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We previously identified a number of metabolic abnormalities in tko25t larvae, including elevated pyruvate and lactate, and found the larval gut to be a crucial tissue for the regulation of larval growth in the mutant. Here we established that expression of wild-type tko in any of several other tissues of tko25t also partially alleviates developmental delay. The effects appeared to be additive, whilst knockdown of tko in a variety of specific tissues phenocopied tko25t, producing developmental delay and bang-sensitivity. These findings imply the existence of a systemic signal regulating growth in response to mitochondrial dysfunction. Drugs and RNAi-targeted on pyruvate metabolism interacted with tko25t in ways that implicated pyruvate or one of its metabolic derivatives in playing a central role in generating such a signal. RNA-seq revealed that dietary pyruvate-induced changes in transcript representation were mostly non-coherent with those produced by tko25t or high-sugar, consistent with the idea that growth regulation operates primarily at the translational and/or metabolic level."},"field_of_science":[{"id":"a006e4cc-b051-4d1c-b94c-e1655d8888bc","url":"http://www.yso.fi/onto/okm-tieteenala/ta3111","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Biomedicine","fi":"Biolääketieteet","sv":"Biomedicinska 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It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R language that have become standard for high-throughput analytics. However, application of these tools is technically challenging for generic users and require specific computational skills. There is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results. Results We propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application. Conclusions eUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods."},"field_of_science":[{"id":"aaeda937-2324-41b5-af09-17f5f34b7b8e","url":"http://www.yso.fi/onto/okm-tieteenala/ta318","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Medical biotechnology","fi":"Lääketieteen bioteknologia","sv":"Medicinsk bioteknologi"}}],"infrastructure":[],"issued":"2019-01-01","keyword":["Medical 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Background Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Several tools are already available to the community to represent and compare functional profiles of omics experiments. However, when the number of experiments and/or enriched functional terms is high, it becomes difficult to interpret the results even when graphically represented. Therefore, there is currently a need for interactive and user-friendly tools to graphically navigate and further summarize annotations in order to facilitate results interpretation also when the dimensionality is high. Results We developed an approach that exploits the intrinsic hierarchical structure of several functional annotations to summarize the results obtained through enrichment analyses to higher levels of interpretation and to map gene related information at each summarized level. We built a user-friendly graphical interface that allows to visualize the functional annotations of one or multiple experiments at once. The tool is implemented as a R-Shiny application called FunMappOne and is available at https://github.com/grecolab/FunMappOne . Conclusion FunMappOne is a R-shiny graphical tool that takes in input multiple lists of human or mouse genes, optionally along with their related modification magnitudes, computes the enriched annotations from Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, or Reactome databases, and reports interactive maps of functional terms and pathways organized in rational groups. FunMappOne allows a fast and convenient comparison of multiple experiments and an easy way to interpret results."