We use seven public data sets and one private data set for evaluation. Below we outline how to obtain each of the public data sets and how our evaluation system expects the files to be laid out on the file system. We have taken great care into ensuring that we use the original file layout from extracted zip files. See the Rules for details on evaluations. See Getting Started for more information on how to run your own method locally on these data sets.
The POPI data set consists 4D CT series made up of ten 3D volumes representing ten different phases of one breathing cycle. Images were acquired on a 16 Slice Brilliance CT Big Bore Oncology™ configuration (Philips). Each slice has a resolution of 0.976562mm x 0.976562mm and a thickness of 2mm. The resulting volume size of one 3D volume is 512 x 512 x 141. For the challenge, we use the preprocessed meta images at extreme phases of the breathing cycle and the lung masks included in the data set.
The data set is presented in Vandemeulebroucke, J., Sarrut, D. and Clarysse, P. The POPI-model, a point-validated pixel-based breathing thorax model, XVth International Conference on the Use of Computers in Radiation Therapy (ICCR), Toronto, Canada. 2007and can be downloaded from https://www.creatis.insa-lyon.fr/rio/dir_validation_data (quick method: run "$ wget --recursive --accept 00.mhd,00.raw,50.mhd,50.raw,*.pts --no-parent https://www.creatis.insa-lyon.fr/~srit/POPI/MedPhys11").
The file layout expected by the Python scripts to run registration locally is:
POPI|-- bh| |-- mhd| | |-- 00.mhd| | |-- 00.raw| | |-- 50.mhd| | |-- 50.raw| `-- pts| |-- 00.pts| |-- 00.vtk| `-- 50.pts|-- bl| |-- mhd| | |-- 00.mhd| | |-- 00.raw| | |-- 50.mhd| | |-- 50.raw| `-- pts| |-- 00.pts| `-- 50.pts|-- dx| |-- mhd| | |-- 00.mhd| | |-- 00.raw| | |-- 50.mhd| | `-- 50.raw| `-- pts| |-- 00.pts| |-- 50.pts|-- gt| |-- mhd| | |-- 00.mhd| | |-- 00.raw| | |-- 50.mhd| | |-- 50.raw| `-- pts| |-- 00.pts| `-- 50.pts|-- mm2| |-- mhd| | |-- 00.mhd| | |-- 00.raw| | |-- 50.mhd| | |-- 50.raw| `-- pts| |-- 00.pts| |-- 00.vtk| `-- 50.pts`-- ng |-- mhd | |-- 00.mhd | |-- 00.raw | |-- 50.mhd | |-- 50.raw `-- pts |-- 00.pts `-- 50.pts
The data set consists of 10 thoracic 4DCT images acquired at The University of Texas M. D. Anderson Cancer Center in Houston, TX. For this challenge, we use images in the extreme phases have been annotated with 300 anatomical landmarks by an expert in thoracic imaging.
Image details are given athttps://www.dir-lab.com/ReferenceData.html. The data used for this challenge is presented in Castillo R, Castillo E, Guerra R, Johnson VE, McPhail T, Garg AK, Guerrero T. 2009. A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys Med Biol 54 1849-1870 and Castillo E, Castillo R, Martinez J, Shenoy M, Guerrero T. 2009. Four-dimensional deformable image registration using trajectory modeling. Phys Med Biol 55 305-327 and can be downloaded fromhttps://www.dir-lab.com/. Masks used for this challenge can be downloaded here.
