dataesr/scientific-paragraphs-categorization huggingface dataset:
Survey procedure. In each country the questionnaire had to be distributed at least twice during two distinct seasons. The requirement for two distinct seasons was lowered for places with only minor variations in outdoor weather conditions throughout the year. Data were collected from a minimum of 100 respondents per country (a minimum of 50 per season). The following conditions had to be followed for the distribution of the questionnaires: • Timing: at the end or if necessary during classes, when participants had been seated at least 30 minutes • Form: paper-pencil • Language: local language (in case of large groups of foreigners in a country/class (e. g. Chinese in Korea), researchers were free to distribute more than one language version. On a separate sheet, researchers noted the following additional information: • City and Country of survey Fig.
2  Exemplary response to one of the free-positioning tasks used in the main part of the questionnaire. Participants were asked to position the verbal anchors shown to the left on the empty line with verbal anchors at extreme positions only. Grey lines and letters present the drawing by one participant. The complete question together with instructions and examples for participants is available online  23. In addition, researchers acquired data of the outdoor conditions (outdoor temperature and humidity) from close-by weather stations (either owned by the researchers, available to researchers, or using public sources) and optionally recorded the indoor conditions during the distribution period. Despite their significant influence on thermal perception, indoor conditions were made optional for the following two reasons.
Data preparation. Individual research groups prepared the data from their questionnaires and submitted for each application two files: one containing the data transferred from the questionnaire, one containing the additional information for each application. The positions of the labels drawn in the free positioning task were quantified using a ruler and measuring the distance of the positioned label to the left end of each horizontal line. Upon reception of a dataset, the project leader validated the dataset by means of an automated script (see section Technical validation). In addition, the project leader made the following adjustments to harmonize the data and added further variables by means of an R script available online  23  (see section "Custom code used" below): Adjustments: • Researchers participating in this study were advised to print the questionnaire, so that each line representing a linear scale was exactly 100 mm long.
Additional variables: • KG class: Koeppen-Geiger (KG) classifications were derived for the place of survey (provided by the researcher), and the places of current residence, previous residence, and origin (as stated by the participants). To obtain the KG class for each combination of city and country, the KG world map (Version March 2017) provided for R (http://koeppen-geiger. vu-wien. ac. at/present. htm) was used. This map is based on data from 1986 to 2010 and is the re-analysed KG map with a resolution of 5 arc minutes using the downscaling algorithms  25. In order to obtain the KG class automatically, the latitude and longitude were first derived based on Nominatim, the search engine for OpenStreetMap data (http://nominatim. openstreetmap. org), then converted to the pixel number of the map.
"Middle" was assigned to those living 1 to 3 years in the current KG class, but a different one before. All others, i. e. living more than 3 years in the current KG class are "high". • Native: The variable native speaker was a binary variable (yes/no) generated. Responses are marked as "yes", in case the language of the questionnaire is equal to (one of) the language(s) spoken in the country of origin of the respondent. All other responses were marked as "no". • Country of residence plausible: Participants reported their country of residence. This record was compared to the country of application noted by the researcher. In case these two countries differed (52 responses), the new variable "Country of residence plausible" was set to "no", otherwise "yes". The variables available in the dataset, including their measurement scale, and levels (if applicable) are presented in Online-only Table  1.
Data records All data records listed in this section are available from the project page  23  on OSF and can be downloaded without an OSF account. The information regarding the cities of current residence, previous residence and origin were removed, as they can serve to identify an individual participant. The R script used for pre-processing the semi-raw data is also available. The data were licensed under a CC0 1. 0 Universal license. Data structure. All Datasets. File format: comma separated values file (. csv).
technical Validation Incoming datasets were rigorously checked in several steps: (1) Visual inspection of the datasets, whether they comply with the prescribed formats. If not, datasets were send back to researchers. (2) Semi-automated R Script to validate a. Spelling of country names, city names, and language codes b. Whether KG class can be derived automatically (otherwise KG class was added manually) c. Whether data points are within the expected range (e. g. relative humidity between 0 and 100%), or one of the available categories (e. g. only one of 7 age groups) (3) Additional checks. After combining all datasets, combinations of data points were validated. For example, it can be expected that the verbal anchors of the sensation scale are drawn in the right order, i. e. from cold to hot.
