cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6075/j0p26x34
title: CIL:32135, Drosophila melanogaster, epithelial cell. In Cell Image Library

description: Relation to intact cell: dispersed cells in vitro
Item type: recorded image; hand drawn contour
Imaging mode: fluorescence microscopy
Parameter imaged: distribution of a specific protein; distribution of DNA
Source of contrast: distribution of a specific protein
Processing history: unprocessed raw data; tracing
Data qualification: Raw
description: Images were acquired using a motorized Zeiss Axioplan 2 and a Axiocam MRm camera, and are provided courtesy of the laboratory of David Sabatini at the Whitehead Institute for Biomedical Research. Each image is roughly 512 x 512 pixels, with cells roughly 25 pixels in diameter, and 80 cells per image on average. 

Algorithms for finding boundaries between adjacent cells may use the union of the manually outlined cells as input. They may also use the manually outlined nuclei as seed regions. This ensures that the algorithms are compared according to their ability to find boundaries betwen adjacent cells, and not the orthogonal problems of segmenting nuclei and distinguishing foreground (cells) from background.

To compare an algorithm's results to the manual outlines, define the relevant boundary pixels as the pixels that are on the boundary found by the algorith and that are not adjacent to any background pixels. For each relevant pixel, compute the Euclidean distance to the corresponding pixel on the manually found outline. Report the percentage of relevant pixels that are within two pixels of the corresponding pixel on the manually found outline.
description: Drosophila melanogaster Kc167 cells were stained for DNA (to label nuclei, red) and actin (a cytoskeletal protein, to show the cell body, green). Each image is a dual channel fluorescent image followed by a line tracing of the nuclei and a line tracing of the cell boundary. These images are provided as a tool for testing algorithims to find nuclei and the boundaries between adjacent cells.

Chris Gang outlined the nuclei and cells.
Please use the following recommended citation when using this data. "We used the Drosophila Kc167 2 image set (Jones et al., Proc. ICCV Workshop on Computer Vision for Biomedical Image Applications, 2005), available from the Broad Bioimage Benchmark Collection (www.broad.mit.edu/bbbc)."
DOI: 10.6075/j0x34wdh
title: CIL:32133, Drosophila melanogaster, epithelial cell. In Cell Image Library

description: Images were acquired using a motorized Zeiss Axioplan 2 and a Axiocam MRm camera, and are provided courtesy of the laboratory of David Sabatini at the Whitehead Institute for Biomedical Research. Each image is roughly 512 x 512 pixels, with cells roughly 25 pixels in diameter, and 80 cells per image on average. 

Algorithms for finding boundaries between adjacent cells may use the union of the manually outlined cells as input. They may also use the manually outlined nuclei as seed regions. This ensures that the algorithms are compared according to their ability to find boundaries betwen adjacent cells, and not the orthogonal problems of segmenting nuclei and distinguishing foreground (cells) from background.

To compare an algorithm's results to the manual outlines, define the relevant boundary pixels as the pixels that are on the boundary found by the algorith and that are not adjacent to any background pixels. For each relevant pixel, compute the Euclidean distance to the corresponding pixel on the manually found outline. Report the percentage of relevant pixels that are within two pixels of the corresponding pixel on the manually found outline.
description: Relation to intact cell: dispersed cells in vitro
Item type: recorded image; hand drawn contour
Imaging mode: fluorescence microscopy
Parameter imaged: distribution of a specific protein; distribution of DNA
Source of contrast: distribution of a specific protein
Processing history: unprocessed raw data; tracing
Data qualification: Raw
description: Drosophila melanogaster Kc167 cells were stained for DNA (to label nuclei, red) and actin (a cytoskeletal protein, to show the cell body, green). Each image is a dual channel fluorescent image followed by a line tracing of the nuclei and a line tracing of the cell boundary. These images are provided as a tool for testing algorithims to find nuclei and the boundaries between adjacent cells.

Chris Gang outlined the nuclei and cells.
Please use the following recommended citation when using this data. "We used the Drosophila Kc167 2 image set (Jones et al., Proc. ICCV Workshop on Computer Vision for Biomedical Image Applications, 2005), available from the Broad Bioimage Benchmark Collection (www.broad.mit.edu/bbbc)."
DOI: 10.6075/j06t0kk7
title: CIL:32143, Drosophila melanogaster, epithelial cell. In Cell Image Library

description: Images were acquired using a motorized Zeiss Axioplan 2 and a Axiocam MRm camera, and are provided courtesy of the laboratory of David Sabatini at the Whitehead Institute for Biomedical Research. Each image is roughly 512 x 512 pixels, with cells roughly 25 pixels in diameter, and 80 cells per image on average. 