The file layout expected bythe Python scripts to run registration locallyis:
DIR-LAB|-- Case10Pack| |-- ExtremePhases| | |-- case10_dirLab300_T00_xyz.txt| | `-- case10_dirLab300_T50_xyz.txt| `-- Images| |-- case10_T00.img| `-- case10_T50.img|-- Case1Pack| |-- ExtremePhases| | |-- Case1_300_T00_xyz.txt| | `-- Case1_300_T50_xyz.txt| `-- Images| |-- case1_T00_s.img| `-- case1_T50_s.img|-- Case2Pack| |-- ExtremePhases| | |-- Case2_300_T00_xyz.txt| | `-- Case2_300_T50_xyz.txt| `-- Images| |-- case2_T00-ssm.img| `-- case2_T50-ssm.img|-- Case3Pack| |-- ExtremePhases| | |-- Case3_300_T00_xyz.txt| | `-- Case3_300_T50_xyz.txt| `-- Images| |-- case3_T00-ssm.img| `-- case3_T50-ssm.img|-- Case4Pack| |-- ExtremePhases| | |-- Case4_300_T00_xyz.txt| | `-- Case4_300_T50_xyz.txt| `-- Images| |-- case4_T00-ssm.img| `-- case4_T50-ssm.img|-- Case5Pack| |-- ExtremePhases| | |-- Case5_300_T00_xyz.txt| | `-- Case5_300_T50_xyz.txt| `-- Images| |-- case5_T00-ssm.img| `-- case5_T50-ssm.img|-- Case6Pack| |-- ExtremePhases| | |-- case6_dirLab300_T00_xyz.txt| | `-- case6_dirLab300_T50_xyz.txt| `-- Images| |-- case6_T00.img| `-- case6_T50.img|-- Case7Pack| |-- ExtremePhases| | |-- case7_dirLab300_T00_xyz.txt| | `-- case7_dirLab300_T50_xyz.txt| `-- Images| |-- case7_T00.img| `-- case7_T50.img|-- Case8Deploy| |-- ExtremePhases| | |-- case8_dirLab300_T00_xyz.txt| | `-- case8_dirLab300_T50_xyz.txt| `-- Images| |-- case8_T00.img| `-- case8_T50.img`-- Case9Pack |-- ExtremePhases | |-- case9_dirLab300_T00_xyz.txt | `-- case9_dirLab300_T50_xyz.txt `-- Images |-- case9_T00.img `-- case9_T50.img40 directories, 200 files
The EMPIRE10 challenge was launched in early 2010 with an initial set of 20 scan pairs to be registered by participants in their own facility. This was followed in September by a workshop at the MICCAI 2010 conference where participants registered a further 10 scan pairs. We use the full data set for this challenge and automatically submit results from our cluster to the official EMPIRE evaluation scripts. The challenge and results described in detail in Murphy et al., "Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge.", IEEE Trans Med Imaging. 2011 Nov;30(11):1901-20. and the EMPIRE lung data set can be downloaded from http://empire10.isi.uu.nl/.
To run registration locally with these Python scripts, You have to manually merge the extracted zip files into one folder with a layout like this:
