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Dataset results
14 results for “Adaptive reuse”
Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 100505. https://doi.org/10.1016/j.ccs.2023.100505</p> <ul> <li>Date of data collection: 27/11/2018</li> <li>Geographic location of data collection: Salerno, Italy. The venue of the data collection is <em>Salone dei marmi, Palazzo di Città</em>, via Roma, 84121 Salerno, Italy </li> <li>Activity of data collection: Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions </li> <li>Methods for collection/generation of data: see the methodology section in Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 100505. https://doi.org/10.1016/j.ccs.2023.100505</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh</li> <li>Researcher translating to English, transcribing data in the digital tabular dataset, and cleaning the data: Nadia Pintossi</li> <li>Original language of the data: English, Italian, and mix of English and Italian</li> </ul>
Credit for software creators: An adapted illustration from The Turing Way: Shared under CC-BY 4.0 for reuse
<p>Illustration adapted from <strong>The Turing Way Community, & Scriberia. (2023). Illustrations from The Turing Way: Shared under CC-BY 4.0 for reuse. Zenodo. <a href="https://doi.org/10.5281/zenodo.8169292">https://doi.org/10.5281/zenodo.8169292</a></strong>.</p> <p>The illustration has been adapted by Stephan Druskat (subsumed as co-author in <em>The Turing Way Community</em>):</p> <ul> <li>The speech bubble has been changed to read "More credit" instead of "More credit<strong>s</strong>". (One character removed and alignment adapted.)</li> <li>The inscription on the purse has been changed to read "Credit" instead of "Credit<strong>s</strong>". (One character removed and alignment adapted.)</li> </ul>
Challenges of cultural heritage adaptive reuse: a stakeholders-based comparative study in three European cities. Dataset
<p>Dataset analysed in Pintossi, N., Ikiz Kaya, D., van Wesemael, P. J. V., & Pereira Roders, A. R. (2023). Challenges of cultural heritage adaptive reuse: A stakeholders-based comparative study in three European cities. Habitat International, 136, [102807]. https://doi.org/10.1016/j.habitatint.2023.102807.</p> <ul> <li>Date of data collection: a) 31/05/2018, b) 27/11/2018, and c) 28/03/2019</li> <li>Geographic location of data collection: a) Amsterdam, The Netherlands. The venue of the data collection was Pakhuis de Zwijger, Piet Heinkade 179, 1019 HC, Amsterdam, The Netherlands; b) Salerno, Italy. The venue of the data collection was Salone dei marmi, Palazzo di Città, via Roma, 84121 Salerno, Italy; and c) Rijeka, Croatia. The venue of the data collection is RiHub, Ul. Ivana Grohovca 1/a, 51000, Rijeka, Croatia.</li> <li>Activity of data collection: a) Historic Urban Landscape workshop 1 - Amsterdam. Held in Amsterdam, the Nethelands, on 30-31/05/2018; b) Historic Urban Landscape workshop 2 - Salerno. Held in Salerno, Italy, on 26-27/11/2018; and c) Historic Urban Landscape workshop 3 - Rijeka. Held in Rijeka, Croatia, on 28/03/2019.</li> <li>Aim of data collection: Multi-scale, participatory identification of challenges entailed in the adaptive reuse of cultural heritage and solutions to overcome these challenges.</li> <li>Methods for collection/generation of data: See the methodology section in a) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Identifying Challenges and Solutions in Cultural Heritage Adaptive Reuse through the Historic Urban Landscape Approach in Amsterdam. Sustainability, 13(10), 5547. https://doi.org/10.3390/su13105547; b) Pintossi, N., Ikiz Kaya, D., Pereira Roders, A. (2023). Cultural heritage adaptive reuse in Salerno: Challenges and solutions. City, Culture and Society, 33, 100505. https://doi.org/10.1016/j.ccs.2023.100505; and c) Pintossi, N., Ikiz Kaya, D., & Pereira Roders, A. (2021). Assessing Cultural Heritage Adaptive Reuse Practices: Multi-Scale Challenges and Solutions in Rijeka. Sustainability, 13(7), 3603. https://doi.org/10.3390/su13073603.</li> <li>Researchers facilitating roundtable discussion and writing down paper version of data: a) Gamze Dane, Antonia Gravagnuolo, Paloma Guzman Molina, Ana Pereira Roders, Nadia Pintossi, and Julia Rey-Perez; b) Marco Acri, Gaia Daldanise, Gamze Dane, Cristina Garzillo, Antonia Gravagnuolo, Lu Lu, Nadia Pintossi, and Ruba Saleh; and c) Marco Acri, Martina Bosone, Deniz Ikiz Kaya, Silvia Iodice, Lu Lu, and Nadia Pintossi.</li> <li>Language of the data: English.</li> <li>References: a) Pintossi, Nadia. (2021). Assessing cultural heritage adaptive reuse practices: multi-scale challenges and solutions in Rijeka. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4518743; b) Pintossi, Nadia. (2020). Identifying challenges and solutions in cultural heritage adaptive reuse through the Historic Urban Landscape approach in Amsterdam. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4250495; and c) Pintossi, Nadia. (2023). Cultural heritage adaptive reuse in Salerno: challenges and solutions. Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3925602</li> </ul> <p><br> </p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset2
