cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7848496
title: Datasets of eponyms for South American plants and Mozambican terrestrial vertebrates

description: Two datasets containing information on the Eponyms of South American plants and Mozambique's terrestrial vertebrates.
DOI: 10.5281/zenodo.6383043
title: Fused Image dataset for convolutional neural Network-based crack Detection (FIND)

description: The “<strong>F</strong>used <strong>I</strong>mage dataset for convolutional neural <strong>N</strong>etwork-based crack <strong>D</strong>etection” (<strong>FIND</strong>) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types. The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance. If you share or use this dataset, please cite [4] and [5] in any relevant documentation. In addition, an image dataset for crack classification has also been published at [6]. References: [1] Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. https://doi.org/10.1061/(asce)cp.1943-5487.0000873 [2] Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. https://doi.org/10.1016/j.autcon.2021.103605 [3] Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. https://doi.org/10.1177/1475921720948434 [4] Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. https://doi.org/10.1016/j.autcon.2022.104678 [5] (<strong>This dataset</strong>) Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6383044 [6] Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. https://doi.org/10.17603/ds2-bzv3-nc78
DOI: 10.5281/zenodo.6383044
title: Fused Image dataset for convolutional neural Network-based crack Detection (FIND)

description: The “<strong>F</strong>used <strong>I</strong>mage dataset for convolutional neural <strong>N</strong>etwork-based crack <strong>D</strong>etection” (<strong>FIND</strong>) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types. The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance. If you share or use this dataset, please cite [4] and [5] in any relevant documentation. In addition, an image dataset for crack classification has also been published at [6]. References: [1] Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. https://doi.org/10.1061/(asce)cp.1943-5487.0000873 [2] Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. https://doi.org/10.1016/j.autcon.2021.103605 [3] Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. https://doi.org/10.1177/1475921720948434 [4] Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. https://doi.org/10.1016/j.autcon.2022.104678 [5] (<strong>This dataset</strong>) Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6383044 [6] Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. https://doi.org/10.17603/ds2-bzv3-nc78
DOI: 10.35078/y9rwv8
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Caratinga | Município: Divino das Laranjeiras

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.5281/zenodo.4095494
title: A dataset of measured room impulse responses for the transition between coupled rooms

description: This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included: Meeting Room to Hallway Office to Anechoic Chamber Office to Kitchen Office to Stairwell All were recorded at the Aalto University campus using a Genelec 8331A coaxial loudspeaker and an mh acoustics Eigenmike (32 capsule spherical microphone array for fourth order spherical harmonic capture). Each transition features 101 measurements in 5cm intervals from 2.5m inside the first room to 2.5m inside the second room. Transitions are repeated four times corresponding to four different source positions: two inside each room, one which has no continuous line-of-sight with the microphone when in the opposing room, and one which retains a direct continuous line-of-sight with the microphone for all measurement positions. The spatial room impulse responses are downloadable in either Spatially Oriented Format for Acoustics (SOFA) and Wav formats. Supplementary data includes amplitude plots, direct-to-reverberant graphs, estimated direction-of-arrival plots and scaled illustrations of room geometries and source positions.
description: Correspondence to thomas.mckenzie@aalto.fi
DOI: 10.5281/zenodo.4095493
title: A dataset of measured spatial room impulse responses for the transition between coupled rooms

description: For a detailed description of the measurement and analysis methods, please see: McKenzie, T., Schlecht, S. J., and Pulkki, V. (2021). Acoustic Analysis and Dataset of Transitions between Coupled Rooms. <em>IEEE International Conference on Acoustics, Speech and Signal Processing.</em> This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included: Meeting Room to Hallway Office to Anechoic Chamber Office to Kitchen Office to Stairwell All were recorded at the Aalto University campus using a Genelec 8331A coaxial loudspeaker and an mh acoustics Eigenmike (32 capsule spherical microphone array for fourth order spherical harmonic capture). Each transition features 101 measurements in 5cm intervals from 2.5m inside the first room to 2.5m inside the second room. Transitions are repeated four times corresponding to four different source positions: two inside each room, one which has no continuous line-of-sight with the microphone when in the opposing room, and one which retains a direct continuous line-of-sight with the microphone for all measurement positions. The spatial room impulse responses are downloadable in either Spatially Oriented Format for Acoustics (SOFA) and Wav formats. Supplementary data includes amplitude plots, direct-to-reverberant graphs, estimated direction-of-arrival plots and scaled illustrations of room geometries and source positions. Changelog: V 1.0 - Initial version<br> V 1.1 - Added plots only download option<br> V 1.2 - Improved time alignment of impulse responses and changed normalisation to a single value relative to the maximum of the entire dataset<br> V 1.3 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition data corrected. The SOFA files for each transition are also now downloadable separately, in case the entire dataset is not required. <br> V 1.4 - SRIRs have been denoised using the technique described in https://www.aes.org/e-lib/browse.cfm?elib=21800 and available at https://github.com/chris-hld/Directional-Multi-Slope-Room-Impulse-Response-Denoising. This is particularly noticeable for measurements with a low SNR, such as where there is a large distance between source and receiver and occluded direct path. Note that the wav files have not been included in this release - see previous releases if wav files (not denoised) are required. SOFA files are renamed and available to download separately.
description: Correspondence to thomas.mckenzie@ed.ac.uk
DOI: 10.5281/zenodo.4506379
title: A dataset of measured room impulse responses for the transition between coupled rooms