EMPIRE`-- scans |-- 01_Fixed.mhd |-- 01_Fixed.raw |-- 01_Moving.mhd |-- 01_Moving.raw |-- 02_Fixed.mhd |-- 02_Fixed.raw |-- 02_Moving.mhd |-- 02_Moving.raw |-- 03_Fixed.mhd |-- 03_Fixed.raw |-- 03_Moving.mhd |-- 03_Moving.raw |-- 04_Fixed.mhd |-- 04_Fixed.raw |-- 04_Moving.mhd |-- 04_Moving.raw |-- 05_Fixed.mhd |-- 05_Fixed.raw |-- 05_Moving.mhd |-- 05_Moving.raw |-- 06_Fixed.mhd |-- 06_Fixed.raw |-- 06_Moving.mhd |-- 06_Moving.raw |-- 07_Fixed.mhd |-- 07_Fixed.raw |-- 07_Moving.mhd |-- 07_Moving.raw |-- 08_Fixed.mhd |-- 08_Fixed.raw |-- 08_Moving.mhd |-- 08_Moving.raw |-- 09_Fixed.mhd |-- 09_Fixed.raw |-- 09_Moving.mhd |-- 09_Moving.raw |-- 10_Fixed.mhd |-- 10_Fixed.raw |-- 10_Moving.mhd |-- 10_Moving.raw |-- 11_Fixed.mhd |-- 11_Fixed.raw |-- 11_Moving.mhd |-- 11_Moving.raw |-- 12_Fixed.mhd |-- 12_Fixed.raw |-- 12_Moving.mhd |-- 12_Moving.raw |-- 13_Fixed.mhd |-- 13_Fixed.raw |-- 13_Moving.mhd |-- 13_Moving.raw |-- 14_Fixed.mhd |-- 14_Fixed.raw |-- 14_Moving.mhd |-- 14_Moving.raw |-- 15_Fixed.mhd |-- 15_Fixed.raw |-- 15_Moving.mhd |-- 15_Moving.raw |-- 16_Fixed.mhd |-- 16_Fixed.raw |-- 16_Moving.mhd |-- 16_Moving.raw |-- 17_Fixed.mhd |-- 17_Fixed.raw |-- 17_Moving.mhd |-- 17_Moving.raw |-- 18_Fixed.mhd |-- 18_Fixed.raw |-- 18_Moving.mhd |-- 18_Moving.raw |-- 19_Fixed.mhd |-- 19_Fixed.raw |-- 19_Moving.mhd |-- 19_Moving.raw |-- 20_Fixed.mhd |-- 20_Fixed.raw |-- 20_Moving.mhd |-- 20_Moving.raw |-- 21_Fixed.mhd |-- 21_Fixed.raw |-- 21_Moving.mhd |-- 21_Moving.raw |-- 22_Fixed.mhd |-- 22_Fixed.raw |-- 22_Moving.mhd |-- 22_Moving.raw |-- 23_Fixed.mhd |-- 23_Fixed.raw |-- 23_Moving.mhd |-- 23_Moving.raw |-- 24_Fixed.mhd |-- 24_Fixed.raw |-- 24_Moving.mhd |-- 24_Moving.raw |-- 25_Fixed.mhd |-- 25_Fixed.raw |-- 25_Moving.mhd |-- 25_Moving.raw |-- 26_Fixed.mhd |-- 26_Fixed.raw |-- 26_Moving.mhd |-- 26_Moving.raw |-- 27_Fixed.mhd |-- 27_Fixed.raw |-- 27_Moving.mhd |-- 27_Moving.raw |-- 28_Fixed.mhd |-- 28_Fixed.raw |-- 28_Moving.mhd |-- 28_Moving.raw |-- 29_Fixed.mhd |-- 29_Fixed.raw |-- 29_Moving.mhd |-- 29_Moving.raw |-- 30_Fixed.mhd |-- 30_Fixed.raw |-- 30_Moving.mhd `-- 30_Moving.raw1 directory, 120 files
The LONI Probabilistic Brain Atlas (LPBA40) consists of whole-head MRI of 40 human volunteers and manually delineations of 56 structures in the brain, most of which are within the cortex. The data is presented in Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW, Construction of a 3D Probabilistic Atlas of Human Cortical Structures, NeuroImage (2007), doi: 10.1016/j.neuroimage.2007.09.031 and can be downloaded from http://www.loni.usc.edu/atlases/Atlas_Detail.php?atlas_id=12.
