cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6848592
title: TrypTag: plate U5340 (replicate dated 20160428)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5340, replicate dated 20160428.
DOI: 10.5281/zenodo.6848593
title: TrypTag: plate U5340 (replicate dated 20160428)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5340, replicate dated 20160428.
DOI: 10.5281/zenodo.7072310
title: TrypTag: plate U5340 (replicate dated 20160428)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5340, replicate dated 20160428.
DOI: 10.5281/zenodo.6848647
title: TrypTag: plate U5342 (replicate dated 20160429)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5342, replicate dated 20160429.
DOI: 10.5281/zenodo.6848648
title: TrypTag: plate U5342 (replicate dated 20160429)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5342, replicate dated 20160429.
DOI: 10.5281/zenodo.7072315
title: TrypTag: plate U5342 (replicate dated 20160429)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5342, replicate dated 20160429.
DOI: 10.5281/zenodo.6848704
title: TrypTag: plate U5344 (replicate dated 20160429)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5344, replicate dated 20160429.
DOI: 10.5281/zenodo.6848705
title: TrypTag: plate U5344 (replicate dated 20160429)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5344, replicate dated 20160429.
DOI: 10.5281/zenodo.7072316
title: TrypTag: plate U5344 (replicate dated 20160429)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5344, replicate dated 20160429.
DOI: 10.5281/zenodo.6848903
title: TrypTag: plate U5350 (replicate dated 20160415)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5350, replicate dated 20160415.
DOI: 10.5281/zenodo.6848904
title: TrypTag: plate U5350 (replicate dated 20160415)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5350, replicate dated 20160415.
DOI: 10.5281/zenodo.7072317
title: TrypTag: plate U5350 (replicate dated 20160415)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5350, replicate dated 20160415.
DOI: 10.5281/zenodo.6849406
title: TrypTag: plate U8367 (replicate dated 20160725)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U8367, replicate dated 20160725.
DOI: 10.5281/zenodo.6849407
title: TrypTag: plate U8367 (replicate dated 20160725)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U8367, replicate dated 20160725.
DOI: 10.5281/zenodo.7072318
title: TrypTag: plate U8367 (replicate dated 20160725)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U8367, replicate dated 20160725.
DOI: 10.5281/zenodo.7072520
title: UrbanOccupationsOETR_1844/45 Bursa Region Temettuat Agricultural Production Dataset

description: <strong>‘UrbanOccupationsOETR_1844/45 Bursa Region Temettuat Agricultural Production Dataset’</strong> is constructed first by manual data entry from the Ottoman temettuat registers (available at the Turkish Presidency State Archives of the Republic of Turkey – Department of Ottoman Archives, (ML. VRD. TMT. d.) collection) into our customized relational database and then by making a selection available in spreadsheet format. <br> Our team located, curated, and extracted agricultural production data for a total of 81 geosampled villages from the Bursa region for 1844/45. We coded both agricultural production areas as well as product type using the Corine Land Cover nomenclature (CLC2018, https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html) and made available only the category, ‘2.1.1 Non-irrigated arable land’ (https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/index-clc-211.html), as a proxy for grain production. <strong>02122020-A5_Total</strong> count of agricultural entries CLC 2.1.1 coded, qualified (dönüm and convertible) and quantified (numerical value)<br> provides data on the cultivated area in square meters of 4,657 units, belonging to 3,019 producers, of 2,892 households, in 81 villages. <strong>02122020_T6_Total</strong> count of tithe tax entries CLC 2.1.1 coded, qualified (kile and convertible), and quantified (numerical value)<br> provides data on production volumes in kgs of 4,516 units, belonging to 1,924 producers, of 1,873 households, in 70 villages. <br> Please cite the below paper in your publications if you use the dataset: Ustaoglu, Eda, M. Erdem Kabadayı, and Petrus Johannes Gerrits, ‘The Estimation of Non-Irrigated Crop Area and Production Using the Regression Analysis Approach: A Case Study of Bursa Region (Turkey) in the Mid-Nineteenth Century’, PLOS ONE 16, no. 4 (30 April 2021): e0251091, https://doi.org/10.1371/journal.pone.0251091.
DOI: 10.5281/zenodo.7072519
title: UrbanOccupationsOETR_1844/45 Bursa Region Temettuat Agricultural Production Dataset