Algorithms for finding boundaries between adjacent cells may use the union of the manually outlined cells as input. They may also use the manually outlined nuclei as seed regions. This ensures that the algorithms are compared according to their ability to find boundaries betwen adjacent cells, and not the orthogonal problems of segmenting nuclei and distinguishing foreground (cells) from background.

To compare an algorithm's results to the manual outlines, define the relevant boundary pixels as the pixels that are on the boundary found by the algorith and that are not adjacent to any background pixels. For each relevant pixel, compute the Euclidean distance to the corresponding pixel on the manually found outline. Report the percentage of relevant pixels that are within two pixels of the corresponding pixel on the manually found outline.
description: Drosophila melanogaster Kc167 cells were stained for DNA (to label nuclei, red) and actin (a cytoskeletal protein, to show the cell body, green). Each image is a dual channel fluorescent image followed by a line tracing of the nuclei and a line tracing of the cell boundary. These images are provided as a tool for testing algorithims to find nuclei and the boundaries between adjacent cells.

Chris Gang outlined the nuclei and cells.
Please use the following recommended citation when using this data. "We used the Drosophila Kc167 2 image set (Jones et al., Proc. ICCV Workshop on Computer Vision for Biomedical Image Applications, 2005), available from the Broad Bioimage Benchmark Collection (www.broad.mit.edu/bbbc)."
description: Relation to intact cell: dispersed cells in vitro
Item type: recorded image; hand drawn contour
Imaging mode: fluorescence microscopy
Parameter imaged: distribution of a specific protein; distribution of DNA
Source of contrast: distribution of a specific protein
Processing history: unprocessed raw data; tracing
Data qualification: Raw
DOI: 10.6075/j0p26x17
title: CIL:32142, Drosophila melanogaster, epithelial cell. In Cell Image Library

description: Drosophila melanogaster Kc167 cells were stained for DNA (to label nuclei, red) and actin (a cytoskeletal protein, to show the cell body, green). Each image is a dual channel fluorescent image followed by a line tracing of the nuclei and a line tracing of the cell boundary. These images are provided as a tool for testing algorithims to find nuclei and the boundaries between adjacent cells.

Chris Gang outlined the nuclei and cells.
Please use the following recommended citation when using this data. "We used the Drosophila Kc167 2 image set (Jones et al., Proc. ICCV Workshop on Computer Vision for Biomedical Image Applications, 2005), available from the Broad Bioimage Benchmark Collection (www.broad.mit.edu/bbbc)."
description: Relation to intact cell: dispersed cells in vitro
Item type: recorded image; hand drawn contour
Imaging mode: fluorescence microscopy
Parameter imaged: distribution of a specific protein; distribution of DNA
Source of contrast: distribution of a specific protein
Processing history: unprocessed raw data; tracing
Data qualification: Raw
description: Images were acquired using a motorized Zeiss Axioplan 2 and a Axiocam MRm camera, and are provided courtesy of the laboratory of David Sabatini at the Whitehead Institute for Biomedical Research. Each image is roughly 512 x 512 pixels, with cells roughly 25 pixels in diameter, and 80 cells per image on average. 

Algorithms for finding boundaries between adjacent cells may use the union of the manually outlined cells as input. They may also use the manually outlined nuclei as seed regions. This ensures that the algorithms are compared according to their ability to find boundaries betwen adjacent cells, and not the orthogonal problems of segmenting nuclei and distinguishing foreground (cells) from background.

To compare an algorithm's results to the manual outlines, define the relevant boundary pixels as the pixels that are on the boundary found by the algorith and that are not adjacent to any background pixels. For each relevant pixel, compute the Euclidean distance to the corresponding pixel on the manually found outline. Report the percentage of relevant pixels that are within two pixels of the corresponding pixel on the manually found outline.
DOI: 10.6075/j0kk99pp
title: CIL:32139, Drosophila melanogaster, epithelial cell. In Cell Image Library

description: Relation to intact cell: dispersed cells in vitro
Item type: recorded image; hand drawn contour
Imaging mode: fluorescence microscopy
Parameter imaged: distribution of a specific protein; distribution of DNA
Source of contrast: distribution of a specific protein
Processing history: unprocessed raw data; tracing
Data qualification: Raw
description: Drosophila melanogaster Kc167 cells were stained for DNA (to label nuclei, red) and actin (a cytoskeletal protein, to show the cell body, green). Each image is a dual channel fluorescent image followed by a line tracing of the nuclei and a line tracing of the cell boundary. These images are provided as a tool for testing algorithims to find nuclei and the boundaries between adjacent cells.