},"field_of_science":[{"id":"aaeda937-2324-41b5-af09-17f5f34b7b8e","url":"http://www.yso.fi/onto/okm-tieteenala/ta318","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Medical biotechnology","fi":"Lääketieteen bioteknologia","sv":"Medicinsk bioteknologi"}}],"infrastructure":[],"issued":"2019-02-15","keyword":["Medical 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The transmittance of vibration stimulus across the median sternotomy was measured.Results:The validation showed that the force produced by the actuator was stable, and the sensor could be calibrated to precisely measure the acceleration values. The vibration transmittance response to material cut and sternotomy was evident and detectable in the 20 Hz to 2 kHz band. The transmittance decreased when the connectivity between the sternal halves became unstable. The trend was visible in all the settings.Conclusion:Technical solutions and description of validation process were given. The device was calibrated, and the vibration transmittance analysis differentiated intact and cut polylactide rod. In the sternum, intact bone, wire fixation with exact apposition, and with a gap were identified separately. 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Uusitalo","external_identifier":"38d5a2cb-1d3f-402c-b5e4-ad7b1bd7d8f5"},"organization":{"id":"ea83ea7b-4d44-4d0b-b4ac-a8df3f00ae20","pref_label":{"en":"Tampere University","fi":"Tampereen yliopisto","sv":"Tammerfors universitet","und":"Tampereen yliopisto"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10122","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}},{"id":"b796c253-c49f-44a6-9ff6-8225e4dd1fb0","roles":["publisher"],"organization":{"id":"159b57a4-179b-4ee0-999e-bb4dda86c4e1","pref_label":{"en":"figshare"}}}],"cumulative_state":0,"data_catalog":"urn:nbn:fi:att:data-catalog-tuni","description":{"en":"Abstract Background Femtosecond laser-assisted in situ keratomileusis (LASIK) has proven to be an efficacious, predictable, and safe procedure for the correction of refractive errors. We examined the early tear protein changes of patients undergoing LASIK surgery in order to better understand the mechanisms and proteins related to laser corneal surgery and initial recovery. Methods Corneal flaps were created with Ziemer FEMTO LDV Z6 I femtosecond laser and stroma was ablated using Wavelight EX500 excimer laser. Tear samples were collected preoperatively as well as 1.5 h and 1 month after LASIK treatment using glass microcapillary tubes. Relative quantification of tear proteins was performed with sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS). Results SWATH-MS revealed that 158 proteins had altered expression levels 1.5 h after the operation. Two-thirds of these proteins, mostly connected to migration and inflammation response, returned to preoperative levels within the first postoperative month. The other proteins, which did not return to baseline levels, included proteins connected to for example epithelial barrier function. We also identified several proteins, which correlated with surgical variables, such as the amount of correction, flap thickness and flap diameter. Conclusions The present study showed that an uneventful femtosecond LASIK refractive surgery induced a significant immune cell migration and inflammation-associated changes in tear proteomics profile quickly after the operation, but the expression of most proteins recovered almost completely to the preoperative levels within the first month. The individual proteins identified in our study are potential targets for the follow-up and modification of LASIK-induced biochemical processes."},"field_of_science":[{"id":"ce0419ba-487d-411c-91d5-b4b8a5a1d74b","url":"http://www.yso.fi/onto/okm-tieteenala/ta3125","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Otorhinolaryngology, ophthalmology","fi":"Korva-, nenä- ja kurkkutaudit, silmätaudit","sv":"Öron-, näs- och halssjukdomar, ögonsjukdomar"}}],"infrastructure":[],"issued":"2020-10-20","keyword":["Otorhinolaryngology ophthalmology"],"language":[{"id":"ec748146-3403-4a7f-adfe-bdbb1b889372","url":"http://lexvo.org/id/iso639-3/eng","in_scheme":"http://lexvo.org/id/","pref_label":{"en":"English","fi":"englanti","sv":"engelska"}}],"metadata_owner":{"id":"eb8aa44e-e16b-4c6c-ae4d-b9be524b58ed","organization":"service_tuni"},"other_identifiers":[{"notation":"8c48b251-35aa-4656-868e-7e4fc72294da","metax_ids":[]}],"persistent_identifier":"doi:10.6084/m9.figshare.c.5180016.v1","pid_generated_by_fairdata":false,"projects":[],"provenance":[],"relation":[],"remote_resources":[],"spatial":[],"state":"published","temporal":[],"theme":[],"title":{"en":"Early changes in tear film protein profiles after femtosecond LASIK surgery"},"created":"2025-01-28T16:44:22Z","modified":"2026-11-02T02:03:15Z","dataset_versions":[{"id":"d03916f2-9d38-47cc-abaf-0149d154d83f","title":{"en":"Early changes in tear film protein profiles after femtosecond LASIK