dataesr/scientific-paragraphs-categorization huggingface dataset:
For some researchers, documenting analyses will mean sharing, for example, R scripts or SPSS syntax; for others it may mean writing a step-by-step description of analyses performed in non-scriptable software programs such as spreadsheets. In all cases, however, the goal is to provide a recipe for reproducing the precise values in the research report. One challenge for sharing analyses is the rapid pace of change in hardware and software (SM: Avoid "works on my machine" errors). Some researchers may find it discouraging to try and create a fully-reproducible analytic ecosystem with all software dependencies completely specified (e. g. ,  Boettiger, 2015 ; SM: Sharing software environments). We have several recommendations. First, do not let the perfect be the enemy of the good. Share and document what you can, as it will provide a benefit compared with not sharing.
Two main options exist to publish Open Access: Green (posting research online through preprint repositories, like PsyArxiv; https://psyarxiv. com) or Gold (full open access via the publisher, most of which currently still charge Article Processing Costs). For the Green route, preprints do not typically affect the traditional publication process as most journals do not consider them a 'prior publication'  (Bourne, Polka, Vale, & Kiley, 2017). A further discussion of Open Access is beyond the scope of this article. However, you can always check a particular journal's stance on open access by typing its name into the SHERPA/ROMEO database (http:// www. sherpa. ac. uk/romeo/index. php). This will also tell you whether the journal makes the final article publicly available on its website, and whether this will require you to pay a fee.
Below we review issues related to privacy and licensing. Considering participants' privacy can be both an ethical issue and a legal requirement (for example, The United States' Health Insurance Portability and Accountability Act and The European Union's General Data Protection Regulation, see SM: EU Data Protection Guidelines). In short, researchers must take appropriate precautions to protect participant privacy prior to sharing data. Fortunately, many datasets generated during psychological research either do not contain identifying information, or can be anonymised ("de-identified") relatively straightforwardly (see SM: Anonymisation). However, some forms of data can be quite difficult to anonymise (e. g. , genetic information, video data, or structural neuroimaging data;  Gymrek et al.
The advantage of these repositories is that they offer additional help and services by professional archiving staff (e. g. , in the creation of study-and variable-level documentation or the conversion of files to nonproprietary formats). 2 We used the content type category from the re3data schema v3. 0 here (see  Rücknagel et al. , 2015). 3 Individual files can be embargoed or made confidential. 4 Dataverse is a special case in several regards. The FAQ on the Mendeley Data website states that they may introduce a freemium model in the future "for instance charging for storing and posting data, above a certain dataset size threshold" (see https://data. mendeley. com/faq). 6 If more storage space or additional services are needed the researchers or their institutions can choose to pay for branded OpenICPSR hosting or the "Professional Curation Package" to access all of the ICPSR (curation) services (see https://www.
openicpsr. org/openicpsr/pricing). 7 The Zenodo terms of use state that "content may be uploaded free of charge by those without ready access to an organized data centre". So, why not share? Given all of the arguments we and others have presented, why would researchers products still not share their data? Beyond the concerns described above (privacy, etc. ), one commonly heard worry is that researchers will make use of shared resources to gain academic precedence ("scooping";  Houtkoop et al. 2018 ). In our view, this worry is usually unwarranted. Most subfields of psychology are not so competitive as to be populated with investigators who are looking to race to publish a particular finding. In addition, in many cases the possibility of being scooped is likely outweighed by the benefits of increased exposure, as noted by Gary King: 4 "The thing that matters the least is being scooped.
Notes 1 One of the major burdens facing scientists is keeping up with the evolution in standards and resources. That's why the SM will be updated regularly and collaboratively. This "live" version is available at http:// open-science-guide. uni-koeln. de will therefore differ from the publisher's version. 2 For example, see the SOP for the Nosek group (https:// osf. io/mv8pj/) and the Green group (https://github. com/acoppock/Green-Lab-SOP).