Chris Gang outlined the nuclei and cells.
Please use the following recommended citation when using this data. "We used the Drosophila Kc167 2 image set (Jones et al., Proc. ICCV Workshop on Computer Vision for Biomedical Image Applications, 2005), available from the Broad Bioimage Benchmark Collection (www.broad.mit.edu/bbbc)."
description: Images were acquired using a motorized Zeiss Axioplan 2 and a Axiocam MRm camera, and are provided courtesy of the laboratory of David Sabatini at the Whitehead Institute for Biomedical Research. Each image is roughly 512 x 512 pixels, with cells roughly 25 pixels in diameter, and 80 cells per image on average. 

Algorithms for finding boundaries between adjacent cells may use the union of the manually outlined cells as input. They may also use the manually outlined nuclei as seed regions. This ensures that the algorithms are compared according to their ability to find boundaries betwen adjacent cells, and not the orthogonal problems of segmenting nuclei and distinguishing foreground (cells) from background.

To compare an algorithm's results to the manual outlines, define the relevant boundary pixels as the pixels that are on the boundary found by the algorith and that are not adjacent to any background pixels. For each relevant pixel, compute the Euclidean distance to the corresponding pixel on the manually found outline. Report the percentage of relevant pixels that are within two pixels of the corresponding pixel on the manually found outline.
DOI: 10.6084/m9.figshare.30488429
title: Integrated Multi-Source Dataset for Grassland Resilience Assessment in Northwestern China (2001–2023)

description: This dataset provides a comprehensive collection of multi-source environmental and ecological data used for assessing grassland resilience and early-warning signals of abrupt decline events in northwestern China, particularly the Xinjiang region.<br>The dataset integrates meteorological, vegetation, land use, and grazing information at a unified 1 km spatial resolution and annual temporal scale, supporting analyses of climate–grazing–resilience interactions.<b>Contents</b>The dataset contains the following components:<b>Weather station data_Xinjiang_2000–2019</b> — Daily temperature and precipitation from national meteorological stations, used for local-scale calibration and trend analysis.<b>ERA5_Climate_Variables_Xinjiang_2000_2023</b> — Reanalysis-based gridded climate variables (temperature, precipitation, radiation, evapotranspiration).<b>NDVI_MOD13A2_2001_2023</b> — MODIS MOD13A2 product-derived NDVI data, 1 km resolution, preprocessed and smoothed.<b>FVC_1km_Xinjiang</b> — Fractional Vegetation Cover (FVC) derived from remote sensing, annual mean rasters at 1 km resolution.<b>Grazing_Intensity_China_2001_2023</b> — National grazing intensity dataset (SU ha⁻¹) from the National Ecosystem Science Data Center, used to quantify anthropogenic pressure.<b>Land_use_Xinjiang_2000–2020</b> — Chinese Land Use/Cover Change (CLCD) dataset for grassland pixel selection.<b>Classification_of_grassland_China_2020</b> — National grassland classification map (1 km) indicating dominant grassland types.<b>Temporal Coverage</b>Meteorological data: 2000–2023NDVI/FVC: 2000–2023Grazing intensity: 2001–2023Land use: 2000 / 2020Grassland classification: 2020<b>Processing and Usage</b>All layers were resampled to 1 km and converted to point format for spatial modeling.The data were used to derive resilience indicators such as <b>temporal autocorrelation (TAC)</b>, its long-term trend (δTAC), and the <b>Proximity-to-collapse indicator</b> based on XGBoost models.Grassland classification and land use maps served for <b>sample selection and ecological stratification</b>.ERA5 and station data supported <b>climate variable interpolation</b> and long-term trend analysis.NDVI and FVC time series were used to identify <b>abrupt decline (AD) events</b> and assess vegetation stability.Grazing intensity data quantified human disturbance levels and their coupling effects with climatic variability.
DOI: 10.6084/m9.figshare.30488429.v1
title: Integrated Multi-Source Dataset for Grassland Resilience Assessment in Northwestern China (2001–2023)