surgery"},"persistent_identifier":"doi:10.6084/m9.figshare.c.5180016.v1","state":"published","created":"2025-01-28T16:44:22Z","version":1}],"published_revision":5,"version":1,"api_version":3,"metadata_repository":"Fairdata","record_created":"2025-01-28T16:44:22Z","record_modified":"2026-11-02T02:03:15Z"},{"id":"69f6ba57-2f90-457b-964b-98967ecc5f14","access_rights":{"id":"a7882602-a08f-44cb-9feb-ece4f3770ce7","license":[{"id":"60ce96ab-a581-4225-a652-bad746a435a2","url":"http://uri.suomi.fi/codelist/fairdata/license/code/notspecified","in_scheme":"http://uri.suomi.fi/codelist/fairdata/license","pref_label":{"en":"License Not Specified","fi":"Ei määritelty"}}],"access_type":{"id":"729ffd9f-6d7a-40e9-aa97-a363a16fd113","url":"http://uri.suomi.fi/codelist/fairdata/access_type/code/restricted","in_scheme":"http://uri.suomi.fi/codelist/fairdata/access_type","pref_label":{"en":"Restricted use","fi":"Saatavuutta rajoitettu"}},"restriction_grounds":[{"id":"0ed3a2e1-6c11-4278-af67-c6713f838ecd","url":"http://uri.suomi.fi/codelist/fairdata/restriction_grounds/code/other","in_scheme":"http://uri.suomi.fi/codelist/fairdata/restriction_grounds","pref_label":{"en":"Restricted access due to other reasons","fi":"Saatavuutta rajoitettu muulla perusteella","sv":"Begränsad åtkomst av övriga skäl"}}]},"actors":[{"id":"8ac1f105-3196-4d03-b2d7-e98f4c6d1d82","roles":["creator"],"person":{"id":"aec5620b-d29e-406c-8be8-7e06ce9b85e8","name":"Konstantinos Drossos","external_identifier":"b1070370-5156-4280-b354-6291618bb965"},"organization":{"id":"ea83ea7b-4d44-4d0b-b4ac-a8df3f00ae20","pref_label":{"en":"Tampere University","fi":"Tampereen yliopisto","sv":"Tammerfors universitet","und":"Tampereen yliopisto"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10122","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}},{"id":"4dbffa10-eefa-4b37-8075-da595084130c","roles":["creator"],"person":{"id":"a5aa85f8-8343-4a91-950f-7a481cafed37","name":"Samuel Lipping","external_identifier":"eff668ea-9ca6-463d-a536-bfbfbe0a341e"},"organization":{"id":"034d3954-da26-43aa-a084-e95ef65af118","pref_label":{"en":"Unknown organization","fi":"Tuntematon organisaatio"}}},{"id":"14bce304-8744-4cb2-ab0e-0f735aed4a59","roles":["creator"],"person":{"id":"91cca653-1bb6-475f-adf0-8f5ecc0f0e0e","name":"Tuomas Virtanen","external_identifier":"210e58bb-c224-40a9-bf6c-5b786297e841"},"organization":{"id":"ea83ea7b-4d44-4d0b-b4ac-a8df3f00ae20","pref_label":{"en":"Tampere University","fi":"Tampereen yliopisto","sv":"Tammerfors universitet","und":"Tampereen yliopisto"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10122","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}},{"id":"b4911dc3-bfc6-4327-84c4-2ae90785515f","roles":["publisher"],"organization":{"id":"01c6b6c4-af68-4848-baac-ce2f1c9e3486","pref_label":{"en":"Zenodo"}}}],"cumulative_state":0,"data_catalog":"urn:nbn:fi:att:data-catalog-tuni","description":{"en":"This is the evaluation split for Task 6, Automated Audio Captioning, in DCASE 2020 Challenge. This evaluation split is the Clotho testing split, which is thoroughly described in the corresponding paper: K. Drossos, S. Lipping and T. Virtanen, \"Clotho: an Audio Captioning Dataset,\" IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 736-740, doi: 10.1109/ICASSP40776.2020.9052990. available online at: https://arxiv.org/abs/1910.09387 and at: https://ieeexplore.ieee.org/document/9052990 This evaluation split is meant to be used for the purposes of the Task 6 at the scientific challenge DCASE 2020. This split it is not meant to be used for developing audio captioning methods. For developing audio captioning methods, you should use the development and evaluation splits of Clotho. If you want the development and evaluation splits of Clotho dataset, you can find them also in Zenodo, at: https://zenodo.org/record/3490684 -------------------------------------------------------------------------------------------------------- == License == The audio files in the archives: clotho_audio_test.7z and the associated meta-data in the CSV file: clotho_metadata_test.csv are under the corresponding licences (mostly CreativeCommons with attribution) of Freesound [1] platform, mentioned explicitly in the CSV file for each of the audio files. That is, each audio file in the 7z archive is listed in the CSV file with the meta-data. The meta-data for each file are: File name Start and ending samples for the excerpt that is used in the Clotho dataset Uploader/user in the Freesound platform (manufacturer) Link to the licence of the file -------------------------------------------------------------------------------------------------------- == References == [1] Frederic Font, Gerard Roma, and Xavier Serra. 