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In the experiments, four Yamaha RIO1608-D2 and four Yamaha RIO8-D audio interfaces were daisy chained to form a 64-input-64-output audio system that communicates with a computer through a Dante virtual sound card. Both the loudspeaker (Genelec 8010A) and microphone (DPA 4060) arrays were connected to the audio interfaces for simultaneous sound generation and acquisition. During the measurements, a 3 s long logarithmic sine sweep signal from 20 Hz to 22 kHz was generated in MATLAB with a sampling rate of 48 kHz and reproduced through each loudspeaker, and the sound pressure in each zone was captured by either the square or double-layer circular microphone array.

The database consists of 260,400 RIRs, among which 134,400 were measured by a square microphone array and 126,000 were measured by a double-layer circular microphone array. Each impulse response is stored as a 24-bit single-channel WAV file at a sampling rate of 48 kHz, with the naming convention of “Room_Zone_MicrophoneArrayType_LoudspeakerIndex_MicrophoneIndex.wav”. For example, “AnechoicRoom_ZoneA_CircularMicrophoneArray_L1_M2.wav” is the impulse response from the first loudspeaker to the second microphone of the double-layer circular microphone array that is placed at Zone A in the anechoic room.

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Please prior to use it, register to [usegalaxy.eu](https://usegalaxy.eu/login/start) and authenticate yourself before starting it." } ], "https://w3id.org/ro/terms/earth-science#normScore": [ { "@value": "36.83206106870229" } ], "https://w3id.org/ro/terms/earth-science#score": [ { "@value": "19.3" } ] }, { "@id": "https://w3id.org/ro-id/572c411e-83d9-4fd8-b581-9724b2674243", "@type": [ "https://w3id.org/ro/terms/earth-science#Phrase" ], "http://schema.org/name": [ { "@value": "usage of JupyterGIS" } ], "https://w3id.org/ro/terms/earth-science#normScore": [ { "@value": "21.043771043771045" } ], "https://w3id.org/ro/terms/earth-science#score": [ { "@value": "12.5" } ] }, { "@id": "https://w3id.org/ro-id/588ada8d-a185-402e-8b60-3c17435110ee/#4a9c340c-3f55-4473-8ebc-9798b38b791a", "@type": [ "http://schema.org/Place" ], "http://schema.org/geo": [ { "@id": "https://w3id.org/ro-id/588ada8d-a185-402e-8b60-3c17435110ee/#c31df009-cac3-4389-9ee6-fc85383fc397" } ], "http://schema.org/identifier": [ { "@value": "4a9c340c-3f55-4473-8ebc-9798b38b791a" } ], "http://schema.org/name": [ { "@value": "POINT (7.0017099363030875 49.229107487031975)" } ] }, { "@id": "https://w3id.org/ro-id/588ada8d-a185-402e-8b60-3c17435110ee/#c31df009-cac3-4389-9ee6-fc85383fc397", "@type": [ "http://schema.org/GeoCoordinates" ], "http://schema.org/latitude": [ { "@value": "7.0017099363030875" } ], "http://schema.org/longitude": [ { "@value": "49.229107487031975" } ], "http://schema.org/name": [ { "@value": "POINT (7.0017099363030875 49.229107487031975)" } ] }, { "@id": "https://w3id.org/ro-id/588ada8d-a185-402e-8b60-3c17435110ee/", "@type": [ "http://purl.org/wf4ever/ro#ResearchObject", "http://purl.org/wf4ever/roevo#ForkedRO", "http://purl.org/wf4ever/roevo#LiveRO", "http://schema.org/Dataset" ], "http://purl.org/wf4ever/roevo#forkedAtTime": [ { "@value": "2025-05-28 12:08:14.740275+00:00" } ], "http://purl.org/wf4ever/roevo#forkedBy": [ { "@value": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/about": [ { "@id": "http://eurovoc.europa.eu/3949" } ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/contentLocation": [ { "@id": "https://w3id.org/ro-id/588ada8d-a185-402e-8b60-3c17435110ee/#4a9c340c-3f55-4473-8ebc-9798b38b791a" } ], "http://schema.org/contentSize": [ { "@value": "0", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/ros/588ada8d-a185-402e-8b60-3c17435110ee/crate/download/" } ], "http://schema.org/contributor": [ { "@id": "mailto:elifsu@simula.no" } ], "http://schema.org/copyrightHolder": [ { "@id": "https://ror.org/00vn06n10" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-1784-2920" } ], "http://schema.org/dateCreated": [ { "@value": "2025-05-17 17:15:18.830605+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-05-28 12:41:54.042079+00:00" } ], "http://schema.org/datePublished": [ { "@value": "2025-05-17 17:15:18.830605+00:00" } ], "http://schema.org/description": [ { "@value": "## Flooding in Saarland\n\nWe showcase the usage of JupyterGIS for creating geographical maps showing the HQ 100 flooding zones in Saarland along with images from Wikimedia Commons.\n\nThese notebooks showcase how to use JupyterGIS.\n\nWe use JupyterGIS on the UseGalaxy.eu. The tool can be launched from [JupyterGIS in Galaxy Europe](https://usegalaxy.eu/root?tool_id=interactive_tool_jupytergis_notebook). 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Since then, many resources (e.g. policy documents, scientific articles, data sets) relevant for national adaptation have been published, yet often remain unknown to, or inaccessible, for the multiple stakeholders that comprise the climate adaptation community (CCA) in Portugal. The application of FAIR (findability, accessibility, interoperability and reusability) principles to these resources has the potential to improve their discoverability, accessibility, and usability and ultimately promote a more effective climate adaptation planning in Portugal. \n\nThis study explores the state of the art of scientific research on climate change adaptation in Portugal since 2010, to assess strengths and limitations of knowledge, identifying key priority areas for future research and the “FAIRification” of these resources. As the frequency and intensity of extreme heat events in Portugal increases, this study focused on heat-related hazards, and associated impacts. A systematic search was performed across Scopus and ISI Web of Science to identify relevant peer-reviewed articles between January 2010 and May 2025. The resulting publications were analysed by geographical scope, adaptation sectorial focus, knowledge sector, climate hazard and CCA needs. Alignment of the current scientific outputs with FAIR principles was also analysed. \n\nA total of 217 articles were reviewed. Research was mostly performed at the city or national level (37% each) and the majority of adaptation actions were done under the agricultural or urban sectors (19% and 26%, respectively). Research was multidisciplinary, covering different knowledge sectors, from health impacts to biodiversity, buildings and construction, and often included several knowledge sectors in the same article. All articles presented accessible metadata but only one made its scripts and data available for further replication. \n\nResults show that scientific research on heat-related climate change adaptation in Portugal is diverse and covers many knowledge areas, but the availability of its data needs improvement. Further work will include policy FAIR assessment of policy documents and datasets. 