description: <strong>‘UrbanOccupationsOETR_1844/45 Bursa Region Temettuat Agricultural Production Dataset’</strong> is constructed first by manual data entry from the Ottoman temettuat registers (available at the Turkish Presidency State Archives of the Republic of Turkey – Department of Ottoman Archives, (ML. VRD. TMT. d.) collection) into our customized relational database and then by making a selection available in spreadsheet format. <br> Our team located, curated, and extracted agricultural production data for a total of 81 geosampled villages from the Bursa region for 1844/45. We coded both agricultural production areas as well as product type using the Corine Land Cover nomenclature (CLC2018, https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html) and made available only the category, ‘2.1.1 Non-irrigated arable land’ (https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/index-clc-211.html), as a proxy for grain production. <strong>02122020-A5_Total</strong> count of agricultural entries CLC 2.1.1 coded, qualified (dönüm and convertible) and quantified (numerical value)<br> provides data on the cultivated area in square meters of 4,657 units, belonging to 3,019 producers, of 2,892 households, in 81 villages. <strong>02122020_T6_Total</strong> count of tithe tax entries CLC 2.1.1 coded, qualified (kile and convertible), and quantified (numerical value)<br> provides data on production volumes in kgs of 4,516 units, belonging to 1,924 producers, of 1,873 households, in 70 villages. <br> Please cite the below paper in your publications if you use the dataset: Ustaoglu, Eda, M. Erdem Kabadayı, and Petrus Johannes Gerrits, ‘The Estimation of Non-Irrigated Crop Area and Production Using the Regression Analysis Approach: A Case Study of Bursa Region (Turkey) in the Mid-Nineteenth Century’, PLOS ONE 16, no. 4 (30 April 2021): e0251091, https://doi.org/10.1371/journal.pone.0251091.
DOI: 10.5281/zenodo.7072541
title: UrbanOccupationsOETR_Generalkarte_MapText_DCNN_dataset

description: The UrbanOccupationsOETR_Generalkarte_MapText_DCNN_dataset contains sample material for text detection and recognition from the Generalkarte (the 3rd Military Mapping Survey of Austria-Hungary) Map. We provided 53 annotated image tiles (1024×1024) divided into a separate training and validation folder. In all images, text regions and non-text regions are labeled.<br> <br> If you would like to use the dataset below in further publication, please use the credentials specified below: Yekta Said Can and M. Erdem Kabadayi, ‘Text Detection and Recognition by using CNNs in the Austro-Hungarian Historical Military Mapping Survey’, Proceedings of the 6th International Workshop on Historical Document Imaging and Processing (HIP’21). 2021. Please contact <em>mkabadayi@ku.edu.tr </em>for questions regarding the DCNN dataset.
DOI: 10.5281/zenodo.7072540
title: UrbanOccupationsOETR_Generalkarte_MapText_DCNN_dataset

description: The UrbanOccupationsOETR_Generalkarte_MapText_DCNN_dataset contains sample material for text detection and recognition from the Generalkarte (the 3rd Military Mapping Survey of Austria-Hungary) Map. We provided 53 annotated image tiles (1024×1024) divided into a separate training and validation folder. In all images, text regions and non-text regions are labeled.<br> <br> If you would like to use the dataset below in further publication, please use the credentials specified below: Yekta Said Can and M. Erdem Kabadayi, ‘Text Detection and Recognition by using CNNs in the Austro-Hungarian Historical Military Mapping Survey’, Proceedings of the 6th International Workshop on Historical Document Imaging and Processing (HIP’21). 2021. Please contact <em>mkabadayi@ku.edu.tr </em>for questions regarding the DCNN dataset.
DOI: 10.6084/m9.figshare.21082450
title: Management Adaptations for Water-Limited Pearl Millet Systems in Senegal - Dataset

description: This dataset comprehends the outputs from the simulations of different managements performed by the APSIM-Millet model calibrated for two landraces in 351 locations for the producing districts of pearl millet (12) in Senegal, between 1990 to 2021. <br> Variables: <br> site_id (1 to 351) district department latitude longitude landrace (early- or late-flowering) management (standard or recommended) plant density (plant m<sup>-2</sup>) N fertilizer (kg ha<sup>-1</sup>) planting date days to flowering days to maturity grain yield (kg ha<sup>-1</sup>) precipitation during the growing season (mm) yield to precipitation ratio (kg m<sup>-3</sup>)
DOI: 10.6084/m9.figshare.21082450.v2
title: Management Adaptations for Water-Limited Pearl Millet Systems in Senegal - Dataset