description: This dataset provides a comprehensive collection of multi-source environmental and ecological data used for assessing grassland resilience and early-warning signals of abrupt decline events in northwestern China, particularly the Xinjiang region.<br>The dataset integrates meteorological, vegetation, land use, and grazing information at a unified 1 km spatial resolution and annual temporal scale, supporting analyses of climate–grazing–resilience interactions.<b>Contents</b>The dataset contains the following components:<b>Weather station data_Xinjiang_2000–2019</b> — Daily temperature and precipitation from national meteorological stations, used for local-scale calibration and trend analysis.<b>ERA5_Climate_Variables_Xinjiang_2000_2023</b> — Reanalysis-based gridded climate variables (temperature, precipitation, radiation, evapotranspiration).<b>NDVI_MOD13A2_2001_2023</b> — MODIS MOD13A2 product-derived NDVI data, 1 km resolution, preprocessed and smoothed.<b>FVC_1km_Xinjiang</b> — Fractional Vegetation Cover (FVC) derived from remote sensing, annual mean rasters at 1 km resolution.<b>Grazing_Intensity_China_2001_2023</b> — National grazing intensity dataset (SU ha⁻¹) from the National Ecosystem Science Data Center, used to quantify anthropogenic pressure.<b>Land_use_Xinjiang_2000–2020</b> — Chinese Land Use/Cover Change (CLCD) dataset for grassland pixel selection.<b>Classification_of_grassland_China_2020</b> — National grassland classification map (1 km) indicating dominant grassland types.<b>Temporal Coverage</b>Meteorological data: 2000–2023NDVI/FVC: 2000–2023Grazing intensity: 2001–2023Land use: 2000 / 2020Grassland classification: 2020<b>Processing and Usage</b>All layers were resampled to 1 km and converted to point format for spatial modeling.The data were used to derive resilience indicators such as <b>temporal autocorrelation (TAC)</b>, its long-term trend (δTAC), and the <b>Proximity-to-collapse indicator</b> based on XGBoost models.Grassland classification and land use maps served for <b>sample selection and ecological stratification</b>.ERA5 and station data supported <b>climate variable interpolation</b> and long-term trend analysis.NDVI and FVC time series were used to identify <b>abrupt decline (AD) events</b> and assess vegetation stability.Grazing intensity data quantified human disturbance levels and their coupling effects with climatic variability.
DOI: 10.6075/j0fn166k
title: CIL:35634, Saccharomyces cerevisiae. In Cell Image Library

description: Preparation: living tissue
Relation to intact cell: dispersed cells in vitro
Item type: recorded image
Imaging mode: fluorescence microscopy; phase contrast microscopy
Parameter imaged: fluorescence emission; differences in amount of elastic light scattering
Source of contrast: distribution of a specific protein; differences in amount of elastic light scattering
Visualization methods: EGFP; visualization of contiguous regions
description: A time lapse experiment of Saccharomyces cerevisiae expressing GFP tagged Cln2. Cln2 is a G1 cyclin involved in regulation of the cell cycle; activates Cdc28p kinase to promote the G1 to S phase transition; late G1 specific expression depends on transcription factor complexes, MBF (Swi6p-Mbp1p) and SBF (Swi6p-Swi4p).These phase,gfp images are part of an image group that ranges from CIL:35620-35639. Note that there are additional groups showing time series of other cell cycle regulation proteins by the same authors in the Library.
description: Time-lapse images were collected on a DeltaVision system (Olympus IX71) with 60x/1.42 NA objective at 5 minute intervals. Please see the microscopy section in the referenced manuscript for details for image analysis.
DOI: 10.6075/j0rj4j4q
title: CIL: 53402, Plankton. In Cell Image Library

description: Imaging mode: Holography
Visualization methods: Amplitude Mode
description: Diatom
description: 128x128 segmented image from volumetric hologram
DOI: 10.6075/j0ns0tbp
title: CIL: 51195, Mus musculus, RAW 264.7 TIB-71. In Cell Image Library

description: A novel cell population analysis to quantify both initial and long-term drug effects, namely pseudopod adaptation and recovery following an initial drug-induced perturbation, adding drug slowly in 1/4 increments over 90 minutes.
description: ~1200 cells were imaged on glass. More gradual additions of drug or carrier were carried out in four equal increments at 0, 30, 60, 90 min, together yielding the same final drug concentration indicated in rapid addition studies. The subpopulation of ~100 cells with extended, leading edge pseudopods at t = 0 were imaged and counted in each unit area at the indicated timepoints during a 125 min observation period T=37C

