fairdataihub/poster-sentry-training-data huggingface dataset:
Looking for evidence of independent researchers in bibliographic records: an exploration Eline Vandewalle* and Camilla Lindelöw** *Eline.Vandewalle@uantwerpen.be https://orcid.org/0000-0002-9852-3373 Centre for Research and Development Monitoring (ECOOM), University of Antwerp, Belgium ** camilla.lindelow@hb.se https://orcid.org/0000-0002-3331-0940 Swedish School of Library and Information Science, University of Borås, Sweden Abstract Independent researchers or scholars have always (co-)existed with academic professionals. The term independent researcher has been used to refer to various phenomena at different times - from the historical researchers pursuing their research interests independently to today's researchers struggling to find permanent positions or funding. Bibliographical data sources offer a possibility to learn more about these researchers, but they do not provide a full picture. In this exploratory study, we compare three different data sources: Web of Science, Scopus and OpenAlex. All three sources show an increase in records associated with independent researchers, and the amount of records varies. We use OpenAlex to further explore publications by independent researchers active in the Social Sciences and Humanities (SSH). We provide an overview of the topics and the forms of open access publishing. We also explore different career trajectories based on the full publication history of independent researchers. 1. Introduction While most scholarly publications today are authored by people affiliated with research organisations, some are (co-)authored by ‘independent’ or ‘unaffiliated’ researchers. These authors are active in research but are not employed by an organisation to do research. There may be many reasons for this. The history of research has been marked by a move towards professionalisation, whereby research has been increasingly pursued within the confines of academic organisations and controlled by scientific societies. At the same time, we can still find examples of independent researchers publishing their work outside of the academic system. Independent researchers are certainly a very diverse group, from the historical cliché of the gentleman scientist, to women excluded from the academy, to people trying to continue research in an academic world of high competition and few stable job opportunities, to the professor emeritus continuing to work in retirement. Independent researchers have been described as a ‘hidden intelligentsia’ in society (Flesch, 1997). Orlans (2002, p. 12) defines ‘Independent Scholars’ as follows: ‘In various definitions, independent scholars are those unaffiliated with an academic, or with any, institution, or whose work duties do not include research and scholarship. Typically, they are Ph.D.s in the humanities, often women, who, unable to find scholarly employment ("independent," a wit says, is a euphemism for "unemployed"), pursue
Nat J EU SAL The Role of National Judges in the Enforcement of EU State Aid Law First Residential Seminar University of Murcia May 30th – 31st 2019 NatJEUSAL is an action co-funded by the European Commission, Directorate General for Competition, and the University of Padova, Department for Public Law, International and EU Law - DiPIC Main scientific partners are the Faculties of Law of the Universities of Murcia and Würzburg Further scientific partners are National University of Ireland Maynooth Department of Law, Riga Graduate School of Law, University of Zagreb Faculty of Law Session I DATE TOPIC TIME PROFESSOR 30 May Introduction 16:00 – 16:10 Juan Jorge Piernas López and Bernardo Cortese Universidad de Murcia/University of Padova 30 May Article 107(1) TFEU The notion of State aid 16.10 – 16.50 Aindrias Ó Caoimh Former Judge of the Court of Justice of the European Union 30 May Article 107(2) and (3) authorized aid 16.50 – 17:30 Delia Ferri Maynooth University BREAK 17:30 – 18:00 30 May The State Aid Tax Cases: A Practical and a Philosophical Perspective 18.00- 18.40 Vincent Power A&L Goodbody 30 May Discussion /sharing experiences 18:40 – 19:20 Marie Baker Court of Appeal, Ireland