description: For a detailed description of the measurement and analysis methods, please see: McKenzie, T., Schlecht, S. J., and Pulkki, V. (2021). Acoustic Analysis and Dataset of Transitions between Coupled Rooms. <em>IEEE International Conference on Acoustics, Speech and Signal Processing.</em> This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included: Meeting Room to Hallway Office to Anechoic Chamber Office to Kitchen Office to Stairwell All were recorded at the Aalto University campus using a Genelec 8331A coaxial loudspeaker and an mh acoustics Eigenmike (32 capsule spherical microphone array for fourth order spherical harmonic capture). Each transition features 101 measurements in 5cm intervals from 2.5m inside the first room to 2.5m inside the second room. Transitions are repeated four times corresponding to four different source positions: two inside each room, one which has no continuous line-of-sight with the microphone when in the opposing room, and one which retains a direct continuous line-of-sight with the microphone for all measurement positions. The spatial room impulse responses are downloadable in either Spatially Oriented Format for Acoustics (SOFA) and Wav formats. Supplementary data includes amplitude plots, direct-to-reverberant graphs, estimated direction-of-arrival plots and scaled illustrations of room geometries and source positions.
description: Correspondence to thomas.mckenzie@aalto.fi
DOI: 10.5281/zenodo.4636068
title: A dataset of measured spatial room impulse responses for the transition between coupled rooms

description: For a detailed description of the measurement and analysis methods, please see: McKenzie, T., Schlecht, S. J., and Pulkki, V. (2021). Acoustic Analysis and Dataset of Transitions between Coupled Rooms. <em>IEEE International Conference on Acoustics, Speech and Signal Processing.</em> This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included: Meeting Room to Hallway Office to Anechoic Chamber Office to Kitchen Office to Stairwell All were recorded at the Aalto University campus using a Genelec 8331A coaxial loudspeaker and an mh acoustics Eigenmike (32 capsule spherical microphone array for fourth order spherical harmonic capture). Each transition features 101 measurements in 5cm intervals from 2.5m inside the first room to 2.5m inside the second room. Transitions are repeated four times corresponding to four different source positions: two inside each room, one which has no continuous line-of-sight with the microphone when in the opposing room, and one which retains a direct continuous line-of-sight with the microphone for all measurement positions. The spatial room impulse responses are downloadable in either Spatially Oriented Format for Acoustics (SOFA) and Wav formats. Supplementary data includes amplitude plots, direct-to-reverberant graphs, estimated direction-of-arrival plots and scaled illustrations of room geometries and source positions. Changelog: V 1.0 - Initial version<br> V 1.1 - Added plots only download option<br> V 1.2 - Improved time alignment of impulse responses and changed normalisation to a single value relative to the maximum of the entire dataset
description: Correspondence to thomas.mckenzie@aalto.fi
DOI: 10.5281/zenodo.6375520
title: A dataset of measured spatial room impulse responses for the transition between coupled rooms

description: For a detailed description of the measurement and analysis methods, please see: McKenzie, T., Schlecht, S. J., and Pulkki, V. (2021). Acoustic Analysis and Dataset of Transitions between Coupled Rooms. <em>IEEE International Conference on Acoustics, Speech and Signal Processing.</em> This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included: Meeting Room to Hallway Office to Anechoic Chamber Office to Kitchen Office to Stairwell All were recorded at the Aalto University campus using a Genelec 8331A coaxial loudspeaker and an mh acoustics Eigenmike (32 capsule spherical microphone array for fourth order spherical harmonic capture). Each transition features 101 measurements in 5cm intervals from 2.5m inside the first room to 2.5m inside the second room. Transitions are repeated four times corresponding to four different source positions: two inside each room, one which has no continuous line-of-sight with the microphone when in the opposing room, and one which retains a direct continuous line-of-sight with the microphone for all measurement positions. The spatial room impulse responses are downloadable in either Spatially Oriented Format for Acoustics (SOFA) and Wav formats. Supplementary data includes amplitude plots, direct-to-reverberant graphs, estimated direction-of-arrival plots and scaled illustrations of room geometries and source positions. Changelog: V 1.0 - Initial version<br> V 1.1 - Added plots only download option<br> V 1.2 - Improved time alignment of impulse responses and changed normalisation to a single value relative to the maximum of the entire dataset<br> V 1.3 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition data corrected. The SOFA files for each transition are also now downloadable separately, in case the entire dataset is not required.
description: Correspondence to thomas.mckenzie@aalto.fi
DOI: 10.5281/zenodo.7848561
title: A dataset of measured spatial room impulse responses for the transition between coupled rooms

