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(in preparation) of 12 measurement stations in Rovaniemi.\n\nTechnical Info:\n\nNB: update on 8.5.2026, file Rovaniemi\\_202604.dat added.\n\nThe measurements were conducted at a height of 3 m in the locations listed in Rovaniemi\\_Table1.txt during 3.6.2025-30.4.2026.\n\nEach measurement station consisted of three parts: a temperature and humidity sensor, a solar radiation shield, and an Internet of Things (IoT) device, which collected the measurement data and communicated them to a server via Long Range Wide Area Network (LoRaWAN). Temperature and relative humidity (RH) were measured by one integrated sensor, the Digital Matter I2C Temperature and Humidity Sensor [https://www.digitalmatter.com/wp-content/uploads/2020/09/I2C-Temperature-and-Humidity-Sensor-Datasheet.pdf]. Within the sensor, the temperature and RH were measured using the Silicon Labs Si7021-A20 I2C Humidity and Temperature Sensor chip. The chip is factory calibrated and has maximum operating ranges of 0% to 100% RH and -40°C to +125°C temperature. The measurement accuracy for temperature is maximum ±0.4°C if the ambient temperature is between -10°C and 85°C. The measurement accuracy of the chip is maximum ±3% RH if the ambient RH is between 0% and 80%. The Temperature and Humidity Sensor was protected by a radiation shield to minimize the influence of direct sunlight and thermal radiation on the measurements. The radiation shield (height 11.5 cm, radius 14 cm) was made of white plastic and consisted of 9 ventilated plates stacked in a cylindrical design allowing for adequate airflow while shielding the sensor from external radiation.\n\nQuality check\n\nThe temperature data was quality checked using a multi-step procedure. First, values were screened based on long-term climatological daily minimum and maximum temperatures derived from 10 km × 10 km resolution gridded temperature data for the Tampere region (Aalto et al., 2016). Measurements falling clearly outside the climatological range were removed. Subsequently, remaining values were filtered based on statistical properties of the measurements, using median and median absolute deviation (MAD) over short time intervals to identify and remove outliers. A final threshold based on deviations from the local median was applied to exclude any remaining extreme values.\n\nThe Local Climate Zones (LCZs) in Rovaniemi\\_Table1\\_New. have been defined for each measurement station following the Global LCZ data (Demuzere 2022a, Demuzere, et al. 2022b) based on the Local Climate Zone (LCZ) Classification system by Stewart and Oke (2012).\n\nTable Of Contents: The descriptions of the measurement stations are in Rovaniemi\\_Table1.txt.   \nColumns   \n1. Station\\_code   \n2. Station\\_id   \n3. latitude   \n4. longitude   \n5. elevation above mean se alevel (m)   \n6. LCZ\\_global\\_point (LCZ at the grid point nearest to the measurement station)   \n7. LCZ\\_global\\_r200 (Mode of the LCZs within a 200-meter radius around the measurement station)\n\nThe data (hourly Temperature) of each the measurements are in ASCII (tabulator as separator) files Rovaniemi\\_YearMonth.dat, with Celsius as Unit and missing value -999.   \nColumns:   \n1. Station\\_code   \n2. Timestamp(YMDHH24) UTC   \n3. Mean temperature of the previous hour   \n4. Minimum temperature of the previous hour   \n5. Maximum temperature of the previous hour   \n6. Standard deviation of the temperature measurements during the previous hour   \n7. Number of measurements during the previous hour (usually 12)\n\nReferences: Aalto, J., Pirinen, P., & Jylhä, K. (2016). New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. Journal of Geophysical Research: Atmospheres, 121(8), 3807–3823. https://doi.org/10.1002/2015JD024651   \nStewart ID, Oke TR. Local Climate Zones for Urban Temperature Studies. Bull Am Meteorol Soc. 2012;93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1 Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022a): A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835-3873, https://doi.org/10.5194/essd-14-3835-2022.   \nDemuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022b): Global map of Local Climate Zones. 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(in preparation) of 23 measurement stations in Tampere.