2013. Freesound technical demo. In Proceedings of the 21st ACM international conference on Multimedia (MM '13). ACM, New York, NY, USA, 411-412. DOI: https://doi.org/10.1145/2502081.2502245"},"field_of_science":[{"id":"33d291b9-9b23-4192-b878-cffc210af1d3","url":"http://www.yso.fi/onto/okm-tieteenala/ta113","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Computer and information sciences","fi":"Tietojenkäsittely ja informaatiotieteet","sv":"Data- och informationsvetenskap"}}],"infrastructure":[],"issued":"2020-05-29","keyword":["Computer and information 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yliopisto"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10122","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}},{"id":"2eb7abd9-dc47-4e4e-a576-6003e662bca5","roles":["publisher"],"organization":{"id":"dce3efc4-72c3-4b57-bf46-349f83242d80","pref_label":{"en":"figshare"}}}],"cumulative_state":0,"data_catalog":"urn:nbn:fi:att:data-catalog-tuni","description":{"en":"Abstract Background Differentiation of functional limbal stem cells (LSCs) from human pluripotent stem cells (hPSCs) is an important objective which can provide novel treatment solutions for patients suffering from limbal stem cell deficiency (LSCD). Yet, further characterization is needed to better evaluate their immunogenicity and regenerative potential before clinical applications. Methods Human PSCs were differentiated towards corneal fate and cryopreserved using a clinically applicable protocol. Resulting hPSC-LSC populations were examined at days 10–11 and 24–25 during differentiation as well as at passage 1 post-thaw. Expression of cornea-associated markers including PAX6, ABCG2, ∆Np63α, CK15, CK14, CK12 and ABCB5 as well as human leukocyte antigens (HLAs) was analyzed using immunofluorescence and flow cytometry. Wound healing properties of the post-thaw hPSC-LSCs were assessed via calcium imaging and scratch assay. Human and porcine tissue-derived cultured LSCs were used as controls for marker expression analysis and scratch assays at passage 1. Results The day 24–25 and post-thaw hPSC-LSCs displayed a similar marker profile with the tissue-derived LSCs, showing abundant expression of PAX6, ∆Np63α, CK15, CK14 and ABCB5 and low expression of ABCG2. In contrast, day 10–11 hPSC-LSCs had lower expression of ABCB5 and ∆Np63α, but high expression of ABCG2. A small portion of the day 10–11 cells coexpressed ABCG2 and ABCB5. The expression of class I HLAs increased during hPSC-LSCs differentiation and was uniform in post-thaw hPSC-LSCs, however the intensity was lower in comparison to tissue-derived LSCs. The calcium imaging revealed that the post-thaw hPSC-LSCs generated a robust response towards epithelial wound healing signaling mediator ATP. Further, scratch assay revealed that post-thaw hPSC-LSCs had higher wound healing capacity in comparison to tissue-derived LSCs. Conclusions Clinically relevant LSC-like cells can be efficiently differentiated from hPSCs. The post-thaw hPSC-LSCs possess functional potency in calcium responses towards injury associated signals and in wound closure. The developmental trajectory observed during hPSC-LSC differentiation, giving rise to ABCG2+ population and further to ABCB5+ and ∆Np63α+ cells with limbal characteristics, indicates hPSC-derived cells can be utilized as a valuable cell source for the treatment of patients afflicted corneal blindness due to LSCD. 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In order to generate the fingerprint vectors, the non-detected APs where filled with the default bogus value +100dBm. i.e., the value +100dBm in the training and test rss files indicates that the AP was not detected. Generally, this default bogus value has been replaced with a low RSSI value in experiments, such as -100dBm, -110dBm, -150dBm or -200dBm. The coordinates X,Y,Z are expressed in meters in a local coordinate system. Additionally, the sequential floor identifier is also provided. Please cite the source at Zenodo when using any of these two datasets: Lohan, E.S; Torres-Sospedra, J.; Gonzalez, A.; \"WiFi RSS measurements in Tampere University multi-building campus, 2017\", Zenodo, 2021. https://zenodo.org/record/5174851 