Usage Notes For further analyses, it is recommended to use the final dataset on OSF and not the individual raw datasets, because revisions by data providers have only be made on the final dataset. In total, 9111 questionnaires were distributed and 8225 responses collected (90% response rate). Note that the dataset provided consists of all 8225 questionnaires submitted. The authors did not want to exclude any questionnaire from the dataset based on specific exclusion criteria, e. g. outlier definition or completeness of questionnaire. Future users of the dataset can make their own decisions, which questionnaires they consider as valid or not. Any exclusion criteria from the authors side, would limit the freedom of future users to make such decision.
version and development of methods. A. A. -Z. contributed to datasets IQTUB. M. A. d. A. contributed to datasets BR, was involved in translating and piloting the Portuguese version. F. A. -A. contributed to datasets JOGJU, was involved in translating and piloting the Arabic version. H. A. -K. contributed to datasets OMSQU. R. R. A. contributed to datasets IDITB was involved in translating and piloting the Indonesian version. H. A. contributed to datasets DEBUW. R. A. contributed to datasets GBUOS and GBUCL. E. Am. contributed to datasets GRCUA, was involved in translating and piloting the Greek version. A. Y. A. contributed to datasets USMIT. M. A. contributed to datasets GBCOV. E. Az. contributed to datasets AEKUS. B. B. contributed to datasets IRSHZ, was involved in translating and piloting the Farsi version.
A. B. contributed to datasets NGBUA. S. B. contributed to datasets DEKIT, was involved in preparing the English version, translating and piloting the German version and development of methods. C. B. contributed to datasets BR, was involved in translating and piloting the Portuguese version. B. C. contributed to datasets CNTHU, was involved in translating and piloting the simplified Chinese version. J. -H. C. contributed to datasets USUSC. C. C. contributed to datasets KRYON, was involved in translating and piloting the Korean version. H. D. contributed to datasets NLVUA. S. A. D. contributed to datasets IDITB was involved in translating and piloting the Indonesian version. L. D. contributed to datasets AUUOA. R. D. V. contributed to datasets BR, was involved in translating and piloting the Portuguese version.
S. D. contributed to datasets INJAI and INHYD. S. D. -A. contributed to datasets ESUSE, was involved in translating and piloting the Spanish (Spain) version. E. D. contributed to datasets PLPWR, was involved in translating and piloting the Polish version. L. P. E. contributed to data preparation and checks. J. F. -A. contributed to datasets ESUSE. M. F. contributed to datasets NLVUA. A. F. contributed to datasets NLVUA. G. G. contributed to datasets EC. V. G. contributed to datasets INJAI and INHYD. S. G. contributed to datasets GBUOS and GBUCL, was involved in preparing and piloting the English version and developments of methods. S. G. J. contributed to datasets IRTIA. D. H. contributed to datasets MYUMS, was involved in translating and piloting the Malaysian version. R. T. H. contributed to datasets DEHSA, was involved in preparing the English version, translating and piloting the German version and development of methods.
G. M. H. contributed to datasets GBUOS and GBUCL, was involved in preparing and piloting the English version and developments of methods. Q. J. contributed to datasets SECTH05 and 06. M. J. contributed to datasets GBCOV. R. K. contributed data preparation and checks. J. Ki. contributed to datasets AUSYD, was involved in preparing and piloting the English version and developments of methods. N. K. contributed to datasets AEKUS. B. K. contributed to datasets NLVUA. M. D. K. contributed to datasets IDITB. J. Ko. contributed to datasets DKDTU, was involved in translating and piloting the Danish version. S. K. contributed to datasets INJAI and INHYD. A. K. contributed to datasets USUOR. R. L. contributed to datasets BR. M. L. contributed to datasets PLPWR, was involved in translating and piloting the Polish version.