\n\nTechnical Info:\n\nNB: Update on 8.5.2026: data file Tampere\\_202604.dat added.\n\nThe measurements were conducted at a height of 3 m in the locations listed in Tampere\\_Table1\\_New.txt during 07/2023-4/2026.\n\nEach measurement station consisted of three parts: a temperature and humidity sensor, a solar radiation shield, and an Internet of Things (IoT) device, which collected the measurement data and communicated them to a server via Long Range Wide Area Network (LoRaWAN). Temperature and relative humidity (RH) were measured by one integrated sensor, the Digital Matter I2C Temperature and Humidity Sensor [https://www.digitalmatter.com/wp-content/uploads/2020/09/I2C-Temperature-and-Humidity-Sensor-Datasheet.pdf]. Within the sensor, the temperature and RH were measured using the Silicon Labs Si7021-A20 I2C Humidity and Temperature Sensor chip. The chip is factory calibrated and has maximum operating ranges of 0% to 100% RH and -40°C to +125°C temperature. The measurement accuracy for temperature is maximum ±0.4°C if the ambient temperature is between -10°C and 85°C. The measurement accuracy of the chip is maximum ±3% RH if the ambient RH is between 0% and 80%. The Temperature and Humidity Sensor was protected by a radiation shield to minimize the influence of direct sunlight and thermal radiation on the measurements. The radiation shield (height 11.5 cm, radius 14 cm) was made of white plastic and consisted of 9 ventilated plates stacked in a cylindrical design allowing for adequate airflow while shielding the sensor from external radiation.\n\nQuality check\n\nThe temperature data was quality checked using a multi-step procedure. First, values were screened based on long-term climatological daily minimum and maximum temperatures derived from 10 km × 10 km resolution gridded temperature data for the Tampere region (Aalto et al., 2016). Measurements falling clearly outside the climatological range were removed. Subsequently, remaining values were filtered based on statistical properties of the measurements, using median and median absolute deviation (MAD) over short time intervals to identify and remove outliers. A final threshold based on deviations from the local median was applied to exclude any remaining extreme values.\n\nThe Local Climate Zones (LCZs) in Table 1. have been defined for each measurement station following the Global LCZ data (Demuzere 2022a, Demuzere, et al. 2022b) based on the Local Climate Zone (LCZ) Classification system by Stewart and Oke (2012).\n\nTable Of Contents:   \nThe descriptions of the measurement stations are in Tampere\\_Table1\\_New.txt.   \nColumns   \n1. Station\\_code   \n2. Station\\_id   \n3. latitude   \n4. longitude   \n5. elevation above mean se alevel (m)   \n6. LCZ\\_global\\_point (LCZ at the grid point nearest to the measurement station)   \n7. LCZ\\_global\\_r200 (Mode of the LCZs within a 200-meter radius around the measurement station)   \n  \nThe data (hourly Temperature) of each the measurements are in ASCII (tabulator as separator) files Tampere\\_YEAR.dat, with Celsius as Unit and missing value -999.   \nColumns:   \n1. Station\\_code   \n2. Timestamp(YMDHH24) UTC   \n3. Mean temperature of the previous hour   \n4. Minimum temperature of the previous hour   \n5. Maximum temperature of the previous hour   \n6. Standard deviation of the temperature measurements during the previous hour   \n7. Number of measurements during the previous hour (usually 12)   \n  \nReferences:\n\nAalto, J., Pirinen, P., & Jylhä, K. (2016). New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. Journal of Geophysical Research: Atmospheres, 121(8), 3807–3823. https://doi.org/10.1002/2015JD024651 Stewart ID, Oke TR. Local Climate Zones for Urban Temperature Studies. Bull Am Meteorol Soc. 2012;93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1   \nDemuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022a): A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835-3873, https://doi.org/10.5194/essd-14-3835-2022.   \nDemuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022b): Global map of Local Climate Zones. 