https://researchdata.se/sv/catalogue Search results sv Datasetet innehåller forskningsdata som samlats in och strukturerats för en litteraturöversikt om elektrodialys för hållbar utvinning och återvinning av kritiska råmaterial. Materialet omfattar bibliografisk information, extraherade studiedata, sökstrategier och screeningdata från litteratursökningar i Scopus och Web of Science. Datasetet består av flera filer i CSV-format, inklusive: studier med bibliografisk metadata och DOI, variabler relaterade till kritiska råmaterial, elektrodialystekniker, membrantyper, råmaterial, metoder och resultat, dokumentation av sökstrategier och söksträngar, codebook med definitioner av variabler. Data har bearbetats genom manuell screening, kategorisering och extraktion från publicerade vetenskapliga artiklar. Inga specialiserade program krävs för att öppna filerna utöver standardprogram för CSV-filer. Tue, 09 Jun 2026 12:26:10 GMT https://researchdata.se/sv/catalogue/dataset/2026-201 https://researchdata.se/sv/catalogue/dataset/2026-201 Luleå tekniska universitet Adam Isaksson Lena Sundqvist Öqvist Raw NMR data for the article "Auto-Relay Catalysis for the Oxidative Carboxylation of Alkenes into Cyclic Carbonates by a MOF Catalyst" The raw NMR data files for all compounds reported in the article are included. The numbering corresponds to those in the article. Each parent folder contains subfolders with different files. In order to process this data, the full parent folder must be dragged into either Mestrenova or Topspin and then the data is automatically processed. If the name of the raw data files are renamed, the software (Mestrenova or Topspin) will not be able to process the files.  This data support the supplementary information of the article entitled  "Auto-Relay Catalysis for the Oxidative Carboxylation of Alkenes into Cyclic Carbonates by a MOF Catalyst" published in Green Chemistry 2025 with  DOI: 10.1039/D4GC06360K. Mon, 03 Feb 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-13365877 https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-13365877 Stockholms universitet Phan, Vu Duc Ha Cabrera-Afonso, María Jesús de la Cruz Sánchez, Pol Martín-Matute, Belen This is a dataset with XRF and XRD scanning of 16 healthy mouse bones. The raw data is seperated into scans in the following manner: 6 frontal sections of from female mice sacrificed at an age of 24 weeks were scanned, they are seperated in the following scans (one line per sample): 8340—8344 382—390 8346–8349 8350–8354 391–394,399–401 8355–8360 Analysis based on these scans are collected in the files named "WT_F_24w.h5" (one with azimuthal resolved fitting, one with fitting in the scattering direction) 4 frontal sections of from female mice sacrificed at an age of 36 weeks were scanned, they are seperated in the following scans (one line per sample): 8461–8469 8470–8476 8477–8484 8557–8563 Analysis based on these scans are collected in the files named "WT_F_36w.h5" (one with azimuthal resolved fitting, one with fitting in the scattering direction) 6 frontal sections of from male mice sacrificed at an age of 36 weeks were scanned, they are seperated in the following scans (one line per sample): 8381–8385 8386–8392 8393–8399 8549–8556 409–414 445–452 Analysis based on these scans are collected in the files named "WT_M_36w.h5" (one with azimuthal resolved fitting, one with fitting in the scattering direction) Scans 357 and 8332 are the scans of LaB6 that the integrations are based on. The poni files are included as attatchments to those files. Scans with a number above 8000 are integrated with the poni from 8332. Scans with a number below 8000 are integrated with the poni from 357. Wed, 01 Jan 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-48391-d7dcf27a-5f2f-4dad-b8f2-d7a54c105815 https://researchdata.se/sv/catalogue/dataset/doi-10-48391-d7dcf27a-5f2f-4dad-b8f2-d7a54c105815 Lunds universitet Christensen, Thorbjørn To date, the SARS CoV-2 virus has infected >400 million people, causing ~6 million fatalities globally. Multiple research efforts are ongoing, aimed at different ways to target the virus, however there is still no magic bullet available. Current vaccines offer excellent protection against severe disease and death, but ever-emerging