تف مالحألا ريس وف يسفنلا ليلحتلل ديورف دنومجيس ةيرظن ق يسفنلا ليلحتلا يف ديورف دنومجيس نع تالاقم ةلسلس نمض ةروشنم ريغ ةلاقم ادادع ديئارماسلا تهزن مساب . ال تراثأ دقو ،ةيلايخ ملاوع يف عقاولا جمدني ثيح لقعلل ةضماغلا ةيعيبطلا رظانملا يه مالحأا نمز ذنم ةيرشبلا مامته سح داور نم ناك .يعواللا نم اًينغ اًجيسن مدقي امم ، فطاوعلاو زومرلاب ةجوسنم تالحر يف انلوقع عرشت ،مانن امدنع .قي ك ضرتفا .ةيرشبلا سفنلا يف ةيفخلا ايابخلا يف هتايرظن تقمعت يذلا ريهشلا يسفنلا للحملا ،ديورف دنومغيس مالحألا رارسأ فش أ ديورفن قمعأ لوح ةقيمع ىؤر مدقت ثيح ،يعواللا ىلإ يكلملا قيرطلا يه لب ،لقعلل ةيئاوشع رهاظم درجم تسيل مالحألا رغ لقعلا ةقورأ ىلإ ةلحر ةباثمب ديورف ةسدع لالخ نم مالحألا فاشكتسا حبصي .اهلح متي مل يتلا اهتاعارصو انفواخمو انتاب ا يتلا تاديقعتلا ىلع ءوضلا طيلستو ،ةضماغلتش.يرشبلا كولسلا لك نظ نم مالحالا ـب دوصقملا ام ، مالحألا نع ديورف راكفأ ىلع ءوضلا يقلن نأ ىلإ ةجاح كلانه مالحألا ةسارد ةيمهأ ىلإ ار و ديورف دنومجيس ةيرظن قفو مالحألا ريسفت " انتلاقم ؟ناسنإلا اههجاوي يتلا مالحألا عاونأ امو ، ؟ اهرسفي فيكو ، ؟هرظن ةهج ل بيجت " يسفنلا ليلحتلعلى .ةلئسألا هذه انماكلا ىوتحملاو رهاظلا ىوتحمل ا وه ملحلل رهاظلا ىوتحملا .ةيمهأ رثكأ نماك ىوتحم ىلإ مالحألل رهاظلا ىوتحملا ليوحتل مالحألا ليلحت ديورف مدختس ا نماكلا ىوتحملا ريشي نيح يف .ظاقيتسالا دنع هركذتت يذلا ملحلا يأ ،ملاحلا همدقي يذلا يعاولا فصولا وأ يحطسلا ىنعمل ملاو عفاودلا نإ .ةيعاواللا هتدام ىلإعت حضاولا ىوتحملا ةهجاو فلخ ةأبخملا ةيعاواللا تايركذلاو تاعارصلاو فطاوعلاو تادق هي .حضاو ىوتحم ىلإ ةنماكلا راكفألا لوحت اهلالخ نم يتلا ةيلمعلا وه ملحلاو .ةنماكلا مالحألا راكفأ اتاينما قيقحت مالحأل ال متيو ةحضاو نوكت تابغرلا ضعب .تاينمأ قيقحت يه اًبيرقت مالحألا عيمج نأ وه ديورف مالحأ ليلحتل يساسألا ضارتفا الت .ذيذللا ماعطلا نم ةريبك تايمك لوانتب ملحيو ًاعئاج ناسنإلا ماني امدنع لاحلا وه امك ،حضاولا نومضملا لالخ نم اهنع ريبع نع ريبعتلا متي ،كلذ عمو تحق .ةبغرلا كلت فشك هنكمي يذلا وه طقف مالحألا ريسفتو نماكلا ىوتحملا يف تابغرلا مظعم قي ي اريثك مالحألا ،يربجلا راركتلا ةدعاق مهيلع قبطتو .ةملؤم ةبرجت نم نوناعي نيذلا ىضرملا تابغرلا قيقحت ةدعاق نم ىنثتس دصلا دعب ام بارطضا نم نوناعي نيذلا صاخشألا يف دجوت امم.ةملؤم وأ ةفيخم براجتب رركتم لكشب ملحي يذلا ،ة دق تن نم ضرغلا نإف ،ديورف اقفو .)مويلا اياقب( ةمهم تاطابحإ ىلع يوطنت يتلا قباسلا مويلا تايركذ نم مالحألا قلط ال مهلكأب ملحلا لالخ نم اضرلا ضعب ىلع لصح زركلا نم ذيذل قبط لوانت نم عونمم لفط .ملاحلا تابغر قيقحت وه مالحأ ب ملحت نأ ديرت ال اهنكلو لماح ةأرماو ،اًعيمجال مالحأ مهيدل تناك ةيديلجلا ةيربلا يف نيفشكتسملا نم ةعومجمو ،ةيرهشلا ةرود
PublicaƟons  2016 Page 1 Refereed Journal Articles (published) Atygalia NW, Baldwin DS, Silvester E, Kappen P, Whitworth KL (2016) The severity of sediment desiccaƟon aﬀects the adsorpƟon characterisƟcs and speciﬁcaƟon of phosphorus. Environmental Science Processes & Impacts. 18, 64-71. DOI: 10.1039/c5em00523j Baumgartner L (2016) AdapƟve management in acƟon: using chemical marking to advance ﬁsh recovery programs in the Murray-Darling Basin. Editorial. Marine and Freshwater Research. 67 i-iii htp://dx.doi.org/10.1071/MF15353 Baldwin DS, Colloﬀ MJ, Mitrovic SM, Bond NR, Wolfenden (2016) Restoring dissolved organic carbon subsidies from ﬂoodplains to lowland river food webs: a role for environmental ﬂows? Marine and Freshwater Research 67 1387- 1399 DOI: htp://dx.doi.org/10.1071/MF15382 Crook DA, O'Mahony DJ, Gillanders BM, Munro AR, Sanger AC, Thurstan S, Baumgartner LJ (2016) ContribuƟon of stocked ﬁsh to riverine populaƟons of golden perch (Macquaria ambigua) in the Murray-Darling Basin, Australia. Marine and Freshwater Research. htp://dx.doi.org/10.1071/MF15037 Durant RA, Nielsen DL, Ward KA (2016) EvaluaƟon of Pseudoraphis spinescens (Poaceae) seed bank from Barmah Forest ﬂoodplain. Australian Journal of Botany. 