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First, values were screened based on long-term climatological daily minimum and maximum temperatures derived from 10 km × 10 km resolution gridded temperature data for the Tampere region (Aalto et al., 2016). Measurements falling clearly outside the climatological range were removed. Subsequently, remaining values were filtered based on statistical properties of the measurements, using median and median absolute deviation (MAD) over short time intervals to identify and remove outliers. A final threshold based on deviations from the local median was applied to exclude any remaining extreme values.\n\nThe Local Climate Zones (LCZs) in Helsinki\\_Table1\\_New. have been defined for each measurement station following the Global LCZ data (Demuzere 2022a, Demuzere, et al. 2022b) based on the Local Climate Zone (LCZ) Classification system by Stewart and Oke (2012).\n\nTable Of Contents:\n\nThe descriptions of the measurement stations are in Helsinki\\_Table1\\_New.txt.   \nColumns:   \n1. Station\\_code   \n2. Station\\_id   \n3. latitude   \n4. longitude   \n5. elevation above mean se alevel (m)   \n6. LCZ\\_global\\_point (LCZ at the grid point nearest to the measurement station)   \n7. LCZ\\_global\\_r200 (Mode of the LCZs within a 200-meter radius around the measurement station)\n\nThe data (hourly Temperature) of each the measurements are in ASCII (tabulator as separator) files Helsinki\\_YEAR.dat, with Celsius as Unit and missing value -999.   \nColumns:   \n1. Station\\_code   \n2. Timestamp(YMDHH24) UTC   \n3. Mean temperature of the previous hour   \n4. Minimum temperature of the previous hour   \n5. Maximum temperature of the previous hour   \n6. Standard deviation of the temperature measurements during the previous hour   \n7. Number of measurements during the previous hour (usually 12)   \n  \nReferences:\n\nAalto, J., Pirinen, P., & Jylhä, K. (2016). 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The data support the manuscript by Laaksonen et al., *\"Cloud condensation experiments probe nanoscale effects on water vapor adsorption by insoluble particles.\"*\n\nThe investigated metal oxides are: Al2O3 (sample #1 and #2), Fe2O3, SnO2, TiO2, NiO, PbO, and CuO.\n\nThe investigated minerals are: K-feldspar, kaolinite, kaolin, and illite.\n\nThe cloud condensation data provide critical supersaturation values as a function of particle size. The adsorption data include water vapor adsorption isotherms and nitrogen adsorption isotherms measured for the same materials. In addition, the dataset includes model output files: the f(X,N,B) correction factor as a function of number of adsorbed water monolayers for selected particle sizes and FHH B parameter values; a comparison table of experimental and modelled critical supersaturation values with their ratios across the full diameter range for all 12 substances; the critical supersaturation as a function of number of adsorbed layers at activation for four FHH correction methods; and the number of adsorbed layers at activation as a function of particle diameter for all 12 substances.\n\n**CONTENTS**\n\n**1. Cloud condensation activation measurements**\n\nFolder: **CCN\\_activation\\_data.zip**\n\nThis dataset contains cloud condensation nuclei activation data reporting the critical supersaturation (S\\*) at which 50% of particles activate as cloud droplets as a function of dry particle diameter.\n\nEach file corresponds to a specific material  `*_AAC_50.txt` and contains:\n\n* **Dp**: Dry particle diameter (nm)\n* **dDp**: Uncertainty in dry particle diameter (nm)\n* **S\\*:** Critical supersaturation at 50% activation. (P/P0 -1)\n* **dS:** Measurement uncertainty in S\\*\n\n**2. Water and Nitrogen adsorption isotherm measurements**\n\nFolders: **H2O isotherms.zip** and **N2\\_isotherms.zip**\n\nThe dataset includes nitrogen adsorption isotherms (adsorption branch, `*_N2_N_ad.dat`) and water adsorption isotherms (desorption branch, `*_H2O_N_de.dat`). Each file contains a header with references to the original experimental filenames and data in two columns:\n\n* **P/P0**: Saturation ratio\n* **N\\_ads  (N\\_des)**: Number of adsorbed water or nitrogen monolayers in the adsorption (desorption) branch\n\n**3. MODEL OUTPUT AND COMPARISON DATA**\n\n**3.1 SSw\\_comparison\\_table.csv**\n\nData for Figures 1 and 3a.\n\nCritical supersaturation (SSw, %) for all 12 substances across a particle diameter grid of 40-1000 nm.