The file layout expected by the Python scripts to run registration locally is:
LPBA40|-- Atlas_Distribution_Agreement_v2.pdf|-- LPBA40_subject_data.pdf|-- delineation_space| |-- S01| | |-- S01.delineation.skullstripped.hdr| | |-- S01.delineation.skullstripped.img.gz| | |-- S01.delineation.structure.label.hdr| | `-- S01.delineation.structure.label.img.gz| |-- S02| | |-- S02.delineation.skullstripped.hdr| | |-- S02.delineation.skullstripped.img.gz| | |-- S02.delineation.structure.label.hdr| | `-- S02.delineation.structure.label.img.gz| |-- S03| | |-- S03.delineation.skullstripped.hdr| | |-- S03.delineation.skullstripped.img.gz| | |-- S03.delineation.structure.label.hdr| | `-- S03.delineation.structure.label.img.gz| |-- S04| | |-- S04.delineation.skullstripped.hdr| | |-- S04.delineation.skullstripped.img.gz| | |-- S04.delineation.structure.label.hdr| | `-- S04.delineation.structure.label.img.gz| |-- S05| | |-- S05.delineation.skullstripped.hdr| | |-- S05.delineation.skullstripped.img.gz| | |-- S05.delineation.structure.label.hdr| | `-- S05.delineation.structure.label.img.gz| |-- S06| | |-- S06.delineation.skullstripped.hdr| | |-- S06.delineation.skullstripped.img.gz| | |-- S06.delineation.structure.label.hdr| | `-- S06.delineation.structure.label.img.gz| |-- S07| | |-- S07.delineation.skullstripped.hdr| | |-- S07.delineation.skullstripped.img.gz| | |-- S07.delineation.structure.label.hdr| | `-- S07.delineation.structure.label.img.gz| |-- S08| | |-- S08.delineation.skullstripped.hdr| | |-- S08.delineation.skullstripped.img.gz| | |-- S08.delineation.structure.label.hdr| | `-- S08.delineation.structure.label.img.gz| |-- S09| | |-- S09.delineation.skullstripped.hdr| | |-- S09.delineation.skullstripped.img.gz| | |-- S09.delineation.structure.label.hdr| | `-- S09.delineation.structure.label.img.gz| |-- S10| | |-- S10.delineation.skullstripped.hdr| | |-- S10.delineation.skullstripped.img.gz| | |-- S10.delineation.structure.label.hdr| | `-- S10.delineation.structure.label.img.gz| |-- S11| | |-- S11.delineation.skullstripped.hdr| | |-- S11.delineation.skullstripped.img.gz| | |-- S11.delineation.structure.label.hdr| | `-- S11.delineation.structure.label.img.gz| |-- S12| | |-- S12.delineation.skullstripped.hdr| | |-- S12.delineation.skullstripped.img.gz| | |-- S12.delineation.structure.label.hdr| | `-- S12.delineation.structure.label.img.gz| |-- S13| | |-- S13.delineation.skullstripped.hdr| | |-- S13.delineation.skullstripped.img.gz| | |-- S13.delineation.structure.label.hdr| | `-- S13.delineation.structure.label.img.gz| |-- S14| | |-- S14.delineation.skullstripped.hdr| | |-- S14.delineation.skullstripped.img.gz| | |-- S14.delineation.structure.label.hdr| | `-- S14.delineation.structure.label.img.gz| |-- S15| | |-- S15.delineation.skullstripped.hdr| | |-- S15.delineation.skullstripped.img.gz| | |-- S15.delineation.structure.label.hdr| | `-- S15.delineation.structure.label.img.gz| |-- S16| | |-- S16.delineation.skullstripped.hdr| | |-- S16.delineation.skullstripped.img.gz| | |-- S16.delineation.structure.label.hdr| | `-- S16.delineation.structure.label.img.gz| |-- S17| | |-- S17.delineation.skullstripped.hdr| | |-- S17.delineation.skullstripped.img.gz| | |-- S17.delineation.structure.label.hdr| | `-- S17.delineation.