M. C. J. L. contributed to datasets TWNUT, was involved in preparing the English version, translating and piloting the traditional Chinese version and development of methods. Y. L. contributed to datasets KRYON. V. L. contributed to datasets DEHSA. M. M. contributed to datasets IRKER. U. M. -O. contributed to datasets NGIMS. L. M. -R. contributed to datasets CLUBB, was involved in translating and piloting the Spanish (Chile) version. A. M. contributed to datasets DEUKL. F. M. contributed to datasets ITPBA, was involved in translating and piloting the Italian version. J. M. contributed to datasets INJAI and INHYD. G. M. contributed to datasets UKGSA. I. M-R. contributed to datasets EC, was involved in translating and piloting the Spanish (Ecuador) version. D. M. contributed to datasets CNTHU, was involved in preparing the English version, translating and piloting the simplified Chinese version and development of methods.
B. M. contributed to datasets FRCER, was involved in translating and piloting the French version. M. N. contributed to datasets USUCB. E. N. contributed to datasets HKCHU. M. Ok. contributed to datasets NGIMS. M. Ol. contributed to datasets UGNKZ. W. O. contributed to datasets HKCHU. A. L. P. A. contributed to datasets BR. A. P. -F. contributed to datasets CLUBB, was involved in translating and piloting the Spanish (Chile) version. I. R. contributed to datasets LKUOM, was involved in translating and piloting the Sinhalese version. G. R. contributed to datasets BR, was involved in translating and piloting the Portuguese version. S. R. contributed to datasets IQUOB, IQTUB and IQNAH, was involved in translating and piloting the Arabic version. C. F. R. contributed to datasets USMIT. M. I. R. contributed to datasets USUOR.
M. Sa. contributed to datasets IRKER. K. S. -E. contributed to datasets DEKIT, was involved in preparing the English version, translating and piloting the German version and development of methods. S. Sch. contributed to datasets USUCB. S. Sh contributed to datasets AURMI. M. Shk. contributed to datasets JPTCU, was involved in translating and piloting the Japanese version. V. S. contributed to datasets USUCB. S. Su. contributed to datasets IDITB was involved in translating and piloting the Indonesian version. M. Ta. contributed to datasets IRSBU. F. T. contributed to datasets AUUOW. D. T. contributed to datasets SECTH01 to 04, was involved in preparing the English version, translating and piloting the Swedish version and development of methods. P. T. contributed to datasets INJAI and INHYD.
S. T. contributed to datasets INSAL. M. Tr. contributed to datasets CLUBB, was involved in translating and piloting the Spanish (Chile) version. J. T. contributed to datasets DEUKL. R. B. T. contributed to datasets NGABU. C. V. contributed to datasets DEBUW. Y. Y. contributed to datasets HKCHU. L. Y. contributed to datasets CNXIA. G. Z-L. contributed to datasets GBCAR. Y. Zhai. contributed to datasets CNXIA. Y. Zhu. contributed to datasets CNTHU, was involved in translating and piloting the simplified Chinese version. Z. Z. contributed to datasets IRSBU and IRTIA, was involved in translating and piloting the Farsi version.
acknowledgements This project was conducted under the framework of IEA EBC Annex 69. M. Sch. , S. B. , and K. S-E. acknowledge funding by the  Heidelberg Academy of Sciences and Humanities  within the project " Thermal comfort and pain ". M. Sch. is thankful to  Prof. F. Nicol  to share the information on participation via NCEUB,  Prof. A. Wagner ,  Prof. G. Vrachliotis , and  Dr. M. Krause  from  Karlsruhe Institute of Technology  for their support in distributing questionnaires. A. A. -Z. would like to thank all the students from  University of Technology-Iraq/Mechanical Engineering Department , who participated by providing answers to the questionnaire. F. A. -A. and the research related to Jordan was supported by  M. Sch. H. A. -K. is thankful to  Dalal Al-Khatri ,  Noor Alhuda Al-Saqri , and  Maryam Al-Bartamani  for their contribution in distributing the questionnaires.