description: For a detailed description of the measurement and analysis methods, please see: McKenzie, T., Schlecht, S. J., and Pulkki, V. (2021). Acoustic Analysis and Dataset of Transitions between Coupled Rooms. <em>IEEE International Conference on Acoustics, Speech and Signal Processing.</em> This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included: Meeting Room to Hallway Office to Anechoic Chamber Office to Kitchen Office to Stairwell All were recorded at the Aalto University campus using a Genelec 8331A coaxial loudspeaker and an mh acoustics Eigenmike (32 capsule spherical microphone array for fourth order spherical harmonic capture). Each transition features 101 measurements in 5cm intervals from 2.5m inside the first room to 2.5m inside the second room. Transitions are repeated four times corresponding to four different source positions: two inside each room, one which has no continuous line-of-sight with the microphone when in the opposing room, and one which retains a direct continuous line-of-sight with the microphone for all measurement positions. The spatial room impulse responses are downloadable in either Spatially Oriented Format for Acoustics (SOFA) and Wav formats. Supplementary data includes amplitude plots, direct-to-reverberant graphs, estimated direction-of-arrival plots and scaled illustrations of room geometries and source positions. Changelog: V 1.0 - Initial version<br> V 1.1 - Added plots only download option<br> V 1.2 - Improved time alignment of impulse responses and changed normalisation to a single value relative to the maximum of the entire dataset<br> V 1.3 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition data corrected. The SOFA files for each transition are also now downloadable separately, in case the entire dataset is not required. <br> V 1.4 - SRIRs have been denoised using the technique described in https://www.aes.org/e-lib/browse.cfm?elib=21800 and available at https://github.com/chris-hld/Directional-Multi-Slope-Room-Impulse-Response-Denoising. This is particularly noticeable for measurements with a low SNR, such as where there is a large distance between source and receiver and occluded direct path. Note that the wav files have not been included in this release - see previous releases if wav files (not denoised) are required. SOFA files are renamed and available to download separately.
description: Correspondence to thomas.mckenzie@ed.ac.uk
DOI: 10.5517/ccdc.csd.cc2934vf
title: CCDC 2118194: Experimental Crystal Structure Determination

description: Related Article: Matthew L. Martin, Claire Wilson, Alistair Boyer|2023|Chem.Commun.|59|4899|doi:10.1039/D3CC01127E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7408852
title: Global crop-specific nitrogen fertilization dataset in 1961-2020

description: This is a dataset for crop-specific N fertilization with 5-arc-min resolution during 1961 to 2020, including N fertilizer inputs, types and placements. The N fertilization data was classified into 21 crop types, 13 fertilizer types and 2 fertilization placements.The datasets extend from 180°E to 180°W longtitude and 90°S to 90°N latitude with a resolution of 5 arc-min for the temporal period 1961–2020 in standard WGS84 coordinate system, with a temporal resolution. The data are provided in Hierarchical Data Format (HDF5) format.
DOI: 10.5281/zenodo.7408851
title: Global crop-specific nitrogen fertilization dataset in 1961-2020

description: This is a dataset for crop-specific N fertilization with 5-arc-min resolution during 1961 to 2020, including N fertilizer inputs, types and placements. The N fertilization data was classified into 21 crop types, 13 fertilizer types and 2 fertilization placements.The datasets extend from 180°E to 180°W longtitude and 90°S to 90°N latitude with a resolution of 5 arc-min for the temporal period 1961–2020 in standard WGS84 coordinate system, with a temporal resolution. The data are provided in Hierarchical Data Format (HDF5) format.
DOI: 10.5281/zenodo.7849320
title: IAQ4EDU measurement campaign

description: This is a database including data gathered during the measurement campaign of the IAQ4EDU project (https://iaq4edu.upc.edu/).
description: This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/501100011033.
DOI: 10.5281/zenodo.7849319
title: IAQ4EDU measurement campaign

description: This is a database including data gathered during the measurement campaign of the IAQ4EDU project (https://iaq4edu.upc.edu/).
description: This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/501100011033.
DOI: 10.6073/pasta/d5e1cd27c96ad7572cab135cd67c410d
title: Daily Meteorological data from East Peak, Sabana, and Bisley the Luquillo Experimental Forest

description: The data archive is here: https://doi.org/10.2737/RDS-2022-0050 please use this DOI when citing this dataset. This data publication contains daily means from laser ceilometer data from Sabana, ozone data from Bisley, and meteorological data collected from Bisley and the mountain top station of East Peak, all located on the Luquillo Experimental Forest (El Yunque National Forest) in Puerto Rico. Atmospheric data include: mean cloud observed frequency, mean lowest height cloud (cloud base), mean daytime mixing layer height observed frequency, mean lowest mixing layer height from hours 7am and 7pm only, and mean daytime mixing layer height collected from February 2013 through April 2021. Also included is mean ozone amount collected from April 2008 through early April 2021. The cloud and mixing layer frequency and low values were calculated using the Automated Surface Observing System (ASOS) method employed at airports in the area. Weather data include high elevation East Peak mean northeast wind speed (wind rose quartiles 45° to 90°), mean southeast wind speed (wind rose quartiles 90° to 135°), and mean total wind speed measured from October 2009 through 2020 (and a few months in 2021). Additional weather data collected from January 2002 through mid October 2020 include mid elevation Bisley mean precipitation, mean relative humidity, and mean temperature.