Images are provided as uncorrected, 16 bit .nd2 files to preserve all original data. To view these .nd2 files:
1. Download ImageJ viewer at (https://imagej.nih.gov/ij/download.html)
2. Download the BioFormats.jar package at (https://www.openmicroscopy.org/bio-formats/downloads/)
3. Open the ImageJ directory after installation and drag the extracted BioFormats.jar package into the "Plugins" subdirectory
4. Open an .nd2 image using imageJ and select opening options when prompted, if desired. If no modifications are desired, select "OK"
5. The 16 bit image will typically look dark and will require brightness adjustment.
6. In the ImageJ toolbar, select Image &gt;&gt; Adjust &gt;&gt; Brightness/Contrast and select "Auto", or manually set the brightness and contrast.
description: Imaging mode: DICM
Visualization methods: Spinning disc confocal DICM & widefield DICM Using a Nikon TiE microscope equipped with a 40x, 0.95 N.A. objective and a Hamamatsu ORCA-Flash 4.0 V3 Digital CMOS camera, or a Nikon TiE spinning-disc confocal microscope equipped with a Yokogawa CSU-X1 scanning unit, an Andor iXon 888 EMCCD camera, and a 60x, 1.3 N.A. water-immersion objective or a 40x, 0.95 N.A. objective
DOI: 10.6075/j05h7fr2
title: CIL: 52177, Homo sapiens. In Cell Image Library

description: A single molecule TIRFM assay was employed to measure the kinase activity of purified PI3K molecules on a supported lipid bilayer by counting the number of product PIP3 molecules they produce.
description: Imaging mode: Fluoresence
Visualization methods: objective based TIRFM using a modified Nikon TE-2000U (60x 1.49 N.A. TIRF objective) and illumination from a 532-nm solid-state laser with power modification by neutral-density filters
description: This assay utilizes physiological concentrations class I PI3Ka, 1mM ATP, drug identified in subfolder, and saturating concentrations of fluorescently-labeled GRP-PH domain, a PIP3 product lipid binder, in order to count each product lipid produced. Enzyme rates are calculated as the slope of product lipid created vs. time. 21.5 ± 0.5°C

Images are provided as uncorrected, 16 bit .nd2 files to preserve all original data. To view these .nd2 files:
1. Download ImageJ viewer at (https://imagej.nih.gov/ij/download.html)
2. Download the BioFormats.jar package at (https://www.openmicroscopy.org/bio-formats/downloads/)
3. Open the ImageJ directory after installation and drag the extracted BioFormats.jar package into the "Plugins" subdirectory
4. Open an .nd2 image using imageJ and select opening options when prompted, if desired. If no modifications are desired, select "OK"
5. The 16 bit image will typically look dark and will require brightness adjustment.
6. In the ImageJ toolbar, select Image &gt;&gt; Adjust &gt;&gt; Brightness/Contrast and select "Auto", or manually set the brightness and contrast.
DOI: 10.6075/j02n51wk
title: CIL: 52987, Homo sapiens, High-grade serious overian cancer. In Cell Image Library

description: Image 5, z=17 from SC_011
description: Imaging mode: Fluorescence in situ hybridization (FISH)
Visualization methods: Nikon Eclipse fluorescence inverted microscope equipped with a charge-coupled device camera (Andor Neo sCMOS), using filter sets for DAPI/ YGFP/TRITC/CY GFP with an objective lens (Plan Apo VC 100x, Nikon). Captured with 100x magnification of the objective and a pixel size of 0.07 microns
description: One z layer of a z-stack at the given image
DOI: 10.6075/j0vd6z1n
title: CIL: 52922, Homo sapiens, High-grade serious overian cancer. In Cell Image Library

description: Imaging mode: Fluorescence in situ hybridization (FISH)
Visualization methods: Nikon Eclipse fluorescence inverted microscope equipped with a charge-coupled device camera (Andor Neo sCMOS), using filter sets for DAPI/ YGFP/TRITC/CY GFP with an objective lens (Plan Apo VC 100x, Nikon). Captured with 100x magnification of the objective and a pixel size of 0.07 microns
description: One z layer of a z-stack at the given image
description: Image 11, z=15 from SC_007
DOI: 10.6075/j0bg2nm2
title: CIL: 53104, Homo sapiens, High-grade serious overian cancer. In Cell Image Library

description: Imaging mode: Fluorescence in situ hybridization (FISH)
Visualization methods: Nikon Eclipse fluorescence inverted microscope equipped with a charge-coupled device camera (Andor Neo sCMOS), using filter sets for DAPI/ YGFP/TRITC/CY GFP with an objective lens (Plan Apo VC 100x, Nikon). Captured with 100x magnification of the objective and a pixel size of 0.07 microns
description: Image 2, z=8 from SC_030
description: One z layer of a z-stack at the given image