E. Am. is thankful to  Eleni Alexandrou  of the  National Technical University of Athens  for her contribution in distributing the questionnaires. B. C. is thankful to the  National Natural Science Foundation of China  (No. 51521005  and No. 51678330 ). C. C. is thankful to  National Research Foundation of Korea  (No. NRF-2017R1A2B4012122 ) J-H. C. would like to express gratitude to the  USC  students who volunteered for the data collection, and a special thanks to  Ms. Zhihe Wang , who provided a technical assistance to compile the acquired dataset. S. D. -A. is thankful for the funding support of the TEP-130 R&D group from the  US. L. P. E. would like to thank  IITM, India  for the support and  DAAD  for the scholarship. G. G. is thankful to  Andrés Chico ,  Freddy Ordoñez ,  Jesús López  ( Escuela Politécnica Nacional -EPN ),  Ricardo Narváez  ( Universidad Central del Ecuador ),  Guillermo Soriano  ( Escuela Superior Politécnica del Litoral -ESPOL ),  Daniel Quiroz  ( Universidad Regional Amazónica Ikiam ) and  Stalin Guamán  ( Universidad Regional Amazónica Ikiam ).
I. R. is thankful to first year Architecture students of  University of Moratuwa  who volunteered for the data collection. M. Ok. would want to thank all the students from  Imo State University, Owerri and Federal Polytechnic, Nekede  who participated by providing answers to the questionnaire. M. Ol. would like to thank  Tadeo Nedala  and  Menelik Tibikabire  who helped administer the questionnaires. W. O. would like to thank  Prof Bruce Lonnman  for his support and all CUHK students involved. The study is supported by  General Research Fund, Research Grant Council ,  Hong Kong  (Project code:  14629516 ) and  Vice-Chancellor's One-off Discretionary Fund of the Chinese University of Hong Kong. A. L. P. A. would like to thank the Civil Engineering students of IFSC who volunteered for the data collection.
code availability R software  26  was used for all steps requiring data manipulation described above in section Data preparation and below in section Technical validation. Custom code was developed for these steps and is made available (see Code availability below). The custom code consists of three main scripts with additional functions loaded when running the scripts. The first script (ScalesSurv_00_DataPreparationAndChecks. r) contains the steps for initial data screening and calculating additional variables for individual datasets. The second script (ScalesSurv_01_LoadAllDatasets_compl_final. R) combines individual datasets and harmonizes some factor levels, e. g. changing all season descriptors "fall" to "autumn" for consistency with other descriptors. The third script (ScalesSurv_02_Prepare_Data_final.
R) is used for additional data preparations, such as additional harmonisations and adjustments to individual data points based on the results from technical validation. All R scripts used for validating incoming data, acquiring climate classifications, and calculating additional variables are available on OSF  23. The R version used for processing the data was 3. 5. 2 (2019-04-23). The packages used for data preparation and validation were: knitr, gridExtra, grid, ggplot2, pander, xtable, png, kableExtra, reshape2, SparseM, plyr, data. table.
In detail, PaPRIKa stands for the Protection of Aquifers incorporating four criteria: P for protection (considering the most protective aspects among parameters related to soil cover, unsaturated zone, and epikarst behaviour), R for rock type, I for infiltration, and Ka for karstification degree  [12]. The novel plugin for Quantum Geographic Information System (QGIS) software  [13]  makes it possible to apply the PaPRIKa method to karst aquifer vulnerability mapping in a standardised way. Developed within an open-source environment, the toolbox provides a clear workflow to establish vulnerability maps. In this study, the QGIS toolbox was applied by conducting vulnerability mapping on the Fontaine de Vaucluse catchment area (France) which is the largest European karst hydrosystem. We highlight both the advantages of the PaPRIKa method and the choices available to hydrogeologists by using the QGIS toolbox, as well as the challenges and the limitations of the method.
The ATTRACT protein-protein docking program has been employed to predict protein-protein complex structures in CAPRI rounds 38-45. For 11 out of 16 targets acceptable or better quality solutions have been submitted (~70%). It includes also several cases of peptide-protein docking and the successful prediction of the geometry of carbohydrate-protein interactions. The option of combining rigid body minimization and simultaneous optimization in collective degrees of freedom based on elastic network modes was employed and systematically evaluated. Application to a large benchmark set indicates a modest improvement in docking performance compared to rigid docking. Possible further improvements of the docking approach in particular at the scoring and the flexible refinement steps are discussed.
| INTRODUCTION The interaction of proteins to form functional complexes is a fundamental property of all living systems. A major goal of structural biology is to obtain structural insight into all relevant protein-protein complexes of living systems. Although the structures of many proteinprotein complexes have been determined experimentally there is still a demand for docking methods to predict entirely new complex structures. For many cases, it is possible to find a homologous complex in the database of known complexes to generate a template-based model. 1  However, in particular for transient protein-protein interactions or low-affinity complexes it is still difficult to determine the complex structure experimentally or based on a homologous template. ] [8] [9] [10] [11]  In ATTRACT, an atomistic or coarse-grained (CG) protein model can be employed for predicting the targets of CAPRI rounds 38-45.