structure.label.img.gz| |-- S18| | |-- S18.delineation.skullstripped.hdr| | |-- S18.delineation.skullstripped.img.gz| | |-- S18.delineation.structure.label.hdr| | `-- S18.delineation.structure.label.img.gz| |-- S19| | |-- S19.delineation.skullstripped.hdr| | |-- S19.delineation.skullstripped.img.gz| | |-- S19.delineation.structure.label.hdr| | `-- S19.delineation.structure.label.img.gz| |-- S20| | |-- S20.delineation.skullstripped.hdr| | |-- S20.delineation.skullstripped.img.gz| | |-- S20.delineation.structure.label.hdr| | `-- S20.delineation.structure.label.img.gz| |-- S21| | |-- S21.delineation.skullstripped.hdr| | |-- S21.delineation.skullstripped.img.gz| | |-- S21.delineation.structure.label.hdr| | `-- S21.delineation.structure.label.img.gz| |-- S22| | |-- S22.delineation.skullstripped.hdr| | |-- S22.delineation.skullstripped.img.gz| | |-- S22.delineation.structure.label.hdr| | `-- S22.delineation.structure.label.img.gz| |-- S23| | |-- S23.delineation.skullstripped.hdr| | |-- S23.delineation.skullstripped.img.gz| | |-- S23.delineation.structure.label.hdr| | `-- S23.delineation.structure.label.img.gz| |-- S24| | |-- S24.delineation.skullstripped.hdr| | |-- S24.delineation.skullstripped.img.gz| | |-- S24.delineation.structure.label.hdr| | `-- S24.delineation.structure.label.img.gz| |-- S25| | |-- S25.delineation.skullstripped.hdr| | |-- S25.delineation.skullstripped.img.gz| | |-- S25.delineation.structure.label.hdr| | `-- S25.delineation.structure.label.img.gz| |-- S26| | |-- S26.delineation.skullstripped.hdr| | |-- S26.delineation.skullstripped.img.gz| | |-- S26.delineation.structure.label.hdr| | `-- S26.delineation.structure.label.img.gz| |-- S27| | |-- S27.delineation.skullstripped.hdr| | |-- S27.delineation.skullstripped.img.gz| | |-- S27.delineation.structure.label.hdr| | `-- S27.delineation.structure.label.img.gz| |-- S28| | |-- S28.delineation.skullstripped.hdr| | |-- S28.delineation.skullstripped.img.gz| | |-- S28.delineation.structure.label.hdr| | `-- S28.delineation.structure.label.img.gz| |-- S29| | |-- S29.delineation.skullstripped.hdr| | |-- S29.delineation.skullstripped.img.gz| | |-- S29.delineation.structure.label.hdr| | `-- S29.delineation.structure.label.img.gz| |-- S30| | |-- S30.delineation.skullstripped.hdr| | |-- S30.delineation.skullstripped.img.gz| | |-- S30.delineation.structure.label.hdr| | `-- S30.delineation.structure.label.img.gz| |-- S31| | |-- S31.delineation.skullstripped.hdr| | |-- S31.delineation.skullstripped.img.gz| | |-- S31.delineation.structure.label.hdr| | `-- S31.delineation.structure.label.img.gz| |-- S32| | |-- S32.delineation.skullstripped.hdr| | |-- S32.delineation.skullstripped.img.gz| | |-- S32.delineation.structure.label.hdr| | `-- S32.delineation.structure.label.img.gz| |-- S33| | |-- S33.delineation.skullstripped.hdr| | |-- S33.delineation.skullstripped.img.gz| | |-- S33.delineation.structure.label.hdr| | `-- S33.delineation.structure.label.img.gz| |-- S34| | |-- S34.delineation.skullstripped.hdr| | |-- S34.delineation.skullstripped.img.gz| | |-- S34.delineation.structure.label.hdr| | `-- S34.delineation.structure.label.img.gz| |-- S35| | |-- S35.delineation.skullstripped.hdr| | |-- S35.delineation.skullstripped.img.gz| | |-- S35.delineation.structure.