmin (green trace) and the solvent control (dipropylene glycol [DPG], black trace) for the putative PD3 glomerulus. Lines are the mean; shaded areas are the SEM.
ACKNOWLEDGMENTS We thank  Christopher Potter  for valuable comments on the manuscript and members of our respective labs for critical feedback during the project. We thank  Elisabeth Barane  for expert assistance with growing cyanobacteria and  Prof. Antonio Euzebio  Goulart Sant'ana for support with the Brazilian field-work. N. M. and M. C. S. were supported by the  Crafoord Foundation ,  Carl Tryggers Foundation , and the  Swedish Research Council. A. L. C. , R. A. , and M. D. were supported by The  Florida Department of Agriculture and Consumer Services ("Highly attractive biological insecticide trap (HABIT)  to reduce mosquito populations") and The  Centers for Disease Control and Prevention (CDC) ,  Southeastern Center of Excellence in Vector-borne Disease. We would like to thank the entire  DeGennaro laboratory  for their help in obtaining the field collections of mosquito eggs.
G. H. W. and J. A. R. were supported by awards from the  National Science Foundation  ( IOS-1354159 ),  Air Force Office of Scientific Research  ( FA9550-16-1-0167 ), and  University of Washington Innovation Award. M. G. and the  Pasteur Collection of Cyanobacteria  is funded by the  Pasteur Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of respective employers or the CDC.
2a ). This enrichment was confirmed by qRT-PCR analysis performed following HA-immunoprecipitation in different D2-RiboTag mice (Fig. 2b ). Cross analysis of our data with in situ hybridization (ISH) profiles from the Allen Brain Atlas further validated the differential gene expression. Close inspection of the distribution of genes more expressed in DS indicated that expression patterns are highly heterogeneous and could be visually classified into different categories including widespread (Foxp1, Pcp4, Camkk2, Itpr1), lateral DS restricted (Ace, Me2, Rgs7bp, Acvrl1), medial DS restricted (Rbp4, Fos, Ntm, Ddit4l), sparse (Cd4, Cit, B3gnt2), or patch/matrix (Calb1, Pdp1, Cdh8, Plxnd1, Sepw1)  17  (Fig. 2c  and Supplementary Fig. 4a ). Differential expression was also confirmed at the protein level for neurogranin   (Nrgn), D2R, calbindin-D28k (CB), and dopamine-and cAMPregulated phosphoprotein, Mr 32 kDa (DARPP-32) (Fig.
Systematic molecular classification of DS and Acb D2R neurons. In order to provide further information about molecular expression biases between the DS and the Acb, we implemented a classification by sorting genes of interest according to neurotransmitter systems based on the publicly available IUPHAR/BPS database (www. guidetopharmacology. org). Thus, genes encoding receptors, transporters, and enzymes involved in neurotransmitter turnover were categorized as Acb-enriched (orange), DS-enriched (pink), not enriched (gray), or not expressed (black) and organized for various neurotransmitters systems including 5-HT, catecholamines, GABA, glutamate acetylcholine, and endocannabinoid (Supplementary Fig. 6a-p ). This representation, which allows a rapid overview of the regional enrichment of genes in D2R neurons, revealed overwhelming differences between the DS and Acb.
5e , f and Supplementary Data 9). Finally, we cross-analyzed our results with recent single-cell RNAseq study defining new D2R-and D1R-SPNs subpopulations  13. Among the 61 gene products defining the D2R-Htr7-SPNs subpopulation, 30 were found to be also enriched in Acb D2R neurons in our dataset (Fig. 5g  and Supplementary Data 10). Such analysis also confirmed that WFS1 neurons are enriched in gene products classifying D1R-SPNs and some of their discrete subpopulations (Fig. 5h  and Supplementary Data 10). D2R from WFS1 neurons control digging behavior. To parse the role of D2R in WFS1 neurons, we generated temporally controlled conditional Drd2 knock-out mice (D2R-cKO) by crossing the tamoxifen-inducible Wfs1-CreERT2 BAC transgenic mouse line with the Drd2 loxP/loxP line. Three weeks after tamoxifen treatment, the efficiency of Drd2 ablation was confirmed by ISH using Basescope assay (Fig.
All data are presented as mean values ± SEM. g ISH of imprinted genes from the Allen Brain Atlas and coronal section of Dlk1 immunostaining (n = 3 mice/group). h Th ISH from the Allen Brain Atlas. i Coronal section of GFP staining in Th-eGFP mice (n = 3 mice/group). j, k Double IF for GFP and DARPP-32 and quantification of GFP/DARPP-32 cells in the Acb of Th-eGFP mice (n = 3 mice/group). l, m Double IF for GFP and CR, NPY, and ChAT and quantification of co-expressing cells in the Acb of Th-eGFP mice (n = 3 mice/group). DS dorsal striatum, Cx cortex, Acb accumbens, AcbC accumbens core, AcbSh accumbens shell, ICjM island of Calleja, Tu olfactory tubercules, aca anterior commissure, lv lateral ventricle, cc corpus callosum. We then stimulated D2R indirectly by boosting striatal DA levels using the psychostimulant amphetamine.
Protein quantification and WBs were performed as described  45. The antibodies used are summarized in a Supplementary resource table. When necessary, membranes were stripped in buffer containing 100 mM glycine (pH 2. 5), 200 mM NaCl, 0. 1% Tween 20, and βmercaptoethanol for 45 min, followed by extensive washing in 100 mM NaCl, 10 mM Tris, and 0. 1% Tween 20 (pH 7. 4). For quantitative purposes, the optical density values of each antibody were normalized to β-actin values from the same sample. Acb values were referred as 100% as control group and DS values were represented with respect to Acb. In all blots, quantified data are shown as the mean ± SEM (n = 7 mice/brain subregion) (Supplementary Fig. 12 ). Immunofluorescence. Tissue preparation and IF were performed as described  45. Primary antibodies used are listed in the resource table (Supplementary Information).