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abundance of proteins and the differential expression of miRNA:mRNA in beta cells treated with FhHDM-1 under basal and apoptotic/inflammatory conditions\n\nSupplementary Table 3.1. Proteins with significantly different abundance in FhHDM-1 treated β-cells compared to untreated controls\n\nSupplementary Table 4.1. Predicted gene targets for differentially expressed miRNAs in FhHDM-1 treated β-cells compared to untreated (Un) controls\n\nSupplementary Table 4.2. Predicted gene targets of differentially expressed miRNAs in FhHDM-1 treated β-cells compared to untreated (Un) controls, common across all online miRNA gene target prediction tools mIRDB, DIANA and Target Scan\n\nSupplementary Table 4.3A. List of predicted gene targets from miRNA upregulated in FhHDM-1 treated β-cells within each PANTHER DB category of molecular function and biological process (from Figure 2).\n\nSupplementary Table 4.3B. KEGG pathway analysis of predicted gene targets from miRNA downregulated in FhHDM-1 treated β-cells compared to untreated controls\n\nSupplementary Table 4.4. List of matched gene targets from miRNA downregulated in FhHDM-1 treated β-cells within each PANTHER DB category of molecular function and biological process (from Figure 4.5).\n\nSupplementary Table 4.5. Differentially expressed miRNAs in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines, compared to cytokines alone\n\nSupplementary Table 4.6. Predicted gene targets for differentially expressed miRNAs in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cytokines (CM) alone\n\nSupplementary Table 4.7. Predicted gene targets of differentially expressed miRNAs in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cytokines (CM) alone, common across all online miRNA gene target prediction tools mIRDB, DIANA and Target Scan\n\nSupplementary Table 4.8. KEGG pathway analysis of predicted gene targets from miRNA upregulated in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cytokines alone\n\nSupplementary Table 4.9. KEGG pathway analysis of predicted gene targets from miRNA downregulated in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cyokines alone\n\nSupplementary Table 4.10. Differentially expressed genes in the transcriptome of FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines, compared to cytokines alone\n\nSupplementary Table 4.11. Matched gene targets common between transcriptome and predicted gene targets of differentially expressed miRNA in FhHDM-1 treated β-cells under inflammatory conditions compared to pro-inflammatory cytokine only (CM) controls.\n\nSupplementary Table 4.12. KEGG pathway analysis of matched genes downregulated in the transcriptome with their corresponding regulatory miRNA upregulated in FhHDM-1 β-cells exposed to pro-inflammatory cytokines compared to cytokines alone\n\nSupplementary Table 4.13. 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It has been developed to provide a large-scale set of datasets for the training, benchmarking and competitive development of classical and quantum algorithms for common tasks in quantum sciences, including quantum control, quantum tomography and noise spectroscopy.\n\nIt has been generated using customised code drawing upon base-level Python packages in order to facilitate interoperability and portability across common machine learning and quantum programming platforms. Each dataset consists of 10,000 samples which in turn comprise a range of data relevant to the training of machine learning algorithms for solving optimisation problems.\n\nThe data includes a range of information (stored in list, matrix or tensor format) regarding quantum systems and their evolution, such as: quantum state vectors, drift and control Hamiltonians and unitaries, Pauli measurement distributions, time series data, pulse sequence data for square and Gaussian pulses and noise and distortion data. 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We also provide working examples of how to use the QDataSet in practice and its use in benchmarking certain algorithms.\n\nThe associated paper provides in-depth detail on the QDataSet for researchers who may be unfamiliar with quantum computing, together with specifications for domain experts within quantum engineering, quantum computation and quantum machine learning.\n\nThe dataset details are set out in the link to raw files.\n\n\n" } ], "http://schema.org/funder": [ { "@value": "" } ], "http://schema.org/hasPart": [ { "@id": "https://data.research.uts.edu.au/api/object/0a5ced406a603943296693967f241226/2108.06661.pdf" } ], "http://schema.org/identifier": [ { "@value": "[70967290a59911eca9a84993133e6628, {\"@id\":\"_:local-id:public_ocfl:70967290a59911eca9a84993133e6628\",\"@type\":\"PropertyValue\",\"value\":\"70967290a59911eca9a84993133e6628\",\"name\":\"public_ocfl\"}]" }, { "@id": "https://w3id.org/np/RAIvSVScGx3gdwGiNP-SZA6hewBf0v5NQZ3kHgXRLUg-A/_1" } ], "http://schema.org/keywords": [ { "@value": "machine learning" }, { "@value": "quantum" } ], "http://schema.org/license": [ { "@value": "\"_:license/http://creativecommons.org/licenses/by/3.0/au\"" } ], "http://schema.org/memberOf": [ { "@id": "arcp://name,uts_public_data_repo/uts_root_collection" } ], "http://schema.org/name": [ { "@value": "QDataSet: Quantum Datasets for Machine Learning" } ], "http://schema.org/publisher": [ { "@id": "https://www.uts.edu.au/" } ], "http://schema.org/temporalCoverage": [ { "@value": "2021-01-01/2021-01-01" } ], "http://schema.org/yearCreated": [ { "@value": "2022" } ], "http://schema.org/yearPublished": [ { "@value": "2022" } ] }, { "@id": "https://arxiv.org/abs/2108.06661", "@type": [ "http://schema.org/ScholarlyArticle" ], "http://schema.org/identifier": [ { "@value": "https://arxiv.org/abs/2108.06661" } ], "http://schema.org/name": [ { "@value": "QDataset: Quantum Datasets for Machine Learning" } ] }, { "@id": "https://data.research.uts.edu.au/api/object/Christopher.Ferrie@uts.edu.au", "@type": [ "http://schema.org/Person" ], "http://schema.org/affiliation": [ { "@id": "https://www.uts.edu.au/" } ], "http://schema.org/email": [ { "@value": "Christopher.Ferrie@uts.edu.au" } ], "http://schema.org/familyName": [ { "@value": "Ferrie" } ], "http://schema.org/givenName": [ { "@value": "Christopher" } ], "http://schema.org/identifier": [ { "@value": "Christopher.Ferrie@uts.edu.au" } ], "http://schema.org/name": [ { "@value": "Christopher Ferrie" } ] }, { "@id": "https://data.research.uts.edu.au/api/object/akram.youssry@eng.asu.edu.eg", "@type": [ "http://schema.org/Person" ], "http://schema.org/affiliation": [ { "@id": "https://www.uts.edu.au/" } ], "http://schema.org/email": [ { "@value": "akram.youssry@eng.asu.edu.eg" } ], "http://schema.org/familyName": [ { "@value": "Youssry" } ], "http://schema.org/givenName": [ { "@value": "Akram" } ], "http://schema.org/identifier": [ { "@value": "akram.youssry@eng.asu.edu.eg" } ], "http://schema.org/name": [ { "@value": "Akram Youssry" } ] }, { "@id": "https://data.research.uts.edu.au/api/object/dacheng.tao@sydney.edu.au", "@type": [ "http://schema.org/Person" ], "http://schema.org/affiliation": [ { "@id": "https://www.uts.edu.au/" } ], "http://schema.org/email": [ { "@value": "dacheng.tao@sydney.edu.au" } ], "http://schema.org/familyName": [ { "@value": "Tao" } ], "http://schema.org/givenName": [ { "@value": "Dacheng" } ], "http://schema.org/identifier": [ { "@value": "dacheng.tao@sydney.edu.au" } ], "http://schema.org/name": [ { "@value": "Dacheng Tao" } ] }, { "@id": "https://data.research.uts.edu.au/api/object/elija.t.perrier@student.uts.edu.au", "@type": [ "http://schema.org/Person" ], "http://schema.org/affiliation": [ { "@id": "https://www.uts.edu.au/" } ], "http://schema.org/contactPoint": [ { "@value": "_:contact/elija.t.perrier@student.uts.edu.au" } ], "http://schema.org/email": [ { "@value": "elija.t.perrier@student.uts.edu.au" } ], "http://schema.org/familyName": [ { "@value": "Perrier" } ], "http://schema.org/givenName": [ { "@value": "Elija" } ], "http://schema.org/identifier": [ { "@value": "elija.t.perrier@student.uts.edu.au" } ], "http://schema.org/name": [ { "@value": "Elija Perrier" } ] }, { "@id": "https://data.research.uts.edu.au/api/object/0a5ced406a603943296693967f241226/", "@type": [ "http://schema.org/Dataset" ], "http://schema.org/description": [ { "@value": "" } ], "http://schema.org/name": [ { "@value": "0a5ced406a603943296693967f241226" } ] }, { "@id": "https://data.research.uts.edu.au/api/object/_cloudstor", "@type": [ "http://schema.org/Dataset" ], "http://schema.org/description": [ { "@value": "The data is publicly available via the URL: https://cloudstor.aarnet.edu.au/plus/s/rxYKXBS7Tq0kB8o, If this is no longer accessible, please contact elija.t.perrier@student.uts.edu.au.