There is the overall Dataverse Project (https://dataverse. org/),thenthere are different Dataverse repositories (e. g. , the Harvard Dataverse: https://dataverse. harvard. edu/orDataverseNL: https://dataverse. nl/ by the Dutch Data Archiving and Networked Services) which host multiple individual Dataverses (e. g. , by individual universities, research groups or researchers). If the institution a researcher is affiliated with does not have its own Dataverse repository or Dataverse, it is possible to create a Dataverse within the Harvard Dataverse repository. For a more detailed description of Dataverse and its organizational architecture, seeKing (2007)and Leeper (2014).
Acknowledgements The authors thank  Tim van der Zee  and  Kai Horstmann  for helpful comments and  Danië l Lakens  for his help at the start of this project. We also thank  Christoph Stahl  and  Tobias Heycke  for allowing us to use their data and materials for the example project (from  Heycke ,  Aust ,  & Stahl ,  2017 ) and  Luce Vercammen  for proofreading the manuscript. Any remaining errors are the authors' responsibility.
There is the overall Dataverse Project (https://dataverse. org/),thenthere are different Dataverse repositories (e. g. , the Harvard Dataverse: https://dataverse. harvard. edu/orDataverseNL: https://dataverse. nl/ by the Dutch Data Archiving and Networked Services) which host multiple individual Dataverses (e. g. , by individual universities, research groups or researchers). If the institution a researcher is affiliated with does not have its own Dataverse repository or Dataverse, it is possible to create a Dataverse within the Harvard Dataverse repository. For a more detailed description of Dataverse and its organizational architecture, seeKing (2007)and Leeper (2014). 5.
| Data analyses To measure the efficacy of viper mimicry to a human eye, we tested how often humans think a smooth snake is an adder compared to nonmimetic grass snakes (to control for a generic perception of all snakes as adders) and how many times they think an adder is an adder (to account for the general level of identification error). To do so, we modelled the identification tests as a generalized linear mixed model (GLMM) with a binomial error governing the probability of an image being identified as an adder. We included the correct species as a fixed explanatory factor parameterized such that adders represent the intercept. To reflect the sampling structure (every individual was given the same set of pictures to identify), we included picture and interviewee identity as random effects.
To calculate the error in the estimation of ϕ i , we performed Monte Carlo simulations where we drew 10,000 random samples from each parameter estimate distributions and repeated the calculation in Equation 1 to extract a distribution of values for ϕ i. For comparison, we calculated ϕ i for both the smooth snake and the grass snake, where we expect the misidentification component to be much lower. All statistical analyses were conducted in R 3. 3. 1, using function glmer in package lme4 for the GLMM  (Bates, Maechler, Bolker, & Walker, 2015).
Results Here, we tested the hypothesis that karyogamy and inter-nuclear recombination occur in the AMF mycelium using a single-nucleus sequencing approach. Sequenced nuclei were isolated from seven isolates of the genus Rhizophagus, including three known Rhizophagus irregularis dikaryons (A4, A5; SL1  (Ropars et al. , 2016) ), two R. irregularis homokaryons (A1, C2  (Ropars et al. , 2016) ), and two closely related species (Rhizophagus diaphanus MUCL-43196, Rhizophagus cerebriforme  DAOM-227022). Genome data from R. irregularis SL1 (also known as DAOM-240409), R. diaphanus and R. cerebriforme represent new additions to public databases from representatives of the Glomeromycotina, and all sequencing data were assembled using SPAdes  (Bankevich et al. , 2012)  to facilitate comparisons with published genome data  (Ropars et al.
Thus, the higher assembly fragmentation of SL1 may result from other factors such as, for example, the presence of higher heterokaryosis and/or inter-nuclear recombination. In contrast to SL1, the R. diaphanus and R. cerebriforme assemblies harbor only one MAT-locus and show a conventional homokaryotic allele frequency pattern (Figure  1B ). To find direct evidence of inter-nuclear recombination in AMF, we obtained and compared partial genome sequences from 86 single nuclei -that is single haploid genotypes -isolated from R. irregularis dikaryons (SL1, n = 15; A4, n = 14; A5, n = 8), R. irregularis homokaryons (A1, n = 12; C2, shows the hallmark 50:50 ratio reference to alternate allele frequency of dikaryotic isolates. The R. irregularis plots for A1, A4, A5 and C2 represent new analyses of publicly available genome sequence data obtained by  (Ropars et al.