description: This dataset comprehends the outputs from the simulations of different managements performed by the APSIM-Millet model calibrated for two landraces in 351 locations for the producing districts of pearl millet (12) in Senegal, between 1990 to 2021. <br> Variables: <br> site_id (1 to 351) district department latitude longitude landrace (early- or late-flowering) management (standard or recommended) plant density (plant m<sup>-2</sup>) N fertilizer (kg ha<sup>-1</sup>) planting date days to flowering days to maturity grain yield (kg ha<sup>-1</sup>) precipitation during the growing season (mm) yield to precipitation ratio (kg m<sup>-3</sup>)
DOI: 10.6084/m9.figshare.21082450.v1
title: Untitled Management Adaptations for Water-Limited Pearl Millet Systems in Senegal - Dataset

description: This dataset comprehends the outputs from the simulations of different managements performed by the APSIM-Millet model calibrated for two landraces in 351 locations for the producing districts of pearl millet (12) in Senegal, between 1990 to 2021. <br> Variables: <br> site_id (1 to 351) district department latitude longitude landrace (early- or late-flowering) management (standard or recommended) plant density (plant m<sup>-2</sup>) N fertilizer (kg ha<sup>-1</sup>) planting date days to flowering days to maturity grain yield (kg ha<sup>-1</sup>) precipitation during the growing season (mm) yield to precipitation ratio (kg m<sup>-3</sup>)
DOI: 10.5281/zenodo.7065657
title: gyroScatterEFF data: 590k element cyclone mesh

description: Arrays and constants needed to run the gyroScatterEFF kernel on a single GPU without XGCm.<br> <br> Created with https://github.com/SCOREC/xgcm/tree/cws/extractGyroScatter @ 5a8f0b4. <br> <br> The build on OLCF Summit used the following scripts: https://github.com/zhangchonglin/XGCm_build_scripts/tree/792c9378b6a12ca141811cbd788c6d6542e59938/Summit_gcc11.2.0_cuda11.5.2<br> <br> And the Cyclone 590k mesh case here: https://github.com/SCOREC/xgc1_data/tree/0d32f7f1354346a37df1fac517fa1f4fceff2015/Cyclone_ITG/Cyclone_ITG_deltaf_590kmesh <br> <br> At the time of dataset publication, the XGCm and xgc1_data repos were private.
description: {"references": ["https://doi.org/10.1016/j.jpdc.2021.06.004"]}
DOI: 10.5281/zenodo.7065658
title: gyroScatterEFF data: 590k element cyclone mesh

description: Arrays and constants needed to run the gyroScatterEFF kernel on a single GPU without XGCm.<br> <br> Created with https://github.com/SCOREC/xgcm/tree/cws/extractGyroScatter @ 425c93d. <br> <br> The build on OLCF Summit used the following scripts: https://github.com/zhangchonglin/XGCm_build_scripts/tree/792c9378b6a12ca141811cbd788c6d6542e59938/Summit_gcc11.2.0_cuda11.5.2<br> <br> And the Cyclone 590k mesh case here: https://github.com/SCOREC/xgc1_data/tree/0d32f7f1354346a37df1fac517fa1f4fceff2015/Cyclone_ITG/Cyclone_ITG_deltaf_590kmesh <br> <br> At the time of dataset publication, the XGCm and xgc1_data repos were private.
description: {"references": ["https://doi.org/10.1016/j.jpdc.2021.06.004"]}
DOI: 10.5281/zenodo.7072575
title: gyroScatterEFF data: 590k element cyclone mesh

description: Arrays and constants needed to run the gyroScatterEFF kernel on a single GPU without XGCm.<br> <br> Created with https://github.com/SCOREC/xgcm/tree/cws/extractGyroScatter @ 5a8f0b4. <br> <br> The build on OLCF Summit used the following scripts: https://github.com/zhangchonglin/XGCm_build_scripts/tree/792c9378b6a12ca141811cbd788c6d6542e59938/Summit_gcc11.2.0_cuda11.5.2<br> <br> And the Cyclone 590k mesh case here: https://github.com/SCOREC/xgc1_data/tree/0d32f7f1354346a37df1fac517fa1f4fceff2015/Cyclone_ITG/Cyclone_ITG_deltaf_590kmesh <br> <br> At the time of dataset publication, the XGCm and xgc1_data repos were private.
description: {"references": ["https://doi.org/10.1016/j.jpdc.2021.06.004"]}
DOI: 10.17632/spsg756sx8.2
title: Proteomics output data for "Two independent respiratory chains adapt OXPHOS performance to glycolytic switch" by Fernandez-Vizarra et al. (2022). 