64, 669-677 htp://dx.doi.org/10.1071/BT15288 Forbes J, Wats RJ, Robinson WA, Baumgartner LJ, McGuﬃe P, Cameron LM, Crook DA (2016) Assessment of sƟcking eﬀecƟveness for Murray cod (Maccullochella peelii) and golden perch (Macquaria ambigua) in rivers and impoundments of south-eastern Australia. Marine and Freshwater Research 67, 1410-1419 htp://dx.doi.org/10.1071/MF15230 Friery RF, Esuinaƫ FM, Stenert C, Arenzo A, Nielsen DL, Maltchik L (2016) Eﬀects of spaƟal scale and habitat on the diversity of diapausing wetland invertebrates. Aquatic Biology 25: 173-181 doi: 10.3354/ab00666 Harris JH, Kingsford RT, Peirson W, Baumgartner L (June 2016) MiƟgaƟng the eﬀects of barriers to freshwater ﬁsh migraƟons: the Australian experience. Marine and Freshwater Research htp://dx.doi.org/10.1071/MF15284 Harris CW, Silvester E, Rees GN, Pengelly J, Puskar L (2016) Proteins are a major component of dissolved organic nitrogen (DON) leached from terrestrially aged Eucalyptus camaldulensis leaves. Supplementary material. Environmental Chemistry 13, 877-887 doi:10.1071/EN16005_AC Hermoso V, Kennard MJ, Schmidt DJ, Bond N, Huey JA, Mon dol RK, Jamandre BW, Juhes JM (2016) Species distribuƟons represent intraspeciﬁc geneƟc diversity of freshwater ﬁsh in conservaƟon assessments. Freshwater Biology. 61: 1707-1719 doi:10.1111/fwb.12810 Karis T, Silvester E, Rees G (2016) Chemical regulaƟon of alpine headwater streams during a storm event (Bogong High Plains, Victoria, Australia) Journal of Hydrology 542: 317-329 htp://doi.org/10.1016/j.jhydrol.2016.09.014 Marshall JC, Menke N, Crook DA, Lobegeiger JS, Balcombe SR, Huey JA, Fawcet JH, Bond NR, Starkey AH, Sternberg D, Linke S, Arthington AH (2016) Go with the ﬂow: the movement behaviour of ﬁsh from isolated waterhole refugia Albury-Wodonga Campus P: 02 6024 9690 Mildura Campus P: 03 5051 4050 E:cfe@latrobe.edu.au W: latrobe.edu.au/centre-for- freshwater-ecosystems School of Life Sciences PublicaƟons 2016
1 Infectious Disease Modeling Reproducibility Checklist (IDMRC) with examples. The checklist consists of questions related to the six categories (1) computational environment; (2) analytical software; (3) model description; (4) model implementation; (5) data; and (6) experimental protocol. The center column provides examples for each category and element. 1. Computational Environment 1.1) Is the operating system documented? Examples: Microsoft Windows, macOS, Linux ▢ Yes; the operating system is documented ▢ No; the operating system is not documented ▢ Not applicable 1.2) Is the operating system version documented? Examples: Windows 8.1 (Blue) or macOS 10.12 (Sierra) ▢ Yes; the operating system version is documented ▢ No; the operating system version is not documented ▢ Not applicable 2. Analytical Software 2.1) Is the name of the analytical software documented (e.g., the programming language name)? Examples: SAS, R, STATA, Python, C++. Authors may have also used an originally developed software with a unique name. ▢ Yes; the name of the analytical software is documented ▢ No; the name of the analytical software is not documented ▢ Not applicable 2.2) Is the analytical software accessible for free? For mentioned analytical software, if the analytical software is available online for download and it is free, select "Yes." If the analytical software is available online but must be bought or licensed, select "Partially." Additionally, if the authors used multiple types of analytical software in their analyses and not all of them are available for free download, select "Partially." If the analytical software is not available online for download, select "No." Examples of analytical software available for free download: Java, R, Python; non-free examples: STATA, SAS, SPSS, MATLAB. ▢ Yes; all mentioned software is available for free download ▢ Partially; all mentioned software is available for download but requires a purchase and/or acquiring a license or not all the software used were available for free download ▢ No; the software