# Figure  3. Selected examples of genotypes, recombination and inter-nuclear variability observed in the dikaryons SL1, A4 and A5. In SL1, there is no obvious co-linearity between the MAT-locus of single nuclei and their genotype. White columns reflect the absence of Illumina sequencing along the nucleus at those homologous positions. The data shown are representative of the genotypes found along the first 100 scaffolds analysed in this study and are based on data available in Supplementary file 5. Variations along homologous nucleotide positions are highlighted in yellow or green, depending on the MAT-locus which the genotype is expected to associate with. The first and third columns represent, respectively the scaffold and position number where the SNP was scored using stringent filtering methodologies.
Materials and methods Single nucleus sorting, WGA, and Illumina sequencing FACS-based sorting of single nuclei and WGA were performed according to  Ropars et al. , 2016 (Ropars et al. , 2016). DNA concentration of the WGA samples was measured by fluorometric quantification using Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific). Subsequently, sequencing libraries were constructed using Nextera XT DNA Library Preparation Kit (Illumina). Library quality and quantity were checked with a Bioanalyzer and the High Sensitivity DNA Analysis Kit (Agilent Technologies) and a Qubit with the DNA HS Assay Kit (Invitrogen). Samples were sequenced on a HiSeq1500 (Illumina) using 100 bp paired-end sequencing in rapid-run mode at LAFUGA (LMU Genecenter, Munich), resulting in 2,020,347,187 reads. Single nuclei sequencing and genome reads are available in NCBI under the following bioproject: PRJNA477348.
Finally, the third criterion was confirming the single copy nature of the SNP using BLAST procedures. In this case, the 100 bp upstream and downstream regions overlapping the scored SNPs were BLAST against the reference genome. If BLAST results returned more than two good hits (e-value of better than 0. 001) this region was considered to be a multi-copy region and thus discarded from downstream analyses. SRR7416438, R. irregularis C2: SRR7416465 to SRR7416473, and R. irregularis SL1: SRR7411814 to SRR7411829). The following datasets were generated: The following previously published datasets were used: Author(s) Year Dataset title Dataset URL.
Acknowledgements We thank  Timothy Y James  and  Linda Bonen  for their comments on an earlier version of the manuscript. NC is supported by the  Discovery program  from the  Natural Sciences and Engineering Research Council of Canada (NSERC-Discovery) , an  Early Researcher Award  from the  Ontario Ministry of  , and the  ZygoLife  project funded by the  National Science Foundation  ( DEB 1441677 ). AH, KS-T, and AB are supported by the  German Research Foundation  (DFG grants  BR 3527/1-1  and  PA 493/11-1 ).
Data availability Single nuclei sequencing and genome reads are available in Genbank under the following bioproject: PRJNA477348. SPAdes assemblies are publicly available on NCBI (R. diaphanus: QZLH00000000, R. cerebrifrome: QZLG00000000, SL1 SPAdes: QZCD00000000, and SL1 ALLPATH-LG: QZCC00000000) along with the core meiosis SL1 genes (RAD21/Rec8: MH974797, MND1: MH974798, DMC1: MH974799, Spo11: MH974800, HOP2: MH974801, MSH4: MH974802, MSH5: MH974803). The whole genome reads are available on NCBI SRA: R. cerebriforme (SRR7418134 and SRR7418135), R. diaphanus (SRR7418169 and SRR7418170), and R. irregularis SL1 (SRR7418171 and SRR7418172). The single nucleus reads are also deposited (R. cerebriforme: SRR7411799 to SRR7411813, R. diaphanus: SRR7410308 to SRR7410319, R. irregularis A1: SRR7416439 to SRR7416450, R.
These assessments were carried out as part of a European Space Agency (ESA) study on the potential role of wideswath altimetry in future versions of the European Union Copernicus programme. The impact of future wide-swath altimetry data is evident for investigating the reliability of sea level values in OSSEs. The most significant results were obtained when looking at the sensitivity of the system to wideswath instrumental error: considering a constellation of three nadir and two "accurate" (small instrumental error) wideswath altimeters, the error in ocean analysis was reduced by up to 50 % compared to conventional altimeters. Investigating the impact of the repetitivity of the future measurements, the results showed that two wide-swath missions had a major impact on sea-level forecasting -increasing the accuracy over the entire time window of the 5-day forecasts -compared with a single wide-swath instrument.