https://www.doi.org/10.5281/zenodo.5174851"},"field_of_science":[{"id":"5b789f2f-f545-497c-8354-c21a6718c723","url":"http://www.yso.fi/onto/okm-tieteenala/ta213","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Electronic, automation and communications engineering, electronics","fi":"Sähkö-, automaatio- ja tietoliikennetekniikka, elektroniikka","sv":"El-, automations- och telekommunikationsteknik, elektronik"}}],"infrastructure":[],"issued":"2021-08-10","keyword":["Electronic automation and communications engineering electronics"],"language":[{"id":"ec748146-3403-4a7f-adfe-bdbb1b889372","url":"http://lexvo.org/id/iso639-3/eng","in_scheme":"http://lexvo.org/id/","pref_label":{"en":"English","fi":"englanti","sv":"engelska"}}],"metadata_owner":{"id":"eb8aa44e-e16b-4c6c-ae4d-b9be524b58ed","organization":"service_tuni"},"other_identifiers":[{"notation":"04787b22-362d-41df-808b-17f041e9fb6f","metax_ids":[]}],"persistent_identifier":"doi:10.5281/zenodo.5174851","pid_generated_by_fairdata":false,"projects":[],"provenance":[],"relation":[],"remote_resources":[],"spatial":[],"state":"published","temporal":[],"theme":[],"title":{"en":"WiFi RSS measurements in Tampere University multi-building campus, 2017"},"created":"2025-01-28T16:22:01Z","modified":"2026-11-02T02:02:31Z","dataset_versions":[{"id":"fb12efea-05a9-4fbd-9d53-b9435e91726e","title":{"en":"WiFi RSS measurements in Tampere University multi-building campus, 2017"},"persistent_identifier":"doi:10.5281/zenodo.5174851","state":"published","created":"2025-01-28T16:22:01Z","version":1}],"published_revision":5,"version":1,"api_version":3,"metadata_repository":"Fairdata","record_created":"2025-01-28T16:22:01Z","record_modified":"2026-11-02T02:02:32Z"},{"id":"a74785a0-0904-4847-bb9c-a3a9573680f8","access_rights":{"id":"2945f035-5517-436d-8ba9-18b9e79133b4","license":[{"id":"6142d0c6-945d-4085-b9f7-81e52afa253b","url":"http://uri.suomi.fi/codelist/fairdata/license/code/other","in_scheme":"http://uri.suomi.fi/codelist/fairdata/license","pref_label":{"en":"Other","fi":"Muu"}}],"access_type":{"id":"d01ac02c-fc70-4c68-9434-8383cb693ff0","url":"http://uri.suomi.fi/codelist/fairdata/access_type/code/open","in_scheme":"http://uri.suomi.fi/codelist/fairdata/access_type","pref_label":{"en":"Open","fi":"Avoin"}},"restriction_grounds":[]},"actors":[{"id":"5abbfa47-5f04-439b-8b72-dbc4a3fb5190","roles":["creator"],"person":{"id":"eebe5b10-08db-4a9e-bbef-17b0e2e2e245","name":"Irene Martin Morato","external_identifier":"b2dd1b39-eb96-47ce-bc73-71664586c510"},"organization":{"id":"ea83ea7b-4d44-4d0b-b4ac-a8df3f00ae20","pref_label":{"en":"Tampere University","fi":"Tampereen yliopisto","sv":"Tammerfors universitet","und":"Tampereen yliopisto"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10122","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}},{"id":"d9372595-bea7-4718-b0e8-aa084acd0093","roles":["creator"],"person":{"id":"d608a9c2-52ad-4af1-ac41-cda2defd5d53","name":"Manu Harju","external_identifier":"a9a26492-134f-49cc-9432-dff1e97aabdc"},"organization":{"id":"195e8639-53fb-4e9a-9658-c21c789a82b9","pref_label":{"en":"Unknown organization","fi":"Tuntematon organisaatio"}}},{"id":"b340a4c7-1907-4e0f-b04e-b999efb224dc","roles":["creator"],"person":{"id":"38292e40-8bb9-4db3-96e6-a367cc794e7d","name":"Annamaria Mesaros","external_identifier":"48bea19c-41eb-4fd0-b309-d886963bf7e8"},"organization":{"id":"ea83ea7b-4d44-4d0b-b4ac-a8df3f00ae20","pref_label":{"en":"Tampere University","fi":"Tampereen yliopisto","sv":"Tammerfors universitet","und":"Tampereen yliopisto"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10122","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}},{"id":"6264eb3c-d007-40ef-abf4-ba1be9199787","roles":["publisher"],"organization":{"id":"48b8c843-4a57-4a57-821c-64624e9e41b2","pref_label":{"en":"Zenodo"}}}],"cumulative_state":0,"data_catalog":"urn:nbn:fi:att:data-catalog-tuni","description":{"en":"The dataset was created for studying estimation of strong labels using crowdsourcing. 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database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are: Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural). Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios. Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods. Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms. The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND. NOTE: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository. REPORT AND REFERENCE A compact description of the dataset, recording setup, recording procedure, and extraction can be found in: Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020), Tokyo, Japan. available here. A more detailed report specifically focusing on the dataset collection and properties will follow. AIM The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios: monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions monophonic and multichannel polyphonic sound events in multi-room reverberant conditions single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios SPECIFICATIONS The SRIRs were captured using an [Eigenmike](https://mhacoustics.com/products) spherical microphone array. A [Genelec G Three loudspeaker](https://www.genelec.com/g-three) was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness. The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\\phi\\in[-180,180)$, while the elevations span approximately a range between $\\theta\\in[-45,45]$ degrees. The currently shared measured spaces are as follows: Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory. Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory. Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory. Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory. Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory. Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory. Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory. Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory. Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory. The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a close and a far one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, close and far, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room. Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights. The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier. More details on the trajectory geometries can be found in the README file and the measinfo.mat file. RECORDING FORMATS As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README. We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural). REFERENCE DOAs For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal. The DOAs are provided as Cartesian components [x, y, z] of unit length vectors. SCENE GENERATOR A set of routines is shared, here termed scene generator, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the TAU-NIGENS Spatial Sound Events 2021 dataset, and has been ported to Python from the original version written in Matlab. The generator can be found [**here**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use. The generator at the moment is set to work with the NIGENS sound event sample database, and the FSD50K sound event database, but additional sample banks can be added with small modifications. The dataset together with the generator has been used by the authors in the following public challenges: - DCASE 2019 Challenge Task 3, to generate the TAU Spatial Sound Events 2019 dataset (development/evaluation) - DCASE 2020 Challenge Task 3, to generate the TAU-NIGENS Spatial Sound Events 2020 dataset - DCASE2021 Challenge Task 3, to generate the TAU-NIGENS Spatial Sound Events 2021 dataset - DCASE2022 Challenge Task 3, to generate additional SELD synthetic mixtures for training the task baseline NOTE: The current version of the generator is work-in-progress, with some code being quite \"rough\". If something does not work as intended or it is not clear what certain parts do, please contact us. DATASET STRUCTURE The dataset contains a folder of the SRIRs (TAU-SRIR_DB), with all the SRIRs per room in a single MAT file. The file rirdata.mat contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file measinfo.mat contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (TAU-SNoise_DB), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. For more information on how to SRIRs and DOAs are organized, check the README. DOWNLOAD The files TAU-SRIR_DB.z01, ..., TAU-SRIR_DB.zip contain the SRIRs and measurement info files. The files TAU-SNoise_DB.z01, ..., TAU-SNoise_DB.zip contain the ambient noise recordings. Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal: Combine the split archive to a single archive: zip -s 0 split.zip --out single.zip Extract the single archive using unzip: unzip single.zip LICENSE The database is published under a custom **open non-commercial with attribution** license. 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