Researchdata.se
Not a member yet
6142 research outputs found
Sort by
Mångfaldsbarometern 2005-2022: tidsseriedata om attityder till etnisk mångfald och invandring bland befolkningen i Sverige.
No full textThis dataset was generated through the Diversity Barometer, a study tracking attitudes towards ethnic diversity and immigration in Sweden since 2005. The data were collected annually between 2005 and 2014, and biennially thereafter. Unweighted samples, consisting of adults aged between 18 and 75 years were used. The data can be managed and analyzed in the statistical program SPSS. The dataset includes the following variable categories:
1. Respondent descriptives
2. Interaction with persons with foreign background at school, work and in the neighborhood.
3. Cultural rights for persons with foreign background.
4. Social rights for persons with foreign background.
5. Immigration as beneficial to the Swedish society.
6. Immigration as a threat to the Swedish society.
7. Attitudes towards Swedish immigration policies.
8. Immigrants are exploited in the Swedish labor market.
9. Interest in interacting with immigrants and learning foreign cultures.
10. Attitudes towards religion in general, and Islam in particular.
11. Willingness to live in the same neighborhood as immigrants.
12. Immigrant-neighborhoods are problem neighborhoods.Detta dataset genererades genom Mångfaldsbarometern, en studie av attityder till etnisk mångfald och invandring i Sverige sedan 2005. Data samlades in årligen mellan 2005 och 2014, och vartannat år därefter. Vuxna mellan 18 och 75 år deltog i studien. Urvalens resultat redovisades ovägt. Data kan hanteras och analyseras i SPSS. Datasetet innehåller följande variabelkategorier:
1. Beskrivning av respondenterna.
2. Umgänge med personer med utländsk bakgrund i skolan och arbetsliv.
3. Kulturella rättigheter för personer med utländsk bakgrund.
4. Sociala rättigheter för personer med utländsk bakgrund.
5. Invandring som gynnsam för det svenska samhället.
6. Invandring som ett hot mot det svenska samhället.
7. Attityder till svensk invandringspolitik.
8. Invandrare utnyttjas på den svenska arbetsmarknaden.
9. Intresse av att interagera med invandrare och lära sig främmande kulturer.
10. Attityder till religion i allmänhet och islam i synnerhet.
11. Vilja att bo i samma bostadsområdet som invandrare.
12. Invandrartät är problemområde
FRESH-MAP dataset: study on the ecological success of streamlined aquatic microorganisms
No full textWe release the FRESH-MAP dataset, a compilation of 9028 prokaryotic species-clusters (ANI >95%) detected on a set of 636 globally-distributed freshwater metagenomes after competitive mapping. The main goal of our study was to provide the first systematic evaluation of the ‘Black Queen Hypothesis’ on a global scale based on aquatic metagenomic datasets. In this repository, we provide the supplementary tables and the full set of genomes. Moreover, we include all 9028 representative genomes (FRESH-MAP dataset) as a zip file (FreshMap_dataset.zip). You can also find 12 supplementary tables that include the following information.
- Table S1: Genomic statistics of all 80561 medium-to-high quality genomes (>50% completeness and 95%), including the best representative genome. We include origin of the species-clusters (freshwater, non-freshwater or mixed) and the type of genome (omics, isolate or both). Genome stats here included refer to the best representative genome of each species-cluster.
- Table S3: List of all 636 freshwater genomes used for competitive mapping. We include accession numbers, reference of publication and metadata.
- Table S4: Mapping statistics after trimming. We include the total number of reads, number of mapped reads, average length of the reads (bp), and GC content (%).
- Table S5: Relative abundance results for each of the 24050 species-clusters (ANI >95%) across the freshwater metagenomes.
- Table S6: List of 1202 representative genomes part of the co-occurrence network, including information on the degree of connectedness and cohort.
- Table S7: Recruitment of species-clusters per phyla across the different cohorts. Yellow indicates phyla linked to a specific cohort.
- Table S8: Completeness (ranging from 0 to 1) of all KEGG modules involved in biosynthesis of amino acids, nucleotides and vitamins for each of the 9028 species-clusters (ANI > 95%) detected on ≥1 freshwater metagenomes.
- Table S9: Number of copies of each KEGG KO involved in flagellar, sigma factors, two-component systems, carbon fixation, nitrogen cycle, and sulfur cycle for each of the 9028 species-clusters (ANI > 95%) detected on ≥1 freshwater metagenomes.