A spectral analysis underlined that the contributions of wide-swath altimetry data observed in ocean analyses and forecast statistics were mainly due to the more accurate resolution, compared with along-track data, of ocean variability at spatial scales smaller than 100 km. Considering the ocean currents, the results confirmed that the information provided by wide-swath measurements at the surface is propagated down the water column and has a considerable impact (30 %) on ocean currents (up to a depth of 300 m), compared with the present constellation of altimeters. The ocean analysis and forecasting systems used here are those currently used by the Copernicus Marine Environment and Monitoring Service (CMEMS) to provide operational services and ocean reanalysis. The results obtained in the OSSEs considering along-track altimeters were consistent with those derived from real data (observing system experiments, OSEs).
Introduction Satellite altimetry measurements, based on nadir radar altimeters, have made a fundamental contribution to the un-Published by Copernicus Publications on behalf of the European Geosciences Union. derstanding of ocean circulation  (Fu and Chelton, 2001; Le Traon and Morrow, 2001)  over the past 2 decades. The continuous improvement of the physics aspects in ocean general circulation models (OGCMs) and of data assimilation schemes (DAS)  (Bell et al. , 2015)  now make it possible to exploit these unique sources of information in global and regional ocean monitoring and forecasting systems  (Le Traon et al. , 2017b). The focus of the present study is to investigate the impact of wide-swath altimetry on a high-resolution regional ocean analysis and forecasting system. This is carried out as part of a European Space Agency (ESA) study on the potential role of wide-swath altimetry for the further development of the European Union Copernicus programme (space component).
, 2009) , will make nextgeneration satellite altimetry measurements with a high potential for ocean analysis and forecasting. The NASA/CNES SWOT mission will be the first wide-swath altimetry mission and will be launched in 2021. The wide-swath altimetry concept is expected to make an essential contribution to operational oceanography  (Bell et al. , 2015; Le Traon et al. , 2017b). Conversely, the temporal resolution of wide-swath data, considering SWOT-like orbit parameters (repeat cycle of 21 days with a 10-day sub-cycle), may be not suitable for resolving the evolution of mesoscale eddies  (Ubelmann et al. , 2015). A major challenge will be the combination of data from both wide-swath and conventional along-track altimeters  (Pujol et al. , 2012)  with high resolution OGCMs to enable a dynamical interpolation of wide-swath data and a detailed description and forecast of the ocean state at high resolution.
This approach was also adopted in this study, using two different configurations of the Nucleus for European Modelling of the Ocean (NEMO;  Madec, 2016). The first configuration is used to perform a "natural" run (hereafter NR) to represent the "true" ocean. Synthetic observations are obtained by sampling the NR in order to mimic either an existing or future observing system. Using data assimilation techniques, the synthetic observations are then fed into numerical simulations performed with a second OGCM configuration to obtain a representation of the state of the ocean constrained by the observing system considered (assimilated run). The impact of the simulated observation system is quantified by comparing the assimilated run (AR) with the NR, and the different performances among the OSSEs can then be assessed by the reduction (increase) of ocean analysis and forecast errors due to considering (neglecting) the new observing system designs.
OSSE approach ESA is conducting a study to assess the feasibility and potential of wide-swath altimetry for the EU Copernicus programme. The main objective is to provide a much improved operational monitoring of ocean mesoscale variability for the Copernicus Marine Service (e. g. Le Traon et al. , 2017a). Different wide-swath altimeter concepts were analysed by Thales Alenia Space (TAS) as part of this ESA study. Compared to the SWOT mission that is focused on sub-mesoscale variability, these European wide-swath altimetry concepts have less stringent noise measurement requirements. Their potential for ocean analysis and forecasting are analysed here by means of OSSEs. In this section we describe the OSSEs components, represented by the OGCM configurations used to obtain the synthetic observations and to perform data assimilation experiments, the DAS adopted to consider the new observing systems in ocean analysis, and the simulated ocean observations and their errors.