new variants could be better controlled with small molecule inhibitors that are not dependent on the spike protein structure. To this end we would like to access the FragMAX platform BioMAX to screen for fragment hits against key components of the SARS CoV-2 viral replication machinery, the nsp10-nsp14ExoN complex. Mon, 01 Jan 2024 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-48391-cff474da-2b3f-4a3a-9226-9c4f1d62470e https://researchdata.se/sv/catalogue/dataset/doi-10-48391-cff474da-2b3f-4a3a-9226-9c4f1d62470e Lunds universitet Fisher, Zoe krojer, tobias To test tools to perform remote experiments at BioMAX Sun, 01 Jan 2023 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-48391-52813eb9-df48-4f4c-911b-f24f8d079419 https://researchdata.se/sv/catalogue/dataset/doi-10-48391-52813eb9-df48-4f4c-911b-f24f8d079419 Lunds universitet Gonzalez, Ana The experiment is a collaboration between MAX IV and PSI to prove the feasibility to use the high frame-rate state of the art integrating pixel detector (JUNGFRAU) for serial crystallography experiments at a 4th generation synchrotron light source. A PSI-provided JUNGFRAU 4M prototype detector was installed at the BioMAX beamline. The serial synchrotron crystallography (SSX) experiment was using the MAX IV high-viscosity extruder (HVE) for sample delivery. Two reference datasets were collected at 1 kHz and 2 kHz framerates. The third reference dataset was collected with frame rate 100 Hz using 1 kHz detector settings and adjusted flux and jet parameters in order to preserve constant radiation dose. Sun, 01 Jan 2023 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-48391-b0c36bb8-a00c-4519-8dcc-08d5ca60a313 https://researchdata.se/sv/catalogue/dataset/doi-10-48391-b0c36bb8-a00c-4519-8dcc-08d5ca60a313 Lunds universitet Leonarski, Filip Nan, Jie Matej, Zdenek Bertrand, Quentin Furrer, Antonia Gorgisyan, Ishkhan Bjelčić, Monika Kepa, Michal Glover, Hannah Hinger, Viktoria Eriksson, Thomas Cehovin, Aleksander Eguiraun, Mikel Gasparotto, Piero Mozzanica, Aldo Weinert, Tobias Gonzalez, Ana Standfuss, Jörg Wang, Meitian Ursby, Thomas Dworkowski, Florian Denna studie (Johansson Falck & Okonski, 2023) adresserar problematiken i att identifiera metaforisk betydelse som reflekteras i språk som innehåller prepositioner (t.ex., Reijnierse, 2019, Herrmann et al., 2019, Nacey et al. 2019a, Marhula och Rosiński, 2019). Studien visar hur Procedure for Identifying Metaphorical Scenes (PIMS) reflected and evoked by linguistic expressions in discourse diskurs (Johansson Falck & Okonski, 2022) kan användas för att identifiera metaforiska relationer i språk som innehåller prepositioner. Två studier presenteras där metodens reliabilitet samt känslighet i att identifiera metaforisk betydelse testas. Studierna visar att metoden är tillförlitlig samt att den underlättar identifiering av metaforisk betydelse i den här typen av konstruktioner. Litteraturreferenser Herrmann, B., et al. (2019). Linguistic metaphor identification in German. In Nacey, S., Dorst, A. G., Krennmayr, T. W., Reijnierse, W. G. (Eds.). Metaphor Identification in Multiple Languages: MIPVU Around the World, Volume 22 (pp 113-136). Amsterdam/Philadelphia, John Benjamins Publishing Company. Johansson Falck, M., & Okonski, L. (2022). Procedure for Identifying Metaphorical Scenes (PIMS): A Cognitive Linguistics Approach to Bridge Theory and Practice. Cognitive Semantics, 8, 294-322. doi:10.1163/23526416-bja10031 Marhula, J. and M. Rosiński (2019). Linguistic metaphor identification in Polish. In Nacey, S., Dorst, A. G., Krennmayr, T. W., Reijnierse, W. G. (Eds.). Metaphor Identification in Multiple Languages: MIPVU Around the World, Volume 22 (pp 183-202). Amsterdam/Philadelphia, John Benjamins Publishing Company. Nacey, S., et al. (2019). Linguistic metaphor identification in Scandinavian. In Nacey, S., Dorst, A. G., Krennmayr, T. W., Reijnierse, W. G. (Eds.). Metaphor Identification in Multiple Languages: MIPVU Around the World, Volume 22 (pp 137-158). Amsterdam/Philadelphia, John Benjamins Publishing Company. Reijnierse, W. G. (2019). Linguistic metaphor identification in French. In Nacey, S., Dorst, A. G., Krennmayr, T. W., Reijnierse, W. G. (Eds.). Metaphor Identification in Multiple Languages: MIPVU Around