- Table S10: Metadata of the two newly sequenced metagenomic samples from Stadsträdgården, Uppsala (Sweden), including accession numbers.
- Table S11: Metagenomic samples used for re-binning from the StratfreshDB (Buck et al., 2021), including ENA accession numbers.
- Table S12: Genomic statistics, including completeness and contamination of the re-binned (n = 11146) and original (n = 7837) MAGs
LNP drug delivery image data
No full textData to accompany the manuscript "Deep learnings models for lipid-nanoparticle-based drug delivery"
Abstract:Large-scale time-lapse microscopy experiments are useful to understand delivery and expression in RNA-based therapeutics. The resulting data has high dimensionality and high (but sparse) information content, making it challenging and costly to store and process. Early prediction of experimental outcome enables intelligent data management and decision making. We start from time-lapse data of HepG2 cells exposed to lipid-nanoparticles loaded with mRNA for expression of green fluorescent protein (GFP). We hypothesize that it is possible to predict if a cell will express GFP or not based on cell morphology at time-points prior to GFP expression. Here we present results on per-cell classification (GFP expression/no GFP expression) and regression (level of GFP expression) using three different approaches. In the first approach we use a convolutional neural network extracting per-cell features at each time point. We then utilize the same features combined with: a long-short-term memory (LSTM) network encoding temporal dynamics (approach 2); and time-series feature extraction using the python package tsfresh followed by principal component analysis and gradient boosting machines (approach 3), to reach a final classification or regression result. Application of the three approaches to a previously unanalyzed test set of cells showed good predictive performance of all three approaches but that accounting for the temporal dynamics via LSTMs or tsfresh led to significantly improved performance. The predictions made by the LSTM and tsfresh applications were not significantly different. The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high content imaging.
Python code:https://github.com/pharmbio/phil_LNP_modellin
Cyp1A- och ABC-transportöruttryck och funktion i PLHC-1cellinje efter exponering för mikrocystin-LR och benso (a)pyren
No full textThe dataset presented corresponds to a study investigating mixture toxicity between two common aquatic contaminants, microcystin-LR (MCLR) and benzo[a]pyrene (BaP), on the detoxification system of fish. We used the Poeciliopsis lucida hepatocellular carcinoma (PLHC-1) cell line as a model for fish liver cells.
Cells were exposed to MCLR (0.01, 1 µM), BaP (0.01, 0.1, 1 µM), or combinations of both chemicals for periods ranging from 1 to 48 hours. We measured the following endpoints:
- Cytochrome P450 1A (CYP1A) function and regulation, assessing ethoxy resorufin-O-deethylase (EROD) activity and CYP1A mRNA expression. EROD activity was normalized to the protein.
- P-glycoprotein (Pgp) function and expression, using the rhodamine 123 (Rh123) accumulation assay and Pgp mRNA expression. Rh123 accumulation was normalized to the protein.
- Cell cytotoxicity, using two fluorescent indicators, 5-Carboxyfluorescein Diacetate-Acetoxymethyl Ester (CFDA-AM) and Alamar Blue (AB).
The dataset includes the following information:
1) CYP1A Activity: This section contains raw data used to calculate CYP1A activity, expressed as specific EROD activity. The EROD assay is based on the ability of the CYP1A enzyme to catalyze the O-deethylation of ethoxyresorufin to resorufin, a fluorescent product. Resorufin production was monitored over time and quantified using a resorufin standard curve. Each sample value was normalized to total protein content, which was measured using fluorescamine, and bovine serum albumin (BSA) as the protein standard. The files include:
- EROD slopes for each sample
- Protein concentration per sample
- Specific EROD activity
- Standard curves (for BSA and resorufin).
Additionally, the dataset includes results from an experiment involving pre-induction of EROD activity using β-naphthoflavone (BNF). Detailed descriptions of measurement conditions and calculation methods are provided in the accompanying README file (EROD_readme).
2) Rhodamine 123 Accumulation: This section includes raw data for calculating rhodamine 123 (Rh123) accumulation in cells, expressed as fluorescent units (FU) per mg of protein. The Rh123 accumulation assay measures the ability of the P-glycoprotein (Pgp) transporter to efflux this fluorescent compound from cells. When chemicals interfere with Pgp transport function, Rh123 accumulates within the cells. The files include:
- Rh123 fluorescence values (FU) per sample
- Protein concentration per sample
- BSA standard curves for protein quantification
Detailed descriptions of the measurement conditions and calculations are provided in the accompanying README file (Rh123_readme).