<p>This dataset contains the data underlying the following publication: Li Qimeng, Wang Cheng, Hua Ming, Shuang Chendong, Li Aimin, Gao Canzhu. (2017). High-efficient removal of phthalate esters from aqueous solution with an easily regenerative magnetic resin: Hydrolytic degradation and simultaneous adsorption. <em>Journal of Cleaner Production</em><em>. </em> <a href="https://doi.org/10.1016/j.jclepro.2017.11.121">https://doi.org/10.1016/j.jclepro.2017.11.121</a></p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset1
<p>This dataset contains the data underlying the following publication: Mouna Mahjoubi, Simone Cappello, Yasmine Souissi, Atef Jaouani and Ameur Cherif (February 7th 2018). Microbial Bioremediation of Petroleum Hydrocarbon– Contaminated Marine Environments, Recent Insights in Petroleum Science and Engineering Mansoor Zoveidavianpoor, IntechOpen, DOI: 10.5772/intechopen.72207</p> <p> </p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset5
<p>This dataset contains the data underlying the following publication: Yu Huang, Shi Cheng, Ya-Ping Wu, Ji Wu<sup> </sup>, Yan Li, Zong-Li Huo, Ji-Chun Wu<sub>­</sub>, Xian-Chuan Xie, Gregory V. Korshin , Ai-Min Li<sup> </sup>, Wen-Tao Li (2019)</p> <p>Developing surrogate indicators for predicting suppression of halophenols formation potential and abatement of estrogenic activity during ozonation of water and wastewater. <em>Water Research.</em> <a href="https://doi.org/10.1016/j.watres.2019.05.092">https://doi.org/10.1016/j.watres.2019.05.092</a></p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset3
<p>This dataset contains the data underlying the following publication: Li Qimeng, Wu Ji, Hua Ming, Zhang Guang, Li Wentao, Shuang Chendong, Li Aimin. (2017). Preparation of Permanent Magnetic Resin Crosslinking by Diallyl Itaconate and Its Adsorptive and Anti-fouling Behaviors for Humic Acid Removal. <em>Scientific Report. </em> <a href="https://doi.org/10.1038/s41598-017-17360-8">https://doi.org/10.1038/s41598-017-17360-8</a></p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset2
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset1
<p>This dataset contains the data underlying the following publication: Li Wen-Tao, Cao Meng-Jie, Young Tessora, Ruffino Barbara, Dodd Michael, Li Ai-Min, Korshin Gregory. (2017).</p> <p>Application of UV absorbance and fluorescence indicators to assess the formation of biodegradable dissolved organic carbon and bromate during ozonation. Water Research 2017, 111, 154-162. <a href="http://dx.doi.org/10.1016/j.watres.2017.01.009">http://dx.doi.org/10.1016/j.watres.2017.01.009</a>.</p>
MADFORWATER: WP2: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task 2.3–Agro-industrial wastewater treatment: Subtask 2.3.1. –Treatment of olive mill wastewater (OMWW): Aerobic biological treatment in sequenced batch reactors (SBRs): Subset 1
<p>This dataset contains the data underlying the following publication: Fatma AROUS, Chadlia HAMDI, Souhir KMIHA, Nadia KHAMMASSI, Amani AYARI, Mohamed NEIFAR, Tahar MECHICHI, Atef JAOUANI (2018). Treatment of olive mill wastewater through employing sequencing batch reactor: Performance and microbial diversity assessment. 3 Biotech 2018, 8, 481. https://doi.org/10.1007/s13205-018-1486-6.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
Results Data for the Circular City and Adaptive Reuse of Cultural Heritage Opportunity Index
<p>The data file provides the results data for the article, "Circular City and Adaptive Reuse of Cultural Heritage Index: Measuring the Investment Opportunity in Europe" written by Dr. Gillian Foster and Dr. Ruba Saleh.</p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset4
<p>This dataset contains the data underlying the following publication: Ji Wu, Shi Cheng, Min-Hui Cai, Ya-Ping Wu, Yan Li, Ji-Chun Wu, Ai-Min Li, Wen-Tao Li(2018)</p> <p>Applying UV absorbance and fluorescence indices to estimate inactivation of bacteria and formation of bromate during ozonation of water and wastewater effluent. <em>Water Research. </em> <a href="https://doi.org/10.1016/j.watres.2018.08.030">https://doi.org/10.1016/j.watres.2018.08.030</a></p>
Dataset MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Subset 4
<p>This dataset contains the data underlying the following publication: Chouchane H, Mahjoubi M, Ettoumi B, Neifar, M. Cherif A (2017).</p> <p>A novel thermally stable heteropolysaccharide based bioflocculant from hydrocarbonoclastic strain <em>Kocuria rosea</em> BU22S and its application in dye removal, Environmental Technology, 39(7):859-872. <a href="https://doi.org/10.1080/09593330.2017.1313886">https://doi.org/10.1080/09593330.2017.1313886</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.