label.hdr| | `-- S35.delineation.structure.label.img.gz| |-- S36| | |-- S36.delineation.skullstripped.hdr| | |-- S36.delineation.skullstripped.img.gz| | |-- S36.delineation.structure.label.hdr| | `-- S36.delineation.structure.label.img.gz| |-- S37| | |-- S37.delineation.skullstripped.hdr| | |-- S37.delineation.skullstripped.img.gz| | |-- S37.delineation.structure.label.hdr| | `-- S37.delineation.structure.label.img.gz| |-- S38| | |-- S38.delineation.skullstripped.hdr| | |-- S38.delineation.skullstripped.img.gz| | |-- S38.delineation.structure.label.hdr| | `-- S38.delineation.structure.label.img.gz| |-- S39| | |-- S39.delineation.skullstripped.hdr| | |-- S39.delineation.skullstripped.img.gz| | |-- S39.delineation.structure.label.hdr| | `-- S39.delineation.structure.label.img.gz| |-- S40| | |-- S40.delineation.skullstripped.hdr| | |-- S40.delineation.skullstripped.img.gz| | |-- S40.delineation.structure.label.hdr| | `-- S40.delineation.structure.label.img.gz| `-- lpba40.label.xml`-- lpba40.txt41 directories, 164 files
The ISBR18 data set consists of T1-weighted MR Image data with expert segmentations of 84 individual structures. The 18 brain images were acquired at different laboratories. The T1-weighted images have been rotated to Talairach alignment (Talairach and Tournoux, 1988) and have have undergone ‘autoseg’ bias field correctio by the CMA (Center for Morphometric Analysis, Massachusetts General Hospital (MGH) in Boston). To obtain this dataset, download"IBSR_v2.0_nifti_stripped.thz"from https://www.nitrc.org/projects/ibsr/.
CUMC12 consists of 18 images acquired at the Columbia University Medical Center on a 1.5 T GE scanner. Images were resliced coronally to a slice thickness of 3 mm, rotated into cardinal orientation, then segmented and manually labeled by one technician trained according to the Cardviews labeling scheme (Caviness et al., 1996) created at the CMA. The images have 128 labeled regions and can be downloaded from https://www.synapse.org/#!Synapse:syn3207203.
The file layout expected by the Python scripts used to run registration locally is:
CUMC12|-- Atlases| |-- m1.hdr| |-- m1.img| |-- m10.hdr| |-- m10.img| |-- m11.hdr| |-- m11.img| |-- m12.hdr| |-- m12.img| |-- m2.hdr| |-- m2.img| |-- m3.hdr| |-- m3.img| |-- m4.hdr| |-- m4.img| |-- m5.hdr| |-- m5.img| |-- m6.hdr| |-- m6.img| |-- m7.hdr| |-- m7.img| |-- m8.hdr| |-- m8.img| |-- m9.hdr| `-- m9.img|-- Heads| |-- m1.hdr| |-- m1.img| |-- m10.hdr| |-- m10.img| |-- m11.hdr| |-- m11.img| |-- m12.hdr| |-- m12.img| |-- m2.hdr| |-- m2.img| |-- m3.hdr| |-- m3.img| |-- m4.hdr| |-- m4.img| |-- m5.hdr| |-- m5.img| |-- m6.hdr| |-- m6.img| |-- m7.hdr| |-- m7.img| |-- m8.hdr| |-- m8.img| |-- m9.hdr| `-- m9.img`-- Labels `-- CUMC_labels.m3 directories, 49 files
This data set consist of 10 subjects scanned at the MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging using a 3 T Siemens scanner and standard head coil. The data is inhomogeneity-corrected, affine-registered to the MNI152 template (Evans et al., 1992), and segmented using SPM2 software (Friston et al., 1995) into 74 regions according to the labeling protocol by Tourville and Guenther, 2003. The MGH10 brain data set can be downloaded from https://www.synapse.org/#!Synapse:syn3207203.