3) Cytotoxicity: Cytotoxicity was assessed by measuring mitochondrial activity and cell membrane integrity using two fluorescent indicators, Alamar Blue (AB) for mitochondrial activity and CFDA-AM for membrane integrity. This section includes raw fluorescence data (in fluorescent units, FU), which were used to express cytotoxicity as a percentage of FU relative to the control treatment. Detailed descriptions of measurement conditions and calculations are provided in the README file (cytotox_readme).
4) qPCR Data: This section contains raw cycle threshold (Ct) values from real-time polymerase chain reaction (qPCR) used to estimate the mRNA levels of target genes (CYP1A and Pgp). The data is normalized to a reference housekeeping gene, 18S, and presented as fold increase relative to the control treatment. Detailed descriptions of measurement conditions and normalization calculations are provided in the README file (qPCR_readme).Detta dataset inkluderar följande parametrar mätta i PLHC-1: cellinje.
1)Cyp1a-aktivitetsdata: Detta avsnitt innehåller rådata som används för att beräkna Cyp1a-aktivitet som EROD-aktivitet (etoxiresorufin-O-deetylas). Filerna inkluderar proteinkoncentration, EROD-lutning och specifik EROD-aktivitet, tillsammans med data från standardkurvor (BSA och resorufin). Dessutom finns det data från ett experiment som involverade pre-induktion av EROD-aktivitet med användning av β-naftoflavon. Detaljerade beskrivningar av mätförhållanden och beräkningar finns i den medföljande README-filen.
2) Cytotoxicitetsdata: Detta avsnitt tillhandahåller rådata (fluorescerande enheter) som används för att beräkna cytotoxicitet med två fluorescerande indikatorer: 5-Carboxyfluorescein Diacetate-Acetoximethyl Ester (CFDA-AM) och Alamar Blue (AB). README-filen innehåller beskrivningar av mätförhållandena och beräkningar.
3) qPCR-data: Detta inkluderar råa Ct-värden från qPCR-experiment.
4) Rhodamine 123-ackumuleringsdata: Detta a
Lake variables - Sonde profiling from Bolmen, Subbasin South
No full textManual or high frequency lake profiling using sonde technology over various water depths to measure lakes of SITES Water.
Bolmen Research Station (2025). Lake variables - Sonde profiling from Bolmen, Subbasin South, 2023-10-23–2024-11-28 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/7b5_BnFL5omKMaiYWJAQgnP
Skriftlig produktion i inlärarfranska
No full textThis corpus contains student texts. Swedish lower secondary school students aged 11-15 (years 6, 7, 8 and 9) were invited to retell a short story ("The Dog Story") in writing. The data were collected during an ordinary French lesson. The students had 20 minutes to write the text. They were encouraged to write as much as they could and to focus on communicating the content.
The texts were originally written by hand and transferred manually to Word-documents. The documents were later converted to plain text using LibreOffice 7.4 (command: 'soffice --headless --convert-to txt:Text YOUR-DOCUMENT-HERE.DOC').
For access to the corpus, contact Christina Lindqvist ([email protected]).Denna korpus innehåller elevtexter. Svenska högstadieelever i åldrarna 11-15 (år 6, 7, 8 och 9) fick i uppgift att återberätta en bildserie ("The Dog Story") skriftligt. Datan samlades in under en vanlig lektion i franska. Eleverna hade 20 minuter på sig att skriva texten. De uppmuntrades att skriva så mycket de kunde och att fokusera på att kommunicera innehållet.
Texterna skrevs ursprungligen för hand och överfördes sedan manuellt till Word-dokument. Dokumenten konverterades därefter till ren text med hjälp av LibreOffice 7.4 (kommando: 'soffice --headless --convert-to txt:Text YOUR-DOCUMENT-HERE.DOC').