is not available for download ▢ Not applicable 2.3) Is the version of the analytical software documented? For mentioned analytical software, if the version is documented in the publication, supplement, or in an online repository (e.g., GitHub), select "Yes." If the version is documented for some of the mentioned analytical software, select "Partially." If the version is not documented, select "No." Examples: SAS 9.4 or R version 3.3.3. ▢ Yes; the version is documented for all mentioned analytical software ▢ Partially; the version is documented for some mentioned analytical software ▢ No; the version of the analytical software is not documented ▢ Not applicable
Accessing bulk predicted QSAR data via batch search functionality in the CompTox Chemicals Dashboard Antony J. Williams1, Chris Grulke1, Todd Martin1 and Kamel Mansouri2 1Ctr. for Comput. Toxi. & Exposure, ORD, U.S. EPA; 2NICEATM, RTP, USA OBJECTIVES • Provide community access to experimental and predicted data for physicochemical properties, environmental fate and transport data and other QSAR outputs • Provide batch interface to query and download data using inputs of CAS registry numbers, chemical synonyms and DSSTox substance identifiers (DTXSIDs) • Allow for data to be intersected with multiple other data streams associated with chemicals – structure formats, formulae, masses, in vivo and in vitro toxicity data etc. APPROACH • CompTox Chemicals Dashboard [1] offers batch search (https://comptox.epa.gov/dashboard) • QSAR predicted data from data streams available: EPI Suite, ACD/Labs, OPERA [2], Toxicity Estimation Software Tool (TEST) [3] • Data include logKow, water solubility, vapor pressure, bioconcentration and bioaccumulation factors and many other properties • Batch mode delivers data in CSV, Excel and SDF format MAIN RESULTS IMPACT • Fast, simple access to data for >800,000 chemicals • QSAR predicted data available, en masse, via a batch search [4] interface, delivers access to >25 million predicted properties • Curated data sets used to create QSAR models available [5] Abstract #240 ORCID: 0000-0002-2668-4821 • Batch search CAS and Names • Select data to download • Export CSV, XLS or SDF • FAIR data available
Peptides of synthetic and microbial origin with antimicrobial and antibiofilm activity against Xylella fastidiosa Moll, L.1, Daranas, N.1, Badosa, E.1, Montesinos, E.1, Feliu, L.2, Planas, M.2, Bonaterra, A.1 1Laboratory of Plant Pathology, Institute of Food and Agricultural Technology-CIDSAV- XaRTA, University of Girona 2LIPPSO, Department of Chemistry, University of Girona
conda-lock scipy 2023 virtual poster Abstract conda-lock is a tool to designed to aid reproducible science and analysis by providing a reliable and easy to use means to ensure consistent creation of computing environments. This is not a document on how to use conda-lock, for that consult the documentation. This document covers the design consideration as well as common usage patterns. Why conda? A large number of commonly used libraries in python make entensive use of extension modules written in a different language (usually C/C++). Building these libraries has historically been challenging since the python package management tools (like pip) cannot be used to the dependencies that are needed by native compilers. Conda solves this by building both the native libraries and the python libraries that make use of these. It performs a few adjustments to the compiled artifacts to ensure that the built binaries can be more easily intalled without needing to recompile. Why conda-lock? Conda was designed as a developer facing tool, much like its more pure python sibling pip. This means that when trying to use conda in cases where reproducibility is required it has a number of short-comings that can appear from time to time which can result in inconsistent execution environments. conda-lock addresses these shortcomings by leaning on existing package management tools (conda, mamba and poetry) and the concept of a dependency lockfile (popularized by systems like npm and cargo) and allowing users to generate a lockfile that covers both conda and PyPI packages. Design principles 1. The environment created by conda lock should be consistent across all machines on a given platform Conda packages are platform-specific binaries we cannot guarantee any resolution more general than platform level. 1