\n\nContains experimental SSw values with uncertainties, model estimates from three FHH-based parameterisations (original, size-corrected, and size+fractal-corrected), the Kelvin-equation reference curve, and the ratios of modelled to experimental SSw. Rows where no experimental measurement exists at a given diameter contain model values only.\n\n* **Substance**: sample identifier\n* **Dp (nm)**: particle diameter (nm)\n* **dDp (nm)**: uncertainty in particle diameter (nm)\n* **SSw\\_exp**: experimental critical supersaturation (%)\n* **dSSw\\_exp**: uncertainty in experimental SSw (%)\n* **SSw\\_kelvin**: Kelvin-equation SSw (%) — pure water limit\n* **Ssw\\_FHH**: modelled SSw (%) — FHH original\n* **SSw\\_FHH(D)**: modelled SSw (%) — FHH with size correction\n* **SSw\\_FHH(D,f)**: modelled SSw (%) — FHH with size and fractal correction\n* **ratio\\_SSw\\_FHH**: SSw\\_FHH / SSw\\_exp\n* **ratio\\_SSw\\_FHH(D)**: SSw\\_FHH(D) / SSw\\_exp\n* **ratio\\_SSw\\_FHH(D,f)**: SSw\\_FHH(D,f) / SSw\\_exp\n* **data\\_type**: \"experimental + model\" or \"model only\"\n\n**3.2 fXNB\\_data.csv**\n\nData for Figure 2.\n\nTheoretical f(X,N,B) correction factor as a function of number of adsorbed water monolayers N (range 1-2000), computed for three particle diameters (10, 100, 1000 nm) and three FHH B parameter values (1.01, 2, 3).\n\n* **Diameter\\_nm** : dry particle diameter (nm)\n* **B\\_value**: FHH B parameter\n* **N\\_mono**: number of adsorbed water monolayers\n* **fXNB**: f(X,N,B) correction factor (dimensionless, 0-1)\n\n**3.3 SSw\\_vs\\_N.csv**\n\nData for Figure 3b.\n\nCritical supersaturation SSw (%) as a function of number of adsorbed water layers N at activation, for four correction methods applied to K-Feldspar (diameter = 300 nm, A = 3.46, B = 2.14, B\\_corr = 1.06). N range: 10-10000.\n\n* **Method**: correction method (Uncorrected, Size\\_corrected, Fractal\\_corrected, Size\\_and\\_fractal\\_corrected)\n* **N**: number of adsorbed water monolayers at activation\n* **SSw**: critical supersaturation (%)\n\n**3.4 N\\_vs\\_diameter.csv**\n\nData for Figure 3c.\n\nNumber of adsorbed water layers N at CCN activation as a function of particle diameter, for all 12 metal oxide and mineral substances. Diameter range: 40-1000 nm. 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Interviewees were also asked, among other things, to reflect on gender roles in student association activities, their political or non-political character, and their relationship to wider society. In addition, interviewees were asked to share their views on the activities and history of the national umbrella organization of student associations, the Finnish Union of Secondary School Students. Finally, interviewees were asked to summarize the significance that student association activities had had for their personal lives and, more broadly, for Finnish secondary schooling. Four of the interviews were conducted as paired interviews, with the remainder being individual interviews. In total, 30 individuals participated in the interviews, of whom 17 were men and 13 women. 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Haastateltavien taustatietoja ovat sukupuoli, syntymävuosi, haastatteluajankohta sekä tieto siitä, mikä rooli haastateltavalla oli teinikunnassa."},"field_of_science":[{"id":"2787a348-6c0f-4d0c-9238-e6c78d62d89f","url":"http://www.yso.fi/onto/okm-tieteenala/ta6","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Humanities","fi":"HUMANISTISET TIETEET","sv":"Humanistiska vetenskaper"}}],"infrastructure":[],"issued":"2026-06-10","keyword":["muistelu","koululaiset","muistitieto","nuoruus","oppikoulu","poliittiset opiskelijajärjestöt","opiskelijajärjestöt","poliittinen osallistuminen","poliittinen toiminta","opiskelijaliikkeet","schoolchildren","students","youth","student organizations","student unions","political participation","student participation","life histories","secondary education","upper secondary schools","Historia","Muu 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The dataset examines ethical guidelines, practices, violations, and the values and operating culture of research work. Respondents were asked about their organisation's commitment to the RCR guidelines, their