\n" } ], "http://schema.org/encodingFormat": [ { "@value": "The data is described below:\n\nG 1q X : (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: none; (iv) No\ndistortion.\n\nG 1q X D : (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: none; (iv)\nDistortion.\n\nG 1q XY: (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussian; (iii) Noise: none;\n(iv) No distortion.\n\nG 1q XY D : (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussian; (iii) Noise: none;\n(iv) Distortion.\n\nG 1q XY XZ N1N5: (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussian; (iii) Noise:\nN1 on x-axis, N5 on z-axis; (iv) No distortion.\n\nG 1q XY XZ N1N5 D: (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussian; (iii)\nNoise: N1 on x-axis, N5 on z-axis; (iv) No distortion.\n\nG 1q XY XZ N1N6: (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussian; (iii) Noise:\nN1 on x-axis, N6 on z-axis; (iv) Distortion.\n\nG 1q XY XZ N1N6 D: (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussian; (iii)\nNoise: N1 on x-axis, N6 on z-axis; (iv) No distortion.\n\nG 1q XY XZ N3N6: (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussian; (iii) Noise:\nN1 on x-axis, N6 on z-axis; (iv) Distortion.\n\nG 1q XY XZ N3N6 D: (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussian; (iii)\nNoise: N1 on x-axis, N6 on z-axis; (iv) No distortion.\n\nG 1q X Z N1 : (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: N1 on z-axis;\n(iv) No distortion.\n\nG 1q X Z N1 D: (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: N1 on z-axis;\n(iv) Distortion.\n\nG 1q X Z N2: (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: N2 on z-axis;\n(iv) No distortion.\n\nG 1q X Z N2 D: (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: N2 on z-axis;\n(iv) Distortion.\n\nG 1q X Z N3: (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: N3 on z-axis;\n(iv) No distortion.\n\nG 1q X Z N3 D: (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: N3 on z-axis;\n(iv) Distortion.\n\nG 1q X Z N4: (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: N4 on z-axis;\n(iv) No distortion.\n\nG 1q X Z N4 D: (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: N4 on z-axis;\n(iv) Distortion.\n\nG 2q IX-XI IZ-ZI N1-N6: (i) Qubits: two; (ii) Control: x-axis on both qubits, Gaussian;\n(iii) Noise: N1 and N6 z-axis on each qubit; (iv) No distortion.\n\nG 2q IX-XI IZ-ZI N1-N6 D: (i) Qubits: two; (ii) Control: x-axis on both qubits,\nGaussian; (iii) Noise: N1 and N6 z-axis on each qubit; (iv) Distortion.\n\nG 2q IX-XI-XX: (i) Qubits: two; (ii) Control: single x-axis control on both qubits and\nx-axis interacting control, Gaussian; (iii) Noise: none; (iv) No distortion.\n\nG 2q IX-XI-XX D: (i) Qubits: two; (ii) Control: single x-axis control on both qubits and\nx-axis interacting control, Gaussian; (iii) Noise: none; (iv) Distortion.\n\nG 2q IX-XI-XX IZZI N1-N5: (i) Qubits: two; (ii) Control: single x-axis control on both\nqubits and x-axis interacting control, Gaussian; (iii) Noise: N1 and N5 on z-axis\nnoise on each qubit; (iv) No distortion.\n\nG 2q IX-XI-XX IZZI N1-N5: (i) Qubits: two; (ii) Control: single x-axis control on both\nqubits and x-axis interacting control, Gaussian; (iii) Noise: N1 and N5 on z-axis\nnoise on each qubit; (iv) Distortion.\n\nS 1q X: (i) Qubits: one; (ii) Control: x-axis, square; (iii) Noise: none; (iv) No distortion.\n\nS 1q X D: (i) Qubits: one; (ii) Control: x-axis, Gaussquaresian; (iii) Noise: none; (iv)\nDistortion.\n\nS 1q XY: (i) Qubits: one; (ii) Control: x-axis and y-axis, square; (iii) Noise: none; (iv)\nNo distortion.\n\nS 1q XY D: (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussquaresian; (iii) Noise:\nnone; (iv) Distortion.\n\nS 1q XY XZ N1N5: (i) Qubits: one; (ii) Control: x-axis and y-axis, square; (iii) Noise: N1\non x-axis, N5 on z-axis; (iv) No distortion.\n\nS 1q XY XZ N1N5 D: (i) Qubits: one; (ii) Control: x-axis and y-axis, Gaussian; (iii)\nNoise: N1 on x-axis, N5 on z-axis; (iv) No distortion.\n\nS 1q XY XZ N1N6: (i) Qubits: one; (ii) Control: x-axis and y-axis, square; (iii) Noise: N1\non x-axis, N6 on z-axis; (iv) Distortion.\n\nS 1q XY XZ N1N6 D: (i) Qubits: one; (ii) Control: x-axis and y-axis, square; (iii) Noise:\nN1 on x-axis, N6 on z-axis; (iv) No distortion.\n\nS 1q XY XZ N3N6: (i) Qubits: one; (ii) Control: x-axis and y-axis, square; (iii) Noise:\nN1 on x-axis, N6 on z-axis; (iv) Distortion.\n\nS 1q XY XZ N3N6 D: (i) Qubits: one; (ii) Control: x-axis and y-axis, square; (iii) Noise:\nN1 on x-axis, N6 on z-axis; (iv) No distortion.\n\nS 1q X Z N1: (i) Qubits: one; (ii) Control: x-axis, square; (iii) Noise: N1 on z-axis; (iv)\nNo distortion.\n\nS 1q X Z N1 D: (i) Qubits: one; (ii) Control: x-axis, square; (iii) Noise: N1 on z-axis; (iv)\nDistortion.\n\nS 1q X Z N2 : (i) Qubits: one; (ii) Control: x-axis, square; (iii) Noise: N2 on z-axis; (iv)\nNo distortion.\n\nS 1q X Z N2 D: (i) Qubits: one; (ii) Control: x-axis, Gaussian; (iii) Noise: N2 on z-axis;\n(iv) Distortion.\n\nS 1q X Z N3: (i) Qubits: one; (ii) Control: x-axis, square; (iii) Noise: N3 on z-axis; (iv)\nNo distortion.\n\nS 1q X Z N3 D: (i) Qubits: one; (ii) Control: x-axis, square; (iii) Noise: N3 on z-axis; (iv)\nDistortion.\n\nS 1q X Z N4: (i) Qubits: one; (ii) Control: x-axis, square; (iii) Noise: N4 on z-axis; (iv)\nNo distortion.\n\nS 1q X Z N4 D: (i) Qubits: one; (ii) Control: x-axis, square; (iii) Noise: N4 on z-axis;\n(iv) Distortion.\n\nS 2q IX-XI IZ-ZI N1-N6: (i) Qubits: two; (ii) Control: x-axis on both qubits, square; (iii)\nNoise: N1 and N6 z-axis on each qubit; (iv) No distortion.\n\nS 2q IX-XI IZ-ZI N1-N6 D: (i) Qubits: two; (ii) Control: x-axis on both qubits, square;\n(iii) Noise: N1 and N6 z-axis on each qubit; (iv) Distortion.\n\nS 2q IX-XI-XX: (i) Qubits: two; (ii) Control: single x-axis control on both qubits and\nx-axis interacting control, square; (iii) Noise: none; (iv) No distortion.\n\nS 2q IX-XI-XX D: (i) Qubits: two; (ii) Control: single x-axis control on both qubits and\nx-axis interacting control, square; (iii) Noise: none; (iv) Distortion.\n\nS 2q IX-XI-XX IZZI N1-N5: (i) Qubits: two; (ii) Control: x-axis on both qubits and xaxis interacting control, square; (iii) Noise: N1 and N5 z-axis on each qubit; (iv) No\ndistortion.