The standard soft degrees of freedom corresponded to eigenvectors of the proteins calculated using an ENM developed by Hinsen  22  related to Anisotropic ENMs. 23 In our implementation, the normal modes were calculated with respect to only the protein backbone (Cα atoms). This results in collective degrees of freedom that allow backbone loop motions and/or domain-domain motions. Each side chain (bound to a Cα atom) performs exactly the same (translation) movement as the corresponding Cα atom. Hence, the complete side chain moves (in translation) as a rigid body following the motion of the backbone. The method was implemented in a GPU-version of the ATTRACT program in order to achieve high computational efficiency. Note, that this includes only the search (energy minimization) along rigid degrees of freedom and along the normal mode directions (starting from thousands of initial placements) but not the Hessian matrix diagonalization to calculate the normal modes.
A structure-based force field is determined on-the-fly (for each complex) to evaluate intraprotein interactions for the flexible interface part. 24 e AMBER16 molecular dynamics (MD) package  28  was used for atomistic refinement MD simulations using an implicit solvent description. The structures were converted to the AMBER atom type description using the pdb4amber tool. A Generalized-Born (GB) implicit solvent model (igb = 8) was used with the newest version of the AMBER force field ff14SB. 29 The structures were first minimized with the sander program (1000 steepest descent steps) with a short cutoff (cut = 9. 0) and a small initial step size (dx0 = 0. 0000001) to relax possible atom overlap and deformations. Then two short MD simulations were run with the pmemd. cuda program of the AMBER package for 5000 and 2500 time steps (2 fs) at temperatures T = 400 K and T = 350 K, respectively.
During the MD simulations, intramolecular distances and intermolecular distances between backbone atoms were restrained to prevent large deformations and dissociation of the binding partners. Finally, the structures were minimized for 5000 steps with a large cutoff (cut-off-radius = 9999. 0 Å) to include all pairwise interactions using the pmemd. cuda program without restraints. The force field energy was evaluated for the complex and the individual protein partners by the sander program. The binding interaction energy score was calculated by subtracting the energy of the free protein partners from the energy of the complex. The final models were ranked by their binding interaction energy score without using any reweighting scheme of the energy contributions. In case of peptide-protein complexes of round 44, we also performed in some cases a refinement protocol that involved explicit solvent simulations by all-atomic MD simulations using CHARMM36 force field  30  for protein and TIP3P model  31  for water.
| Molecular docking of carbohydrate-protein complexes For the docking and evaluation of carbohydrate-protein complexes (Capri round 41), we applied Autodock 3  33  to perform molecular docking calculations because at the time of the Capri challenge no carbohydrate specific force field was available in ATTRACT. Receptor structures were prepared by the Autodock Tools 34 after parameterization with the Xleap module of AMBER 16 using ff99SB force field parameters. 35 The internal charges from Autodock 3 were assigned, while nonpolar hydrogen atoms were merged on corresponding carbon atoms using a default procedure. Ligands were prepared using the Xleap module of AMBER 16 with GLYCAM06 parameters. 36 For oligosaccharide ligands, the charges assigned in this procedure were kept unaltered in the docking calculation.
After the minimization, a system was heated up to 300 K for 10 ps with harmonic force restraints on solute (10 kcalÁmol -1 Å -2 ), equilibrated for 100 ps at 300 K and 10 5 Pa in isothermal isobaric ensemble (NTP). This was followed by a 10 ns MD production run in the same NTP ensemble. The SHAKE algorithm, 2 fs time integration, 8 Å cutoff for nonbonded interactions and the Particle Mesh Ewald method were used. The trajectories were analyzed using the cpptraj module of AMBER Tools.