familiarity with various research ethics guidelines, the visibility of research ethics support persons, and their participation in research ethics training. Violations of the RCR were explored by assessing the prevalence of different forms of unethical conduct within respondents' research communities over the past three years. These included, for example, plagiarism, self-plagiarism, fabrication, falsification of results, and neglect related to authorship. In addition, respondents were asked about failures to obtain research permits or ethical review. They were also asked about their own experiences of being subjected to violations and how such cases had been handled within their organisations. Ethical risks and threats to responsible conduct were examined through statements addressing issues such as lack of information on researchers' rights, ambiguities in the ownership of research data, consequences of suspected misconduct, career pressures, the influence of funding, and awareness of RCR processes. The values and ethical principles of research work were examined by asking respondents to identify the most important values, such as openness, honesty, reliability, independence, critical thinking, and societal impact. In addition, views on the ethical operating culture and leadership of the work community were surveyed. Background variables in the dataset include the type of the respondent's organisation (university, university of applied sciences, or other research organisation), highest degree obtained, country where the degree was completed (classified), main work content (research, teaching, administration, or other), field of science, and years of research experience.","fi":"Tutkimusetiikan barometri 2018 kartoittaa suomalaisen tiedeyhteisön näkemyksiä tutkimusetiikasta, hyvän tieteellisen käytännön (HTK) noudattamisesta sekä tutkimustyöhön liittyvistä eettisistä haasteista ja ongelmatilanteista vuosina 2016-2018. Aineisto tarkastelee tutkimusetiikan ohjeistusta, käytäntöjä, loukkauksia sekä tutkimustyön arvoja ja toimintakulttuuria. Vastaajilta tiedusteltiin organisaation sitoutumista hyvän tieteellisen käytännön ohjeisiin, eri tutkimuseettisten ohjeistojen tuntemusta, tutkimusetiikan tukihenkilötoiminnan tunnettuutta sekä osallistumista tutkimusetiikan koulutuksiin. Hyvän tieteellisen käytännön loukkauksia kartoitettiin arvioimalla erilaisten epäeettisten toimintatapojen esiintyvyyttä vastaajan tiedeyhteisössä viimeisten kolmen vuoden aikana. Näitä olivat esimerkiksi plagiointi, itseplagiointi, sepittäminen, tulosten vääristely ja tekijyyteen liittyvät laiminlyönnit. Lisäksi kysyttiin tutkimusluvan tai eettisen ennakkoarvioinnin laiminlyönnistä. Vastaajilta tiedusteltiin myös kokemuksia siitä, ovatko he itse joutuneet loukkausten kohteeksi ja miten tapauksiin on organisaatioissa reagoitu. Eettisiin riskeihin ja hyvän tieteellisen käytännön uhkatekijöitä kartoitettiin väittämillä, jotka koskivat esimerkiksi tutkijoiden oikeuksiin liittyvän tiedon puutetta, tutkimusaineistojen omistajuuden epäselvyyksiä, vilppiepäilyjen seurauksia, urapaineita, rahoituksen vaikutuksia sekä HTK-prosessien tunnettuutta. Tutkimustyön arvoja ja eettisiä periaatteita selvitettiin pyytämällä vastaajia valitsemaan keskeisimmät arvot, kuten avoimuus, rehellisyys, luotettavuus, riippumattomuus, kriittisyys ja vaikuttavuus. Lisäksi kartoitettiin työyhteisön eettistä toimintakulttuuria ja johtamista koskevia näkemyksiä. Taustamuuttujina aineistossa ovat vastaajan taustaorganisaation tyyppi (yliopisto, ammattikorkeakoulu, muu tutkimusorganisaatio), korkein suoritettu tutkinto, tutkinnon suoritusmaa luokiteltuna, työn pääasiallinen sisältö (tutkimus, opetus, hallinto tai muu), tieteenala, sekä tutkimuskokemus vuosina."