\n\nS 2q IX-XI-XX IZZI N1-N5 D: (i) Qubits: two; (ii) Control: x-axis on both qubits and xaxis interacting control, square; (iii) Noise: N1 and N5 z-axis on each qubit; (iv)\nDistortion.\n\nS 2q IX-XI-XX IZZI N1-N6: (i) Qubits: two; (ii) Control: x-axis on both qubits and xaxis interacting control, square; (iii) Noise: N1 and N6 z-axis on each qubit; (iv) No\ndistortion.\n\nS 2q IX-XI-XX IZZI N1-N6 D: (i) Qubits: two; (ii) Control: x-axis on both qubits and xaxis interacting control, square; (iii) Noise: N1 and N6 z-axis on each qubit; (iv)\nDistortion." } ], "http://schema.org/hasPart": [ { "@value": "" } ], "http://schema.org/name": [ { "@value": "Link to Raw Files" } ] }, { "@id": "https://data.research.uts.edu.au/api/object/0a5ced406a603943296693967f241226/2108.06661.pdf", "@type": [ "http://schema.org/MediaObject" ], "http://schema.org/encodingFormat": [ { "@value": "application/pdf" } ], "http://schema.org/name": [ { "@value": "2108.06661.pdf" } ] }, { "@id": "https://w3id.org/np/RAIvSVScGx3gdwGiNP-SZA6hewBf0v5NQZ3kHgXRLUg-A/_1", "@type": [ "http://schema.org/PropertyValue" ], "http://schema.org/name": [ { "@value": "repository" } ], "http://schema.org/value": [ { "@value": "arcp://name,uts_public_data_repo/70967290a59911eca9a84993133e6628" } ] }, { "@id": "arcp://name,uts_public_data_repo/uts_root_collection", "@type": [ "http://pcdm.org/models#Collection", "http://schema.org/Dataset" ], "http://purl.org/dc/terms/conformsTo": [ { "@id": "https://w3id.org/ldac/profile#Collection" } ], "http://schema.org/description": [ { "@value": "This is the UTS Research Data Portal. 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Since then, many resources (e.g. policy documents, scientific articles, data sets) relevant for national adaptation have been published, yet often remain unknown to, or inaccessible, for the multiple stakeholders that comprise the climate adaptation community (CCA) in Portugal. The application of FAIR (findability, accessibility, interoperability and reusability) principles to these resources has the potential to improve their discoverability, accessibility, and usability and ultimately promote a more effective climate adaptation planning in Portugal. \n\nThis study explores the state of the art of scientific research on climate change adaptation in Portugal since 2010, to assess strengths and limitations of knowledge, identifying key priority areas for future research and the “FAIRification” of these resources. As the frequency and intensity of extreme heat events in Portugal increases, this study focused on heat-related hazards, and associated impacts. A systematic search was performed across Scopus and ISI Web of Science to identify relevant peer-reviewed articles between January 2010 and May 2025. The resulting publications were analysed by geographical scope, adaptation sectorial focus, knowledge sector, climate hazard and CCA needs. Alignment of the current scientific outputs with FAIR principles was also analysed. \n\nA total of 217 articles were reviewed. Research was mostly performed at the city or national level (37% each) and the majority of adaptation actions were done under the agricultural or urban sectors (19% and 26%, respectively). Research was multidisciplinary, covering different knowledge sectors, from health impacts to biodiversity, buildings and construction, and often included several knowledge sectors in the same article. All articles presented accessible metadata but only one made its scripts and data available for further replication. \n\nResults show that scientific research on heat-related climate change adaptation in Portugal is diverse and covers many knowledge areas, but the availability of its data needs improvement. Further work will include policy FAIR assessment of policy documents and datasets. 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Proteins with significantly different abundance in FhHDM-1 treated β-cells compared to untreated controls\n\nSupplementary Table 4.1. Predicted gene targets for differentially expressed miRNAs in FhHDM-1 treated β-cells compared to untreated (Un) controls\n\nSupplementary Table 4.2. Predicted gene targets of differentially expressed miRNAs in FhHDM-1 treated β-cells compared to untreated (Un) controls, common across all online miRNA gene target prediction tools mIRDB, DIANA and Target Scan\n\nSupplementary Table 4.3A. List of predicted gene targets from miRNA upregulated in FhHDM-1 treated β-cells within each PANTHER DB category of molecular function and biological process (from Figure 2).\n\nSupplementary Table 4.3B. KEGG pathway analysis of predicted gene targets from miRNA downregulated in FhHDM-1 treated β-cells compared to untreated controls\n\nSupplementary Table 4.4. List of matched gene targets from miRNA downregulated in FhHDM-1 treated β-cells within each PANTHER DB category of molecular function and biological process (from Figure 4.5).\n\nSupplementary Table 4.5. Differentially expressed miRNAs in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines, compared to cytokines alone\n\nSupplementary Table 4.6. Predicted gene targets for differentially expressed miRNAs in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cytokines (CM) alone\n\nSupplementary Table 4.7. Predicted gene targets of differentially expressed miRNAs in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cytokines (CM) alone, common across all online miRNA gene target prediction tools mIRDB, DIANA and Target Scan\n\nSupplementary Table 4.8. KEGG pathway analysis of predicted gene targets from miRNA upregulated in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cytokines alone\n\nSupplementary Table 4.9. KEGG pathway analysis of predicted gene targets from miRNA downregulated in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cyokines alone\n\nSupplementary Table 4.10. Differentially expressed genes in the transcriptome of FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines, compared to cytokines alone\n\nSupplementary Table 4.11. Matched gene targets common between transcriptome and predicted gene targets of differentially expressed miRNA in FhHDM-1 treated β-cells under inflammatory conditions compared to pro-inflammatory cytokine only (CM) controls.\n\nSupplementary Table 4.12. KEGG pathway analysis of matched genes downregulated in the transcriptome with their corresponding regulatory miRNA upregulated in FhHDM-1 β-cells exposed to pro-inflammatory cytokines compared to cytokines alone\n\nSupplementary Table 4.13. 