The file layout expected bythe Python scripts to run registration locallyis:
MGH10|-- Atlases| |-- g1.hdr| |-- g1.img| |-- g10.hdr| |-- g10.img| |-- g2.hdr| |-- g2.img| |-- g3.hdr| |-- g3.img| |-- g4.hdr| |-- g4.img| |-- g5.hdr| |-- g5.img| |-- g6.hdr| |-- g6.img| |-- g7.hdr| |-- g7.img| |-- g8.hdr| |-- g8.img| |-- g9.hdr| `-- g9.img|-- Heads| |-- g1.hdr| |-- g1.img| |-- g10.hdr| |-- g10.img| |-- g2.hdr| |-- g2.img| |-- g3.hdr| |-- g3.img| |-- g4.hdr| |-- g4.img| |-- g5.hdr| |-- g5.img| |-- g6.hdr| |-- g6.img| |-- g7.hdr| |-- g7.img| |-- g8.hdr| |-- g8.img| |-- g9.hdr| `-- g9.img`-- Labels |-- g_labels.m `-- g_labels.txt3 directories, 42 files
FAQs
What is meant by grand challenge? ›
A grand challenge is a fundamental problem in science or engineering, with broad applications, whose solution would be enabled by the application of high performance computing resources that could become available in the near future.
What is a grand challenge article? ›A set of challenges devised by several professional associations across the world at the turn of the century to bring the attention of the general public and of professionals to the pressing problems of society, especially to sustainability and issues associated to the overuse of materials.
What are the 5 grand challenges? ›But we know the main sources of today's global greenhouse gas emissions: manufacturing (30 percent), electricity (26 percent), agriculture (21 percent), transportation (16 percent), and buildings (7 percent). We call these the Five Grand Challenges of climate change.
How does a Grand Challenge work? ›There are two types of Victory Challenges: Classic Challenge and Grand Challenge. Players can join either one at any time. The Classic Challenge costs 10 Gems to enter, while the Grand Challenge costs 100 Gems to enter. Once the Challenge has started, players will be able to battle.
What are the characteristics of a Grand Challenge? ›In particular, we identify three characteristics of grand challenges that are conceptually related to the three facets (complexity, uncertainty, evaluativeness) classified by Ferraro et al. (2015). These characteristics are the interconnectedness, fluidity, and paradoxical nature of grand challenges.
Why are grand challenges important? ›Grand Challenges are ambitious but achievable goals that harness science, technology, and innovation to solve important national or global problems and that have the potential to capture the public's imagination.
What are the top 3 reasons for why you would like to join grand challenges? ›- Critical Skills. Develop critical skills in communication, self-awareness, project teaming, cultural humility, and leadership for societal impact.
- Career Connections. ...
- Community Engagement.
The 14 Grand Challenges for Engineering | Grand Challenges Scholars Program.
What is grand challenges annual? ›The Grand Challenges Annual Meeting fosters innovation and international collaboration to address the biggest challenges in global health and development. It has been held in 11 countries over the past 17 years and in a virtual format for the past two years.
How do challenge points work? ›Challenge Points are points you accumulate from completing individual objectives on a Challenge Card. Challenges Points are added to your total Category points, which contribute to your Crystal rank and Challenge-group Capstones. Keep clearing Challenges to level up your Crystal!
What is the open data for good Grand Challenge? ›
The Open Data for Good Grand Challenge is a set of monetary and in-kind prizes awarded for teams who have created high-impact digital tools that solve problems for the public.
What are the NSF grand challenges? ›The grand challenges of today -- protecting human health; understanding the food, energy, water nexus; exploring the universe at all scales -- will not be solved by one discipline alone.
How do you get 12 wins in grand challenge? ›- Picking a Deck.
- Master Your Deck.
- Playing in a Safe Environment.
- Playing while Mentally Stable and Healthy (DON'T TILT)
- Playing at less competitive times of the day – or year.
- Be Realistic.
- Avoid the Common Myths.
GSCs are massive social and environmental issues that transcend national borders – such as climate change, inequality, disruptive migration, and global pandemics – and that have potential or actual negative effects on large numbers of people, communities, and the planet as a whole.
What is a character challenge? ›Character Challenge™ Fun, educational game gets kids to think deeply and critically about important character issues. They learn what constitutes good character and how character affects the quality of relationships with family, friends, and adults at school.