description: The files contain proteomics output datasets associated with the manuscript entitled: "Two independent respiratory chains adapt OXPHOS performance to glycolytic switch" by Fernandez-Vizarra et al. (2022). 
The first file (data for Figure 3A) is the result of a SILAC-based quantitative proteomics analysis on mitochondria from HEK 293T SCAFI-KO, COX7A2-KO and DOUBLE-KO in comparison with WT cells.
The other two files contain peptide intensity maps and hierarchical clustering analysis derived from Complexome Profiling experiments (native electrophoresis separation followed by mass spectrometry) performed in two human postmitotic tissues, I.e, brain fronatal cortex and skeletal muscle. These data were used to create Figures 4A and 4B, respectively.
DOI: 10.17632/n6ctnkmndx.1
title: Supporting Data for 'P\'eclet-number dependence of optimal mixing strategies identified using multiscale norms'

description: This dataset contains videos of the scalar and vorticity plots used in the Physica D: Nonlinear Phenomena Paper 'P\'eclet-number dependence of optimal mixing strategies identified using multiscale norms' for the Charlie Doering Special Issue.
DOI: 10.5281/zenodo.7072594
title: IODP Expedition 351 Color reflectance

description: Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
DOI: 10.5281/zenodo.7072593
title: IODP Expedition 351 Color reflectance

description: Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
DOI: 10.5281/zenodo.7072590
title: IODP Expedition 351 RGB channels (calculated from core photos)

description: Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
DOI: 10.5281/zenodo.7072589
title: IODP Expedition 351 RGB channels (calculated from core photos)

description: Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
DOI: 10.6084/m9.figshare.21084358
title: papilio_aristodemus_zanfona_1.fasta.gz

description: ........
DOI: 10.6084/m9.figshare.21084358.v1
title: papilio_aristodemus_zanfona_1.fasta.gz

description: ........
DOI: 10.17608/k6.auckland.21081235
title: Upstream passage of Galaxias maculatus and Galaxias brevipinnis: comparative behavioural assessment for improved fish pass design

description: Abstract 1. Customized fish passage solutions enhance the upstream passage of fish through low-head instream structures. These solutions are designed based on the behaviour of benchmark species, with the hope that they will also perform well for other species. Across species, suitability can be supported by establishing similarity in passage behaviour. However, for small-bodied fish species, such comparative behavioural assessments are largely missing. 2. With respect to spoiler baffle-based passage design for Southern Hemisphere small-bodied fish species, Galaxias maculatus are typically considered as a benchmark species. In order to characterise the upstream passage of G. maculatus with a view to extending it to other species, here the behaviour of G. maculatus and Galaxias brevipinnis were recorded for three different spoiler baffle designs. Hydrodynamic preferences during upstream swimming and resting, including pathway assessment, were analysed. 3. Both species show increased preference to swim at locations with streamwise velocities similar to the known sustained swimming capacity threshold of G. maculatus, whilst resting locations align with minimal streamwise velocities. Preferred pathways were observed to be influenced by the streamwise water velocity and turbulent kinetic energy, with both species showing increased preference to move towards locations with similar streamwise velocities, by avoiding locations with higher turbulent kinetic energy. 4. The study further shows that both species' passage behaviour is more dependent on space availability than individual behaviour alone. The spatial distribution of fine-scale hydrodynamics influences observed swimming and resting behaviour, with both species showing similar passage behaviour for all three tested designs. 5. Synthesis and applications: Similarities, for both passage behaviour and pathway selection, suggest that spoiler baffle-based passage designs are a suitable management option for both species. Both species were observed to use different fine-scale hydrodynamic regions for resting and subsequent swimming. Among the patches, patch P2 provided increased resting and low-velocity swimming cells for both species, which are shown to be important ecological indicators of successful passage applications, supporting further evaluation for possible application of culvert retrofitting, allowing for varying flow rates, slopes and fish species. <br>