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The benchmark metal transfer modes include three groups, i.e., contact, free flight and controlled. In each group, there are four modes. In Group 1 (contact transfer), the four modes are pure liquid bridge (1b2), forced contact (1c3), repel globular (1g4) and gasless explosive (1e10). In Group 2 (free flight transfer), the four modes are repel globular (2g4), forces spray (2s2), pure stream (2t2) and gasless explosive (2e10). In Group 3 (controlled transfer), the four modes are pure contact (3c2), forced pulse (3p3), projected spray (3s7) and gasless explosive (3e10). Four samples were welded at each mode, hence there are a total of 3 (group) × 4 (modes in each group) × 4 (sample for each mode) = 48 samples in the database. \n\nIn the experiments, a Lincoln Electric PowerWave C300 welder was used with the torch being positive and the negative cable being connected to the workbench. During welding, the torch was fixed and the workbench was moving at a travel speed that was controlled by a servo motor. A GRAS 40PH free field microphone was hanged 0.6 m above the weld pool to record the welding sound. The microphone was connected to an NI 9234 Sound and Vibration module. An LEM HTA 300-S current sensor was installed around the torch cable to measure the welding current through the torch. The current sensor was connected to an NI 9215 Analogue Voltage Input module. \n\nBoth the NI 9234 and NI 9215 modules were installed in an NI cDAQ 9185 chassis, which was connected to a desktop computer. The computer and the data acquisition system was in the black cabinet on the right of the photo in Fig. 6 and therefore is not shown here. A software with a user interface was developed with LabWindows/CVI 2017 to acquire, display and store the signals in synchronisation. The sampling rate for the sound and current/voltage signals are 51.2 kHz and 3.2 kHz, respectively. In the experiments, the pure argon was used as the shielding gas and the diameter of the feeding wire was 0.9 mm. The workpiece metal was carbon steel.\n" } ], "http://schema.org/funder": [ { "@id": "https://w3id.org/np/RAPC9X3W2VOaopb7CeQeIERfoYDlB2Qdw3n2-3fHAnxzY/_3" } ], "http://schema.org/identifier": [ { "@value": "32b4622bb5b77b9523bf97b8af09c133" }, { "@id": "https://w3id.org/np/RAPC9X3W2VOaopb7CeQeIERfoYDlB2Qdw3n2-3fHAnxzY/_4" }, { "@id": "https://w3id.org/np/RAPC9X3W2VOaopb7CeQeIERfoYDlB2Qdw3n2-3fHAnxzY/_5" } ], "http://schema.org/keywords": [ { "@value": "Welding" }, { "@value": "acoustics" }, { "@value": "signal processing" } ], "http://schema.org/license": [ { "@id": "https://w3id.org/np/RAPC9X3W2VOaopb7CeQeIERfoYDlB2Qdw3n2-3fHAnxzY/_6" } ], "http://schema.org/memberOf": [ { "@id": "arcp://name,uts_public_data_repo/uts_root_collection" } ], "http://schema.org/name": [ { "@value": "Welding sound database for benchmark metal transfer modes in gas metal arc welding" } ], "http://schema.org/publisher": [ { "@id": "https://www.uts.edu.au/" } ], "http://schema.org/yearCreated": [ { "@value": "2020" } ], "http://schema.org/yearPublished": [ { "@value": "2020" } ] }, { "@id": "https://w3id.org/np/RAPC9X3W2VOaopb7CeQeIERfoYDlB2Qdw3n2-3fHAnxzY/_1", "@type": [ "http://schema.org/StructuredValue" ], "http://schema.org/identifier": [ { "@value": "_:FOR/091301" } ], "http://schema.org/name": [ { "@value": "091301 - Acoustics and Noise Control (excl. 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"http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/eng/catalog.search#/metadata/ef796898-5baf-465f-9eef-4aebb4e7e3c9" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-3489-268X" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-10 15:05:26.439415+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:53.380074+00:00" } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "Canal Grande 2013 marine litter distribution" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-10 15:05:26.439415+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/38a82b77-26c6-44cb-bd3e-972daf295b74", "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/VideoObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/38a82b77-26c6-44cb-bd3e-972daf295b74" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-11 09:30:44.441430+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:56.016359+00:00" } ], "http://schema.org/description": [ { "@value": "As part of the European MAELSTROM project, a combined approach using bathymetric analysis and underwater video inspections was applied to investigate marine litter (ML) accumulation on the seafloor in the urban area of Sacca Fisola (SF). The integration of multibeam echosounder (MBES) data with diver-operated video transects allowed not only the visual validation of acoustic results but also a more detailed mapping of litter items. During a 100-metre transect conducted by professional divers on 12 July 2022, 34 marine litter objects were identified and classified. This multiparametric strategy enabled the spatial georeferencing of debris and an estimate of their percentage coverage on the seafloor." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "Video Inspections - Sacca Fisola 2022" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-11 09:30:44.441430+00:00" } ] }, { "@id": "mailto:taha.lahami@ve.ismar.cnr.it", "@type": [ "http://xmlns.com/foaf/0.1/Agent" ], "http://schema.org/affiliation": [ { "@value": "CNR ISMAR" } ], "http://schema.org/email": [ { "@value": "taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/name": [ { "@value": "Taha Lahami" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/457123f4-edad-47f2-9462-455cae51f913", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/457123f4-edad-47f2-9462-455cae51f913" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-05-27 09:01:43.184503+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:57.414475+00:00" } ], "http://schema.org/description": [ { "@value": "As part of the European MAELSTROM project, passive acoustic monitoring (PAM) was conducted using a hydrophone deployed before and after the cleaning campaign carried out with a robotic platform at the Mussel Farm site. The primary aim of this acoustic monitoring was to analyze underwater soundscapes to detect the presence of soniferous organisms, with particular attention to fish and potentially invertebrate components" } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "Passive Acoustic Monitoring (PAM) - Mussel Farm 2022/2024" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-05-27 09:01:43.184503+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/60aa5a5d-33bf-4610-9384-7493dc6168de", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/60aa5a5d-33bf-4610-9384-7493dc6168de" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-06 08:49:32.624739+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:49.369364+00:00" } ], "http://schema.org/description": [ { "@value": "The ecological assessment of the Ave Estuary has been conducted since the beginning of the MAELSTROM project, covering the Spring and Autumn periods each year (seasons with the highest biological activity for this type of water body). After the installation of the Bubble Barrier, ecological assessments were carried out seasonally to monitor the ecosystem's progression following the implementation of the waste collection technology." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "MAELSTROM: Ecological assessment campaigns in the Ave River estuary in Portugal (pre and post cleaning)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-06 08:49:32.624739+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/7436a689-b929-4ed0-a06e-0fc8b9df36e5", "@type": [ "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject", "http://schema.org/VideoObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/7436a689-b929-4ed0-a06e-0fc8b9df36e5" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-11 09:29:17.874825+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:55.639252+00:00" } ], "http://schema.org/description": [ { "@value": "As part of the MAELSTROM project, a video survey was conducted on 23 September 2022 at the abandoned mussel farm (MF) site in the coastal area outside the Venetian lagoon. The purpose of the survey was to visually document and classify marine debris items on the seabed and to support the validation of previous bathymetric analyses. The inspection revealed several types of debris, including mooring structures, ropes, and derelict fishing gear, elements that are often challenging to detect through remote sensing techniques alone." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "Video Inspections - Mussel Farm 2022" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-11 09:29:17.874825+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/83b154e3-a8a5-4cc4-9666-4a3ae447a27b", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/83b154e3-a8a5-4cc4-9666-4a3ae447a27b" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-06 12:33:37.643035+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:51.347699+00:00" } ], "http://schema.org/description": [ { "@value": "Soft bottom macrozoobenthic communities were characterized in Sacca Fisola. In the lagoon site, three stations were defined: LV1 (on the planned cleaning area), LV2 (control) and LV3 (additional, in case more information would have been required). The protocol includes sampling by Van Veen grab (surface area of 0.1 m2, volume of approx. 17 L), taxonomic determination and quantitative analysis of the community matrix." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "MAELSTROM: Soft bottom macrozoobenthos - Sacca Fisola 2022 (pre-cleaning)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-06 12:33:37.643035+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/880ae033-6c52-406d-b10f-b44d05d65a4e", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/880ae033-6c52-406d-b10f-b44d05d65a4e" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-06 12:07:02.510715+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:50.578510+00:00" } ], "http://schema.org/description": [ { "@value": "Soft bottom macrozoobenthic communities were characterized in Mussel Farm. In the coastal site, three stations were defined: CS1 (planned cleaning area and discontinued in September 2023), CS2 (control) and CS4 (cleaning area since september 2023). The protocol includes sampling by Van Veen grab (surface area of 0.1 m2, volume of approx. 17 L), taxonomic determination and quantitative analysis of the community matrix." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "Soft bottom macrozoobenthos - Mussel Farm 2023 (pre-cleaning)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-06 12:07:02.510715+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/8cc5e139-1ec0-4c75-acc2-60fa103ca79d", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/8cc5e139-1ec0-4c75-acc2-60fa103ca79d" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-10 07:31:35.283951+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:52.621591+00:00" } ], "http://schema.org/description": [ { "@value": "The bathymetry was acquired in 2013 in the Grand Canal (Venice) as part of the RITMARE (La Ricerca ITaliana per il MARE) research project. The data were collected using a Kongsberg EM-2040 dual-head multibeam system operating at 360 kHz, mounted on the CNR Litus vessel, and subsequently processed with Kongsberg SIS software. The information acquired was integrated with sound velocity profiles and georeferenced using the Seapath 300 positioning system. This survey will serve as a basis for comparison with data collected ten years later, in 2023, in order to assess any morphological and sedimentological changes in the bottom of the Grand Canal." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "Canal Grande 2013 Bathymetry" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-10 07:31:35.283951+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/a48d47e6-6bda-4cae-a5bb-c71c3a5f6cf0", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/a48d47e6-6bda-4cae-a5bb-c71c3a5f6cf0" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-06 08:51:27.045277+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:49.718170+00:00" } ], "http://schema.org/description": [ { "@value": "In the Ave River estuary, monitoring of Floating Macro-Litter (FML) was undertaken before (June 2021 - December 2023) and after the installation of the Bubble Barrier remediation technology in order to assess its effectiveness.\nEvaluation of FML followed standard European guidelines (Hanke et al., 2013, 2023) and was conducted through visual monitoring to assess the diversity and relative abundance of debris in the area. Monitoring campaigns were carried out monthly from June 2021 to December 2024. FML observations were performed using binoculars (during ebb tide at three designated locations in the estuary—downstream, midstream, and upstream.\n\nAll three observation points were positioned at the same elevation along the estuary margin, approximately 2 meters above the water surface (mean sea level), ensuring a consistent viewing angle for reliable comparisons between locations. Although some FML objects may have been partially obscured by wind-generated ripples or small waves from passing boats, this impact was minimized as observations were conducted on calm days with high visibility.\n\nMore details on the methodology can be found in D5.4\n\nMore details on the results can be found in D2.4." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "Monitoring of Floating Macro-Litter in the Ave River estuary in Portugal (pre and post the cleaning)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-06 08:51:27.045277+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/a600e3a4-9b02-40a8-ab01-590dd238015c", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/a600e3a4-9b02-40a8-ab01-590dd238015c" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-06 12:37:11.630649+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:52.203234+00:00" } ], "http://schema.org/description": [ { "@value": "During the marine litter cleanup activities at the lagoon site, fouling organisms were sampled from a subset of the collected litter items. From each item, a 0.1 m² area was scraped from the surface exposed to epifaunal colonization. For the lagoon site, 5 items (4 tires and a metal frame) were subject to scraping on 26-27/09/2022." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "MAELSTROM: Marine litter fouling communities - Sacca Fisola 2022" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-06 12:37:11.630649+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/af2484fc-7929-4034-bca8-7895894cbb8b", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/af2484fc-7929-4034-bca8-7895894cbb8b" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-06 12:36:29.777018+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:51.732843+00:00" } ], "http://schema.org/description": [ { "@value": "Soft bottom macrozoobenthic communities were characterized in Sacca Fisola. In the lagoon site, three stations were defined: LV1 (on the planned cleaning area), LV2 (control) and LV3 (additional, in case more information would have been required). The protocol includes sampling by Van Veen grab (surface area of 0.1 m2, volume of approx. 17 L), taxonomic determination and quantitative analysis of the community matrix." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "MAELSTROM: Soft bottom macrozoobenthos - Sacca Fisola 2023/2024 (post-cleaning)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-06 12:36:29.777018+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/c12f5031-7449-40e1-b41e-068075f0f0f8", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/c12f5031-7449-40e1-b41e-068075f0f0f8" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-05-28 15:02:28.709570+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:48.563190+00:00" } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "MAELSTROM: Assessment of fish community in lagoon area, Sacca Fisola (Post-cleaning Survey)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-05-28 15:02:28.709570+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/c6779901-f2a5-4740-9791-0e712fa9e810", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "https://orcid.org/0000-0002-3489-268X" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/c6779901-f2a5-4740-9791-0e712fa9e810" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-3489-268X" } ], "http://schema.org/dateCreated": [ { "@value": "2024-12-20 15:11:07.324123+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-06-10 10:57:06.842589+00:00" } ], "http://schema.org/description": [ { "@value": "For this project, two study areas were selected and both are characterized by the presence of marine litter that has accumulated over time: a lagoon area (Sacca Fisola) and a coastal area, the latter located on an abandoned mussel farm.The lagoon site of Sacca Fisola is situated in an area where waste accumulates in substantial quantities. Consequently, the channel's seabed is marked by a significant presence of waste. Some of these waste items are buried beneath layers of sediment, while many others remain visible on the surface and can be identified using the bathymetric map generated from MultiBeam EchoSounder (MBES) data." } ], "http://schema.org/license": [ { "@id": "https://choosealicense.com/no-permission/" } ], "http://schema.org/name": [ { "@value": "Macrolitter detection on the seafloor - Sacca Fisola 2022 (post-cleaning)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2024-12-20 15:11:07.324123+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/ca2f4c3d-f4b3-4562-bf27-e955159f9cf9", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": 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From each item, a 0.1 m² area was scraped from the surface exposed to epifaunal colonization. In the coastal site, samples of sufficient quality could be acquired during May-June 2023 cleaning operations, 2 plastic buoys retained near the seabed and a section of synthetic rope, all of them leftover aquaculture equipment." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "Marine litter fouling communities - Mussel Farm 2023" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-06 12:08:38.478285+00:00" } ] }, { "@id": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/ec3ab102-f183-4f02-84fd-d64106c283ad", "@type": [ "http://purl.org/wf4ever/wf4ever#Dataset", "http://purl.org/wf4ever/wf4ever#Resource", "http://schema.org/MediaObject" ], "http://schema.org/author": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/contentUrl": [ { "@value": "http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/ec3ab102-f183-4f02-84fd-d64106c283ad" } ], "http://schema.org/creator": [ { "@id": "mailto:taha.lahami@ve.ismar.cnr.it" } ], "http://schema.org/dateCreated": [ { "@value": "2025-06-06 12:05:09.250864+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-14 08:33:50.129830+00:00" } ], "http://schema.org/description": [ { "@value": "Soft bottom macrozoobenthic communities were characterized in Mussel Farm. In the marine site, three stations were defined: CS1 (planned cleaning area and discontinued in September 2023), CS2 (control) and CS4 (cleaning area since september 2023). The protocol includes sampling by Van Veen grab (surface area of 0.1 m2, volume of approx. 17 L), taxonomic determination and quantitative analysis of the community matrix." } ], "http://schema.org/license": [ { "@id": "https://creativecommons.org/licenses/by/4.0/legalcode" } ], "http://schema.org/name": [ { "@value": "Soft bottom macrozoobenthos - Mussel Farm 2023/2024 (post-cleaning)" } ], "http://schema.org/sdDatePublished": [ { "@value": "2025-06-06 12:05:09.250864+00:00" } ] }, { "@id": "http://www.ismar.cnr.it/", "@type": [ "http://schema.org/Organization" ] }, { "@id": "https://w3id.org/ro-id/9d238d26-7ebd-4517-974f-f9f91b7055fc/#835693ca-7457-4900-beb3-36085141e813", "@type": [ "http://schema.org/GeoCoordinates" ], "http://schema.org/latitude": [ { "@value": "12.311973568866962" } ], "http://schema.org/longitude": [ { "@value": "45.42473574369795" } ], "http://schema.org/name": [ { "@value": "POINT (12.311973568866962 45.42473574369795)" } ] }, { "@id": 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"http://schema.org/contactPoint": [ { "@id": "https://w3id.org/np/RA8MF1g4qFhRCtzxl3oCUXhBu-W-fJVehUkCgSzmdGUtg/_1" } ], "http://schema.org/contentUrl": [ { "@value": "./" } ], "http://schema.org/creator": [ { "@id": "https://data.research.uts.edu.au/api/object/Bronwyn.Obrien@uts.edu.au" }, { "@id": "https://data.research.uts.edu.au/api/object/Inah.Camaya@uts.edu.au" }, { "@id": "https://data.research.uts.edu.au/api/object/Sheila.Donnelly@uts.edu.au" } ], "http://schema.org/dateCreated": [ { "@value": "2023-07-09T23:42:33.612Z" } ], "http://schema.org/datePublished": [ { "@value": "2023-07-09T23:42:33.612Z" } ], "http://schema.org/description": [ { "@value": "Supplementary files for the PhD thesis of Inah Camaya, relating to the analysis of (1) the differential abundance of proteins in the pancreas of NOD mice treated with FhHDM-1, (2) the differential expression of mRNA in beta cells treated with FhHDM-1 under basal and apoptotic conditions and, (3) the differential expression of miRNA:mRNA in beta cells treated with FhHDM-1 under basal and apoptotic conditions. \n\nSupplementary Figure 2.1. Interlinked contiguous quartet of interacting proteins (Cdh1, Agt, Rps6kb1, and Bmi1) \n\nSupplementary Table 2.1. LC-MS/MS identification of proteins in NOD mouse pancreas\n\nSupplementary Table 2.2. Proteins identified in the pancreas of mice\n\nSupplementary Table 2.3. Interrogation of the protein network using specific search terms\n\nSupplementary Table 2.4. Differentially expressed genes with a logarithmic fold change of >1 or <1, and a false discovery rate <0.05 in NIT-1 β-cells treated with FhHDM-1 as compared to untreated cells\n\n\nSupplementary Table 3.1. Proteins with significantly different abundance in FhHDM-1 treated β-cells compared to untreated controls\n\nSupplementary Table 4.1. Predicted gene targets for differentially expressed miRNAs in FhHDM-1 treated β-cells compared to untreated (Un) controls\n\nSupplementary Table 4.2. Predicted gene targets of differentially expressed miRNAs in FhHDM-1 treated β-cells compared to untreated (Un) controls, common across all online miRNA gene target prediction tools mIRDB, DIANA and Target Scan\n\nSupplementary Table 4.3A. List of predicted gene targets from miRNA upregulated in FhHDM-1 treated β-cells within each PANTHER DB category of molecular function and biological process (from Figure 2).