Who came up with grand challenges? ›Launched in 2003 by the Bill & Melinda Gates Foundation as Grand Challenges in Global Health, this initiative initially focused on 14 major scientific challenges that, if solved, could lead to key advances in preventing, treating, and curing the diseases and health conditions contributing most to global health inequity ...
Why is it important to take on new challenges at work? ›One of the key benefits of taking on new challenges is that you get to experience something that takes you to different horizons. When you're in a job for almost 5 years, you may have exhausted all learning opportunities and feel that you're ready to start the next chapter of your work life.
What is the most challenging of social work? ›Working with vulnerable people
One unmissable challenge of a social work job is interacting with vulnerable people daily. To be successful in social work, you need to be patient, and empathetic and have strong communication and interpersonal skills.
- Pick a specific example. ...
- Consider the job description. ...
- Identify why it was a challenge. ...
- Be honest. ...
- Describe your reaction. ...
- Maintain a positive tone. ...
- Indicate what you learned.
Learning new things is always a challenge for me. But with my sincere efforts, hard work I make them look easy and simpler. It is a better way to handle any challenge. Toughest Challenged faced by me is when I joined the new college.
What do you think would be the biggest challenge for you in this role? ›
For example, you could say: “the biggest challenge will be getting to know the members of the team. I'm used to working with a large and diverse team and thrive on finding the best in my colleagues.” Or: “the biggest challenge in this job is keeping pace with technological change in this industry.
What are the grand challenges of our time? ›COVID-19 and vaccines. Black Lives Matter and social unrest. Climate change.
How many wins in a Grand Challenge? ›These challenges are divided into 2 types, Classic and Grand. Each battle won contributes to your final rewards with the maximum number of wins being 12. Losing 3 battles will boot you from the Challenge but rewards are granted to all competitors, even if they don't win any battles!
What are the 5 most challenging moments in life? ›- Quarter-life, Midlife Crisis. As we age, we see ourselves changing physically and mentally. ...
- Breakups. Love happens and breakups too. ...
- Changing Friendship. ...
- Failures. ...
- Divorce. ...
- Losing a Job. ...
- Getting Older. ...
- Getting Injured, Falling Sick.
Discover, Explore, Conserve, Share. Four Challenges lie at the heart of the John Muir Award.
How do you get 12 wins in Grand Challenge? ›- Picking a Deck.
- Master Your Deck.
- Playing in a Safe Environment.
- Playing while Mentally Stable and Healthy (DON'T TILT)
- Playing at less competitive times of the day – or year.
- Be Realistic.
- Avoid the Common Myths.
Actually, 10 Classic challenge wins equal a Grand Challenge win. And winning a Grand challenge has a 35.750% chance of legendary.
What is the top prize for classic challenge? ›Top prize for Classic Challenge are 22000 coins and 1100 cards. With each win in a challenge your final prize will get increases and you will get the top prize at 12 wins. At 3 losses, your challenge ends and you will receive your prize.
Why are the grand challenges important? ›Grand Challenges are ambitious but achievable goals that harness science, technology, and innovation to solve important national or global problems and that have the potential to capture the public's imagination.
How do you get grandmaster in challenges? ›To reach Grandmaster, you must be in the Top 25% of players who have also achieved Master and be above the Grandmaster threshold. To reach Challenger, you must be in the Top 5% of players who have also achieved Master and be above the Challenger threshold. Ladders are recalculated every 24 hours.
What is the hardest part about social work? ›
One unmissable challenge of a social work job is interacting with vulnerable people daily. To be successful in social work, you need to be patient, and empathetic and have strong communication and interpersonal skills.
How many grand challenges are there in social work? ›Grand Challenges have been used for more than a century to address significant societal issues. Explore the history of Grand Challenges, learn how the AASWSW developed the 12 Grand Challenges for Social Work, and meet the leaders of the Grand Challenges for Social Work initiative.
What are six core values of social work? ›Ethical Principles. The following broad ethical principles are based on social work's core values of service, social justice, dignity and worth of the person, importance of human relationships, integrity, and competence.