Page | 1 Determinants of improved sanitation facilities in schools in Brazil: Accelerating progress towards SDG 6 with a multi-level modelling approach Kasandra I.H.M.Poague*1, Justine I.Blanford†1, Javier Martínez‡1 and Carmen Anthonj§1 1 Faculty of Geo-Information Science and Earth Observation–ITC, University of Twente, Enschede, The Netherlands January 05, 2023 Summary Understanding the factors associated with improved sanitation facilities in schools is essential for planning effective interventions, especially in locations with limited resources, such as low-and middle- income countries. Our study aimed to identify the determinants of improved sanitation facilities in schools in Brazil in 2021. For that purpose, a mixed-effect logistic model was built with two levels (schools – level 1, and municipalities – level 2). Results indicate that special attention should be devoted to improving sanitation in public schools, schools located in rural areas, in the North region, with primary education first cycle, and with a high percentage of indigenous students. KEYWORDS: SDG 6; WASH; Improved sanitation; Education; Mixed-effect logistic regression models. 1. Introduction Despite children spending a substantial portion of their daytime hours at schools, most research and sanitation interventions are devoted to the assessment of conditions and impacts of sanitation in the household environment rather than in schools (Chatterley et al., 2018; Cronk et al., 2015; Freeman et al., 2012). While children may have safe domestic sanitation, this might not be true at their schools. Supporting access to improved sanitation in schools is not a simple task, as different factors interfere with the availability of this resource. Keeping in mind that funding for schools is scarced in low-and middle-income countries (LMICs), and, even when available, water, sanitation and hygiene (WASH) might not be a priority for resources allocation, research is needed to comprehend what are the determinants of the availability of improved sanitation facilities in schools. By definition, improved sanitation facilities are those designed to hygienically separate excreta from human contact (WHO, UNICEF, 2022), and they can range from simple to complex solutions. In this study, however, only two solutions were considered. Schools were classified as having improved sanitation facilities if they disposed of their sewage into a public sewerage system or a septic tank. Knowledge about the determinants will help identify where and which type of schools are more in need of sanitation interventions to guide efficient actions and maximize their impacts. Our main objective was to identify what are the determinants of improved sanitation in schools in Brazil in 2021. * k.i.h.mingotipoague@utwente.nl † j.i.blanford@utwente.nl ‡ j.a.martinez@utwente.nl § c.anthonj@utwente.nl
C22B PRODUCTION AND REFINING OF METALS (electrolytic C25); PRETREATMENT OF RAW MATERIALS Definition statement This subclass/group covers: Metallurgical or chemical processes for producing or recovering metals from metal compounds, ores, waste or scrap metal and for refining metal. Included in this subclass are processes drawn to: the production of metal by smelting, roasting or furnace method; the extraction of metal compounds from ore and concentrates by wet processes; electrochemical treatment of ores and metallurgical products for obtaining metals or alloys; apparatus thereof; preliminary treatment of ores, concentrates and