Kontakta Christina Lindqvist ([email protected]) för att få tillgång till korpusen
Atmospheric horizontal gradients measured with eight co-located GNSS stations and a microwave radiometer
No full textWe have used eight co-located GNSS stations, with different antenna mounts, to estimate atmospheric signal propagation delays in the zenith direction and linear horizontal gradients. The gradients are compared with the results from a water vapour radiometer (WVR). Water drops in the atmosphere has a negative influence on the retrieval accuracy of the WVR. Hence we see a better agreement using WVR data with a liquid water content (LWC) less than 0.05mm compared to when LWC values of up to 0.7mm are included. We have used two different constraints when estimating the linear gradients from the GNSS data. Using a weak constraint enhances the GNSS estimates to track large gradients of short duration at the cost of increased formal errors. To mitigate random noise in the GNSS data, we adopted a fusion approach averaging estimates from the GNSS stations. This resulted in significant improvements for the agreement with WVR data, a maximum of 17% increase in the correlation and a 14% reduction in the root-mean-square (rms) difference for the east gradients. The corresponding values for the north gradients are both 25%. Overall, no large differences in terms of quality were observed for the eight GNSS stations. However, one station shows slightly poorer agreement for the north gradients compared to the others. This is attributed to the station's proximity to a radio telescope, which causes data loss of observations at low elevation angles in the south-south-west direction.Vi har använt åtta samlokaliserade GNSS-stationer, med olika antennmonteringar, för att skatta atmosfäriska signalutbredningsfördröjningar i zenitriktningen och linjära horisontella gradienter. Gradienterna jämförs med resultaten från en vattenångeradiometer (WVR). Vattendroppar i atmosfären har en negativ inverkan på noggrannheten. Därför ser vi en bättre överensstämmelse med WVR-data med ett vatteninnehåll (LWC) mindre än 0,05 mm jämfört med när LWC-värden på upp till 0,7 mm ingår. Vi har använt två olika begränsningar för variationerna när vi skattar de linjära gradienterna från GNSS-data. Att använda en svag begränsning förbättrar GNSS-skattningarna för att följa stora gradienter med kort varaktighet till kostnaden för större formella fel. För att undertrycka slumpmässiga fel i GNSS-data, använde vi genomsnittliga skattningar från GNSS-stationerna. Detta resulterade i betydande förbättringar för överensstämmelsen med WVR-data, en maximal ökning på 17 % av korrelationen och en 14 % minskning av skillnaden mellan root-mean-square (rms) för östgradienterna. Motsvarande värden för de nordliga gradienterna är båda 25 %. Sammantaget observeras inga stora skillnader vad gäller kvalitet för de åtta GNSS-stationerna. En station visar dock något sämre överensstämmelse för de nordliga gradienterna jämfört med de andra. Detta beror på stationens närhet till ett radioteleskop, vilket orsakar dataförlust av observationer vid låga höjdvinklar i syd-sydvästlig riktning
Household Appliance Ownership and Electricity Consumption in Ghana
No full textThe data contains information on household demographic and socioeconomic characteristics. The data was collected at the household level, hence, respondents are representatives of each household. It also provides information on electrical appliances owned by each household and their characteristics. Additionally, the data contains information on the monthly amount households pay for electricity and the kilowatts of electricity they consume in a month. The data also provides information on households' knowledge of the impact of electricity consumption on climate change and on the environment. Additionally, it provides information on conditions under which households will be willing to contribute towards climate change mitigation by promoting electricity consumption efficiency.The data contains information on household demographic and socioeconomic characteristics. The data was collected at the household level, hence, respondents are representatives of each household. It also provides information on electrical appliances owned by each household and their characteristics. Additionally, the data contains information on the monthly amount households pay for electricity and the kilowatts of electricity they consume in a month. The data also provides information on households' knowledge of the impact of electricity consumption on climate change and on the environment. Additionally, it provides information on conditions under which households will be willing to contribute towards climate change mitigation by promoting electricity consumption efficiency
MultiGED
No full textDataset description
MultiGED is a dataset for Multilingual Grammatical Error Detection in 5 European languages (Czech, English, German, Italian and Swedish) compiled by the CompSLA working group in the context of MultiGED-2023, the first multilinual GED shared task.
The data comes from learner essays, but the sequence of sentences within essays is not kept. Instead, this is a set of randomized sentences to prevent re-construction of original essays.
Data is provided in a tab-separated format consisting of two columns, where the first column contains the token and the second column contains the label (c or i), i.e. correct and incorrect. Note that there are no column headers, each sentence is separated by an empty line, and double quotes are escaped.
See more on data format .Dataset description
MultiGED is a dataset for Multilingual Grammatical Error Detection in 5 European languages (Czech, English, German, Italian and Swedish) compiled by the CompSLA working group in the context of MultiGED-2023, the first multilinual GED shared task.