\n\nSupplementary Table 4.3B. KEGG pathway analysis of predicted gene targets from miRNA downregulated in FhHDM-1 treated β-cells compared to untreated controls\n\nSupplementary Table 4.4. List of matched gene targets from miRNA downregulated in FhHDM-1 treated β-cells within each PANTHER DB category of molecular function and biological process (from Figure 4.5).\n\nSupplementary Table 4.5. Differentially expressed miRNAs in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines, compared to cytokines alone\n\nSupplementary Table 4.6. Predicted gene targets for differentially expressed miRNAs in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cytokines (CM) alone\n\nSupplementary Table 4.7. Predicted gene targets of differentially expressed miRNAs in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cytokines (CM) alone, common across all online miRNA gene target prediction tools mIRDB, DIANA and Target Scan\n\nSupplementary Table 4.8. KEGG pathway analysis of predicted gene targets from miRNA upregulated in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cytokines alone\n\nSupplementary Table 4.9. KEGG pathway analysis of predicted gene targets from miRNA downregulated in FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines compared to cyokines alone\n\nSupplementary Table 4.10. Differentially expressed genes in the transcriptome of FhHDM-1 treated β-cells exposed to pro-inflammatory cytokines, compared to cytokines alone\n\nSupplementary Table 4.11. Matched gene targets common between transcriptome and predicted gene targets of differentially expressed miRNA in FhHDM-1 treated β-cells under inflammatory conditions compared to pro-inflammatory cytokine only (CM) controls.\n\nSupplementary Table 4.12. KEGG pathway analysis of matched genes downregulated in the transcriptome with their corresponding regulatory miRNA upregulated in FhHDM-1 β-cells exposed to pro-inflammatory cytokines compared to cytokines alone\n\nSupplementary Table 4.13. KEGG pathway analysis of matched genes upregulated in the transcriptome with their corresponding regulatory miRNA downregulated in FhHDM-1 β-cells exposed to pro-inflammatory cytokines compared to cytokines alone\n" } ], "http://schema.org/identifier": [ { "@value": "c8025bb01eac11ee83d1a16b16f71664" }, { "@id": "https://w3id.org/np/RA8MF1g4qFhRCtzxl3oCUXhBu-W-fJVehUkCgSzmdGUtg/_2" }, { "@id": "https://w3id.org/np/RA8MF1g4qFhRCtzxl3oCUXhBu-W-fJVehUkCgSzmdGUtg/_3" } ], "http://schema.org/keywords": [ { "@value": "Fasciola" }, { "@value": "FhHDM" }, { "@value": "IGF" }, { "@value": "PI3K" }, { "@value": "apoptosis" }, { "@value": "beta cell" }, { "@value": "helminth" }, { "@value": "miRNA" }, { "@value": "type 1 diabetes" } ], "http://schema.org/license": [ { "@id": "https://w3id.org/np/RA8MF1g4qFhRCtzxl3oCUXhBu-W-fJVehUkCgSzmdGUtg/_4" } ], "http://schema.org/memberOf": [ { "@id": "arcp://name,uts_public_data_repo/uts_root_collection" } ], "http://schema.org/name": [ { "@value": "Inah Camaya PhD - Supplementary Data" } ], "http://schema.org/publisher": [ { "@id": "https://www.uts.edu.au/" } ], "http://schema.org/yearCreated": [ { "@value": "2023" } ], "http://schema.org/yearPublished": [ { "@value": "2023" } ] }, { "@id": "https://doi.org/10.26195/yy89-dk26.", "@type": [ "http://schema.org/CreativeWork" ], "http://schema.org/identifier": [ { "@value": "https://doi.org/10.26195/yy89-dk26." } ], "http://schema.org/name": [ { "@value": "DOI" } ] }, { "@id": "https://link.springer.com/article/10.1007/s00109-021-02122-x", "@type": [ "http://schema.org/ScholarlyArticle" ], "http://schema.org/description": [ { "@value": "Camaya I, Mok TY, Lund M, To J, Braidy N, Robinson MW, Santos J, O'Brien B, Donnelly S. 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{ "@id": "mailto:francisca.simoes@edu.ulisboa.pt" } ], "http://schema.org/contentSize": [ { "@value": "0", "@type": "http://www.w3.org/2001/XMLSchema#integer" } ], "http://schema.org/contentUrl": [ { "@value": "https://api.rohub.org/api/ros/302b4ebf-db38-49d5-8ab4-4561181f4e94/crate/download/" } ], "http://schema.org/creator": [ { "@id": "https://orcid.org/0000-0002-2104-3187" } ], "http://schema.org/dateCreated": [ { "@value": "2025-10-13 11:12:49.327063+00:00" } ], "http://schema.org/dateModified": [ { "@value": "2025-10-20 13:33:34.491167+00:00" } ], "http://schema.org/datePublished": [ { "@value": "2025-10-13 11:12:49.327063+00:00" } ], "http://schema.org/description": [ { "@value": "Presentation at the CE3C Annual Meeting, Azores - Portugal\n\n*10-12 October 2025*\n\nThe first Portuguese National Strategy for Adaptation to Climate Change was adopted in 2010, aiming to adjust climate and other sectoral policies, and to increase the countries’ resilience to observed and projected climate change impacts. Since then, many resources (e.g. policy documents, scientific articles, data sets) relevant for national adaptation have been published, yet often remain unknown to, or inaccessible, for the multiple stakeholders that comprise the climate adaptation community (CCA) in Portugal. The application of FAIR (findability, accessibility, interoperability and reusability) principles to these resources has the potential to improve their discoverability, accessibility, and usability and ultimately promote a more effective climate adaptation planning in Portugal. \n\nThis study explores the state of the art of scientific research on climate change adaptation in Portugal since 2010, to assess strengths and limitations of knowledge, identifying key priority areas for future research and the “FAIRification” of these resources. As the frequency and intensity of extreme heat events in Portugal increases, this study focused on heat-related hazards, and associated impacts. A systematic search was performed across Scopus and ISI Web of Science to identify relevant peer-reviewed articles between January 2010 and May 2025. The resulting publications were analysed by geographical scope, adaptation sectorial focus, knowledge sector, climate hazard and CCA needs. Alignment of the current scientific outputs with FAIR principles was also analysed. \n\nA total of 217 articles were reviewed. Research was mostly performed at the city or national level (37% each) and the majority of adaptation actions were done under the agricultural or urban sectors (19% and 26%, respectively). Research was multidisciplinary, covering different knowledge sectors, from health impacts to biodiversity, buildings and construction, and often included several knowledge sectors in the same article. All articles presented accessible metadata but only one made its scripts and data available for further replication. \n\nResults show that scientific research on heat-related climate change adaptation in Portugal is diverse and covers many knowledge areas, but the availability of its data needs improvement. Further work will include policy FAIR assessment of policy documents and datasets. 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A survey, however, suggests a lack of documentation simply explaining modal analysis, from a practical point-of-view, for example for a simple, multi-degree-of-freedom (MDoF) system, using a range of common, different analytical, numerical and experimental methods. This tutorial paper therefore aims to discuss the topic of modal analysis in the con-text of a simple eight-storey tower structure utilising a range of such commonly deployed, industrially relevant approaches. 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