Sea surface temperature (SST) and temperature and salinity profiles To accurately assess the impact of satellite altimetry data on ocean analyses and forecasts, the same synthetic observations of SST, T and S profiles were considered in all of the experiments. An SST map representing a daily average is assimilated during each 5-day assimilation cycle. Figure  2  shows an example of a synthetic SST field to be assimilated for 15 June 2009. Temperature and salinity profiles are extracted at the same points and the same dates as the real in situ profiles found in the CORA3. 2 database from CORIO-LIS data centre  (Cabanes et al. , 2013). Figure  1b  and c  show the number of T and S profiles available during 2009 over the IBI region. (e) Along-swath error given by the radar interferometer noise and (f) along swath simulated data.
Satellite altimetry data In order to investigate the impact of different constellations of satellite altimeters, both conventional along-track and wide-swath altimetry measurements were considered. Conventional altimetry data were derived from sampling the NR over the theoretical tracks of the Jason-2, CryoSat-2 and Sentinel-3a satellite missions, with a sampling frequency of 1 Hz (∼ 7 km spatial sampling; e. g. Roblou et al. , 2011 ). An observation white noise of 1 cm root mean square was simulated and added to these pseudo-observations. Using the same approach, wide-swath data were derived considering a 20. 9-day repeat orbit at a spatial resolution of 7 km along and across the swath. In order to investigate the impact of multiple wide-swath altimeter missions, the data were derived by simulating two wide-swath altimeters, hereafter Swath-1 and Swath-2, obtained considering a 10-day shift in the orbits of the simulated missions.
Wide-swath altimetry data As previously mentioned, wide-swath altimetry observations were obtained by sampling the NR in such a way as to mimic wide-swath SSH measurements. A 7 km grid resolution was considered to be consistent with the horizontal resolution of the AR configuration. Wide-swath altimetry measurements can be characterized by correlated and uncorrelated errors. Several studies have given a detailed description of wideswath (SWOT-like) altimetry errors (e. g. Esteban Fernandez et al. , 2014; Dibarboure and Ubelmann, 2014; Gaultier et al. , 2016; Ubelmann et al. , 2015; Ruggiero et al. , 2016). Aware of the importance of a full characterization of the errors to exploit the information coming from wide-swath altimetry data  (Ubelmann et al. , 2017) , and as this work is a first attempt to investigate their contribution to the ocean analyses and forecasts, we focus only on the instrumental uncorrelated errors due to the radar interferometer (thermal noise) and on their cross-track variability.
Experimental set-up: OSSEs In this section we describe the design of the OSSEs performed to investigate the impact of wide-swath altimetry data assimilation in an ocean monitoring and forecasting system in the IBI region. First, a reference experiment, hereafter referred to as OSSE0, was performed considering the AR configuration but without any synthetic observations. Four other different OSSEs were carried out varying the type and number of altimeter missions considered. To assess how the constellation of nadir altimeters constrain the ocean analysis and forecast, an experiment was performed exclusively considering conventional nadir altimeters (Jason-1, CryoSat-2, Sentinel-3a), hereafter referred to as OSSE1, assuming an instrumental error of 1 cm. A second experiment, hereafter referred to as OSSE2, was performed considering nadir altimeters and Swath-1 data to address the question of the impact of future SSH measurements based on both nadir and wide-swath altimeter missions.
As previously mentioned, SWOT-like data have a temporal resolution that does not allow the evolution of mesoscale structures to be resolved correctly. In order to investigate the impact of the repeat cycle of wide-swath altimetry data, in the OSSE3 experiment an additional wideswath altimeter was considered relative to OSSE2. This first series of OSSEs (OSSE2 and OSSE3) was performed assimilating wide-swath altimetry data simulated assuming a radar interferometer error that ranged between 0. 8 cm in the inner part of the swath and 2 cm at the outer edges. The instrumental error was selected, in close collaboration with TAS, to consider less stringent noise requirements compared to the NASA/CNES SWOT mission. In particular, we considered an instrumental error four times larger than the error prescribed for the Ka-band Radar Interferometer (KaRIN) onboard the SWOT mission (TAS technical report).