the World, Volume 22 (pp 69-90). Amsterdam/Philadelphia, John Benjamins Publishing Company. Vi analyserade data som tidigare analyserades genom MIPVU-metoden (Steen et al., 2019; Nacey et al. 2019) och fokuserade på ord som hade taggats som prepositioner i en text om Brexit (Elgot 2016). För detaljer se Johansson Falck och Okonski (2022). Litteraturreferenser: Johansson Falck, M., & Okonski, L. (2023). Procedure for Identifying Metaphorical Scenes (PIMS): The Case of Spatial and Abstract Relations. Metaphor and Symbol, 38(1), 1-22. doi:10.1080/10926488.2022.2062243 Nacey, S., et al. (2019b). What the MIPVU protocol doesn't tell you (even though it mostly does). In Nacey, S., Dorst, A. G., Krennmayr, T. W., Reijnierse, W. G. (Eds.). Metaphor Identification in Multiple Languages: MIPVU Around the World, Volume 22 (pp 41-68). Amsterdam/Philadelphia, John Benjamins Publishing Company. Steen, G. J., et al. (2019). MIPVU: a manaual for identifying metaphore-related words. In Nacey, S., Dorst, A. G., Krennmayr, T. W., Reijnierse, W. G. (Eds.). Metaphor Identification in Multiple Languages: MIPVU Around the World, Volume 22 (pp 23-40). Amsterdam/Philadelphia, John Benjamins Publishing Company. Elgot, J. (2016, June 24). Brexit, the fallout and the UK's future: what we know so far. The Guardian. https://www.theguardian.com/politics/2016/jun/24/brexit-fallout-what-we-know-so-far En beskrivning av MIPVU-metoden finns också på Open Science Framework på adressen: https://osf.io/vw46k/ Mon, 19 Dec 2022 07:47:24 GMT https://researchdata.se/sv/catalogue/dataset/2021-5-2 https://researchdata.se/sv/catalogue/dataset/2021-5-2 Umeå universitet Marlene Johansson Falck Lacey Okonski OSC collection in Biomax for Sample 57531 Sat, 01 Jan 2022 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-48391-91a3a886-df6f-434a-9a1d-7304a0061e00 https://researchdata.se/sv/catalogue/dataset/doi-10-48391-91a3a886-df6f-434a-9a1d-7304a0061e00 Lunds universitet Gonzalez, Ana OSC collection in Biomax for Sample 47910 Sat, 01 Jan 2022 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-48391-b0179486-47c5-4e5c-8301-cde7bfcb2ce1 https://researchdata.se/sv/catalogue/dataset/doi-10-48391-b0179486-47c5-4e5c-8301-cde7bfcb2ce1 Lunds universitet Gonzalez, Ana Note: This is version two of the data set. In a previous version (https://doi.org/10.5281/zenodo.5066997), the vertical beam velocities were missing. A bottom mounted mooring with a fixed upward-looking Nortek Signature 500 ADCP was deployed under the ship lane to Gothenburg Harbour at 32 m depth, position (57.61178 N, 11.66102 E) in the period 28 August to 25 September 2018. The ADCP was set up to measure and store single ping, along beam current velocities, using four slanted beams (25° angle) and one vertical beam (ping frequency 1 Hz, vertical cell size 1 m on all beams). The instrument was set up with 38 bins numbered from 1 to 38 with #1 closest to the instrument centered 1 m above the transducers. The data set contains raw data from the instrument, converted to csv format with the Nortek Deployment software. Processing: This is raw data, so no processing has been performed. Notes: - Beam #2 is not working, so those data do not make sense. - Data from about 10% of the depth below the surface and above should not be used. Files:  *#B0_#.csv: Data from the four slanted beams. Each row is one ping. Header in first row. *#IB0_#.csv: Data from the vertical beam. Each row is one ping. Header in first row. Parameters: DateTime: Date and time in format MM/DD/YYYY hh:mm:ss Ensemble: Ping number Battery: Battery voltage Heading: Instrument heading in deg. Pitch: Instrument pitch in deg. Roll: Instrument roll in deg. Pressure: Pressure in dBar Temperature: Temperature in deg C. Vel#N: Vertical beam velocity (m/s) for bin N VelM#N: Beam velocity (m/s) for beam M and bin N  Amp#N: Vetical beam amplitude for bin N AmpM#N: Beam amplitude for beam M and bin N  CorrM#N: Correlation for beam M and bin N  AltimeterDistanceLE: Distance from transducer to surface (m) Sat, 03 Jul 2021 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-10846812 https://researchdata.se/sv/catalogue/dataset/doi-10-5281-zenodo-10846812 Chalmers tekniska högskola Arneborg, Lars Nylund, Amanda Hassellöv, Ida-Maja Tengberg, Anders