scrap; general process for refining or remelting metals; apparatus for electroslag or arc remelting of metals; obtaining specific metals; consolidating metalliferous charges or treating agents that are subsequently used in other processes of this subclass, by agglomerating, compacting, indurating or sintering. Relationship between large subject matter areas This subclass covers the treatment, e.g. decarburization, of metallferrous material for purposes of refining. C21C, C21D and C22F provide decarburization of metal for modifying the physical structure of ferrous and nonferrous metals or alloys, respectively. C22B also possesses groups for obtaining metals including obtaining metals by chemical processes, and obtaining metal compounds by metallurgical processes. Thus, for example, group C22B 11/00 covers the production of silver by reduction of ammoniacal silver oxide in solution, and group C22B 25/00 covers the production of titanium oxide by metallurgical process. Furthermore, although compounds of arsenic and antimony are classified in C01G, production of the elements themselves is covered by C22B, as well as the production of their compounds by metallurgical processes. Multi-step processes for surface treatment of metallic material involving at least one process provided for in class C23 and at least one process covered by subclass C21D or C22F or class C25 1
Salicaceae, a potential plant network for the spread of Xylella fastidiosa in temperate regions Casarin N.1, Hasbroucq S.2, Géradin A.1, Pesenti L.1, Emond A.1, Carestia G.1, Grégoire J.-C.2, Bragard C.1 1Earth&Life Institute (ELI) Applied Microbiology, UCLouvain, Louvain-la-Neuve, Belgium 2Spatial Epidemiology lab (SpELL), Université libre de Bruxelles, Brussels, Belgium
Filtering Numerical Noise Using Convolutional Autoencoders Paul Keil1,2,3, Vera Sidorenko4, Ekaterina Bagaeva4 1German Climate Computing Center DKRZ, Hamburg, Germany; ² Helmholtz AI, Germany; 3Helmholtz Center Hereon, Gesthacht, Germany; 4Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany PART 1: PROOF OF CONCEPT Problem Fluid dynamic models often produce artefacts that are related to the numerical methods used for discretization (Fig 1a). Classical approaches to filter these artefacts in a postprocessing step (like elliptic inversion and Fourier-based filtering) can be computationally expensive and falsely remove physical phenomena. Approach We use a lightweight convolutional autoencoder that learns to encode the physical characteristics and discards the numerical artefacts. In this example, the encoder and the decoder both have three convolutional layers with approximately 2000 trainable parameters. Results Noise at small scale wavelengths is filtered out while the kinetic energy at large scale wavelengths is approximately preserved (1b and 3). Using only convolutional and pooling layers without fully connected layers combats overfitting and makes the autoencoder lightweight and easy to train. Fig 1: Original zonal water velocity (a) and zonal water velocity filtered with the convolutional autoencoder (b) from an idealised baroclinic channel simulation. The channel width is 4.5 degrees. Fig 2: Frequency spectrum of kinetic energy. Blue show the raw simulation output and orange the kinetic energy calculated from water velocity that was filtered using the convolutional autoencoder.
Isolating Taiwan and Approaching China contributed to European and American Failures of Countering Covid19 and Vaccine Mandate Humanitarian Crises US CDC director Rochelle Walensky is so corrupted that she's colluding with Chinese Communist regime to serve the agenda of covid19 disinformation warfare of China. US CDC director Rochelle Walensky, African CDC director John Nkengasong and European CDC director Andrea Ammon joined online communication with China CDC director Gao Fu on 9 Nov 2021 and they agreed on global pandemic countering strategy without learning Taiwan's success.