},"field_of_science":[{"id":"09e9d38f-26f3-46af-9b47-1e4d0318c406","url":"http://www.yso.fi/onto/okm-tieteenala/ta4","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Agriculture and forestry","fi":"MAATALOUS- JA METSÄTIETEET","sv":"Jordbruks- och skogsvetenskaper"}},{"id":"2787a348-6c0f-4d0c-9238-e6c78d62d89f","url":"http://www.yso.fi/onto/okm-tieteenala/ta6","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Humanities","fi":"HUMANISTISET TIETEET","sv":"Humanistiska 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The survey explores sources of research ethics information, familiarity with guidelines, and misconduct and related suspicions. Respondents were asked how well they are familiar with key RCR guidelines (e.g. the RCR guidelines, authorship practices, researcher's curriculum vitae guidelines, and The European Code of Conduct for Research Integrity) as well as the research ethics support person system within their organisations. In addition, respondents were asked about the various sources (e.g. organisation, funders, publications, the scientific community, media) from which they have obtained information on research ethics. The section on RCR competence examined the availability of research ethics training, participation in such training, and respondents' self-assessed level of competence. Respondents were also asked to evaluate the adequacy of their competence and their need for further training. Respondents were asked how often they had suspected research misconduct (e.g. plagiarism, fabrication, falsification of observations or results) and disregard for the responsible conduct of research (e.g. neglect of research permits, deficiencies in data handling, authorship disputes, self-plagiarism, exaggeration of merits). This was followed by questions on respondents' knowledge of reporting processes, experiences of making allegations, and barriers to reporting, such as loyalty, fear of consequences, or distrust in the fairness of procedures. Finally, the survey examined the effects of the COVID-19 pandemic on compliance with the responsible conduct of research. Background variables include respondents' research experience (in years), highest degree obtained, field of education (by disciplinary classification), country where the highest degree was completed, employing organisation, main work tasks, and the field of science of the respondent's current position or research.","fi":"Tutkimusetiikan barometri 2023 kartoittaa hyvän tieteellisen käytännön (HTK) toteutumista suomalaisessa tiede- ja tutkimusyhteisössä vuosina 2019-2022. Kyselyssä selvitettiin tutkimuseettisen tiedon lähteitä ja ohjeistuksen tuntemusta sekä hyvän tieteellisen käytännön vastaista toimintaa ja siihen liittyviä epäilyjä. Vastaajilta tiedusteltiin, kuinka hyvin he tuntevat hyvän tieteellisen käytännön keskeisiä ohjeita (esim. HTK-ohje, tekijyyskäytännöt, ansioluettelomallit, The European Code of Conduct for Research Integrity) sekä organisaatioiden tutkimusetiikan tukihenkilötoimintaa. Lisäksi kartoitettiin, mistä eri lähteistä (esim. organisaatio, rahoittajat, julkaisut, tiedeyhteisö, media) vastaajat ovat saaneet tietoa tutkimusetiikasta. Hyvän tieteellisen käytännön osaamista koskevassa osiossa tarkasteltiin tutkimuseettisen koulutuksen tarjontaa, siihen osallistumista sekä omaa osaamisen tasoa. Vastaajilta kysyttiin arviota osaamisensa riittävyydestä ja lisäkoulutustarpeista. Vastaajilta kysyttiin, kuinka usein he ovat epäilleet tiedevilppiä (esim. plagiointi, sepittäminen, havaintojen tai tulosten vääristely) sekä piittaamattomuutta hyvästä tieteellisestä käytännöstä (esim. tutkimuslupien laiminlyönti, aineiston käsittelyn puutteet, tekijyysepäselvyydet, itsensä plagiointi, ansioiden liioittelu). Seuraavaksi selvitettiin vastaajien tietämystä ilmoitusprosesseista, kokemuksia epäilyjen tekemisestä sekä ilmoittamisen esteitä, kuten lojaalisuus, pelko seurauksista tai epäluottamus prosessien oikeudenmukaisuuteen. Lopuksi kartoitettiin koronapandemian vaikutuksia hyvän tieteellisen käytännön noudattamiseen. Taustamuuttujina ovat vastaajan tutkimuskokemus (vuosina), korkein suoritettu tutkinto, koulutusala (tieteenalaluokitus), korkeimman tutkinnon suoritusmaa, työnantajorganisaatio, pääasiallinen työtehtävä sekä nykyisen työtehtävän tai tutkimuksen tieteenala."},"field_of_science":[{"id":"09e9d38f-26f3-46af-9b47-1e4d0318c406","url":"http://www.yso.fi/onto/okm-tieteenala/ta4","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Agriculture and forestry","fi":"MAATALOUS- JA METSÄTIETEET","sv":"Jordbruks- och skogsvetenskaper"}},{"id":"2787a348-6c0f-4d0c-9238-e6c78d62d89f","url":"http://www.yso.fi/onto/okm-tieteenala/ta6","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Humanities","fi":"HUMANISTISET TIETEET","sv":"Humanistiska 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