The data comes from learner essays, but the sequence of sentences within essays is not kept. Instead, this is a set of randomized sentences to prevent re-construction of original essays.
Data is provided in a tab-separated format consisting of two columns, where the first column contains the token and the second column contains the label (c or i), i.e. correct and incorrect. Note that there are no column headers, each sentence is separated by an empty line, and double quotes are escaped.
See more on data format
Data för: Coverage of Web Accessibility Guidelines Provided by Automated Checking Tools
No full textThis data set contains three parts:
1. A collection of the raw data, which includes (a) the retrieved landing page of each analyzed PSO (to be precise, the DOM presentation from a browser showing this page) both in HTML and text (text without HTML tags), (b) for each of the six automated checker/engine combination one log file, (c) other metadata such as text file containing tools' and libraries' version information.
Data of case 1(a) may contain personal data (details see below) and is thus kept in a separate archive file and is only available upon request. Data of case 1(b) has been stripped of personal data and thus may get shared freely.
This data allows investigating how the webpages looked at the time of the study and to which assessments the then-current automated checkers came. Future studies can reproduce the same setup and, for example, compare changes over time in PSOs' webpages' accessibility.
2. A "coverage" file that is essentially a big database on WCAG-2 success criteria, their metadata, and links to automated checkers' documentation and source code. The "coverage" file combines information from various sources, such as information scrapped from W3C web page, accessibility tools' Git repositories, or AXE's documentation. Other researchers can load this "coverage" file to get a database of WCAG-2 success criteria and associated metadata in their data analysis without performing those error-prone and tedious steps themselves.
3. A collection of Python files. This not only allows reproducing how raw data was process and filtered (up to the output of LaTeX code), but allows other researchers to get inspiration how to solve problems addressed in this code base as well as to re-use code in their own projects.
The data covered by case 1(a) above includes textual data collected from publicly available web pages of Swedish public sector organizations (PSOs), which may include names, contact details, or other personal or biographical information. Due to the directory structure, for every file the origin of the data is determined, so any further questions about the handling of personal data shall be directed to the respective PSO.Detta dataset innehåller tre delar:
1. En samling rådata som innehåller (a) den hämtade landningssidan för varje analyserad PSO (närmare bestämt DOM-presentationen från en webbläsare som visar denna sida) både i HTML och text (text utan HTML-taggar), (b) en loggfil för var och en av de sex automatiska kontroll-/motorkombinationerna, (c) andra metadata såsom en textfil som innehåller verktygens och bibliotekens versionsinformation.
Uppgifter om fall 1(a) kan innehålla personuppgifter och förvaras därför i ett separat arkivfil och kan endast lämnas ut på förfrågan. Uppgifter om fall 1(b) har rensats på personuppgifter och kan därför delas fritt.
Dessa data gör det möjligt att undersöka hur webbsidorna såg ut vid tidpunkten för studien och vilka bedömningar de då aktuella automatiska kontrollerna gjorde. Framtida studier kan upprepa samma upplägg och t.ex. jämföra förändringar över tid i tillgängligheten på PSO:ernas webbsidor.
2. En ”täckningsfil” som i princip är en stor databas över WCAG-2:s framgångskriterier, deras metadata och länkar till dokumentation och källkod för automatiserade kontrollprogram. ”Täckningsfilen” kombinerar information från olika källor, t.ex. information som hämtats från W3C:s webbsida, tillgänglighetsverktygens Git-arkiv eller AXE:s dokumentation. Andra forskare kan ladda denna ”täckningsfil” för att få en databas med WCAG-2 framgångskriterier och tillhörande metadata i sin dataanalys utan att själva utföra dessa felbenägna och tråkiga steg.
3. En samling Python-filer. Detta gör det inte bara möjligt att reproducera hur rådata bearbetades och filtrerades (upp till utdata av LaTeX-kod), utan gör det också möjligt för andra forskare att få inspiration till hur man löser problem som tas upp i denna kodbas samt att återanvända kod i sina egna projekt.
De uppgifter som omfattas av fall 1(a) ovan inkluderar textuppgifter som samlats in från offentligt tillgängliga webbsidor tillhörande svenska offentliga organisationer (PSO), vilka kan innehålla namn, kontaktuppgifter eller annan personlig eller biografisk information. På grund av katalogstrukturen fastställs uppgifternas ursprung för varje fil, så eventuella ytterligare frågor om hanteringen av personuppgifter ska riktas till respektive PSO