cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc267wbp
title: CCDC 2033362: Experimental Crystal Structure Determination

description: Related Article: Folkert de Vries, Raquel Travieso-Puente, Peter Roewen, Edwin Otten|2020|Organometallics|40|63|doi:10.1021/acs.organomet.0c00720
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: 10.5517/ccdc.csd.cc267wcq
title: CCDC 2033363: Experimental Crystal Structure Determination

description: Related Article: Folkert de Vries, Raquel Travieso-Puente, Peter Roewen, Edwin Otten|2020|Organometallics|40|63|doi:10.1021/acs.organomet.0c00720
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: 10.5517/ccdc.csd.cc267wdr
title: CCDC 2033364: Experimental Crystal Structure Determination

description: Related Article: Folkert de Vries, Raquel Travieso-Puente, Peter Roewen, Edwin Otten|2020|Organometallics|40|63|doi:10.1021/acs.organomet.0c00720
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.7595594
title: Antismash results for 321 strains

description: Antismash results of 321 Streptomyces strains, which have publicly available genomes with maximum 300 contigs and minimum 90 % completeness.
DOI: 10.5281/zenodo.7595593
title: Antismash results for 321 strains

description: Antismash results of 321 Streptomyces strains, which have publicly available genomes with maximum 300 contigs and minimum 90 % completeness.
DOI: 10.5281/zenodo.7489116
title: Sargassum sp extract improve Hematological profile of Tilapia fish (Oreochromis niloticus)

description: Strategies to increase body resistance and prevent disease in cultivation include using vaccines, antibiotics, and probiotics. Today, the use of antibiotics with natural ingredients is becoming a trend. One of the natural ingredients that contain high antioxidants and antibiotics is <em>Sargassum</em> sp. This research was conducted from March to May 2022 at the Biotechnology Laboratory, Faculty of Fisheries and Marine, Universitas Riau. This research was conducted in two stages, namely 1) the sensitivity of extracts of <em>Sargassum</em> sp. and 2) the application of <em>Sargassum</em> sp. extract orally in tilapia (<em>O. niloticus</em>). The results showed that the extract of <em>Sargassum</em> sp. was able to inhibit the growth o<em>f Aeromonas hydrophila </em>bacteria with a clear zone of 6.5-15.0 mm, which is classified as resistant. At doses of 2000, 2500, and 3000 ppm, it did not cause death in fish for 96 hours (LD<sub>50</sub>). Hematological parameters can be a sign of the health status of fish. Tilapia given to the addition of <em>Sargassum</em> sp. with different doses gave an effect between treatments, both after 30 days of rearing and post-test against <em>A.hydrophila</em> bacteria (p&lt;0.05). The results showed that the hematology of fish fed with <em>Sargassum</em> sp. extract was in the normal or healthy range. Healthy tilapia had erythrocyte counts ranging from 1.34-2.11 x10<sup>6</sup> cells/mm<sup>3</sup>, hematocrit 26.17-33.19%, hemoglobin 6.26-11.2 g/dL and total leukocytes 1.01-1.50 x10<sup>4</sup> cells/mm<sup>3</sup> and total erythrocytes 5.88-9.13x10<sup>4</sup> cells/ mm<sup>3</sup>. A dose of 3000 ppm provided the highest health improvement against <em>A.hydrophila</em> bacterial infection
DOI: 10.34740/kaggle/dsv/4932637
title: Nifty-50 & Nifty Future 50 Dataset
DOI: 10.5281/zenodo.7594891
title: P0-mip3_cerebellum_ssSEM_dataset

description: P0-mip3_cerebellum_ssSEM_dataset Produced in collaboration between the Zurzolo lab at Institut Pasteur and the Lichtman lab at Harvard University
DOI: 10.5281/zenodo.7594890
title: P0-mip3_cerebellum_ssSEM_dataset

description: P0-mip3_cerebellum_ssSEM_dataset Produced in collaboration between the Zurzolo lab at Institut Pasteur and the Lichtman lab at Harvard University
DOI: 10.15139/s3/mnfnzv
title: Fast Friends Discussion Between Romantic Partners, 2017

description: We recruited 107 romantic couples (214 people total), including 16 same-sex couples (12 lesbian couples; 4 gay couples), who responded to advertisements on the University of Michigan campus, in the community, and online (e.g., Facebook). Participants self-reported their race/ethnicity as 65% Caucasian, 14% Asian, 7% Arican, 5% multi/bi-racial, 3% Hispanic/Latino, 3% Indian/Pakistani, 1% American-Indian, and 2% other. To be eligible, participants had to be between 18 to 40 years old (because of age-related changes in hormones), speak and read English fluently, and be in a romantic relationship for at least three months. Smokers, people taking any hormonal medication (e.g., hormonal birth control), and pregnant women were not eligible. People who were interested in the study followed a link to an online survey, which they were to complete independently from their partner. After providing informed consent and confirming eligibility, participants completed a battery of personality questionnaires (e.g., Big Five), which are not included in the present report. Once both partners completed the initial survey, they were invited to schedule a lab session together. Upon arrival to the laboratory, participants provided their first saliva sample while simultaneously completing a pre-task survey (to collect mood and relationship measures) at computers placed at opposite ends of the room to ensure independence and allow privacy. After the pre-task measures were completed, couples were seated in armchairs located in a section of our laboratory designed to resemble a living room. They were randomly assigned one of two discussion conditions, modelled closely after the “fast friends” (experimental condition) and “small talk” (control condition) tasks developed by Aron et al. (1997). We have videotape recordings of behaviors (face and body), coded behavioral synchrony between partners, transcribed their conversation, coded laughter, coded sexual desire and nurturance, and assayed testosterone and cortisol before and after this task. Once couples completed the discussion task, they returned to the computers to complete a post-task survey, which included a post-task questionnaire (similar to the pre-task questionnaire) and the same measures of relationship closeness, relationship quality, and positive and negative affect described earlier. We followed up with couples 1 month and approximately 1 year post-lab session to measure relationship status, attachment, and relationship quality. All procedures were reviewed and approved by the University of Michigan Institutional Review Board, approval #: HUM00130413
DOI: 10.6073/pasta/cf4e3102e656d6e2ec63346664f24a43
title: WAT02 Climate legacies determine grassland responses to future rainfall regimes

description: Climate variability and periodic droughts have complex effects on carbon (C) fluxes, with uncertain implications for ecosystem C balance under a changing climate. Responses to climate change can be modulated by persistent effects of climate history on plant communities, soil microbial activity, and nutrient cycling (i.e., legacies). To assess how legacies of past precipitation regimes influence tallgrass prairie C cycling under new precipitation regimes, we modified a long-term irrigation experiment that simulated a wetter climate for &gt;25 years. We reversed irrigated and control (ambient precipitation) treatments in some plots and imposed an experimental drought in plots with a history of irrigation or ambient precipitation to assess how climate legacies affect aboveground net primary productivity (ANPP), soil respiration, and selected soil C pools. Legacy effects of elevated precipitation (irrigation) included higher C fluxes and altered labile soil C pools, and in some cases altered sensitivity to new climate treatments. Indeed, decades of irrigation reduced the sensitivity of both ANPP and soil respiration to drought compared with controls. Positive legacy effects of irrigation on ANPP persisted for at least 3 years following treatment reversal, were apparent in both wet and dry years, and were associated with altered plant functional composition. In contrast, legacy effects on soil respiration were comparatively short-lived and did not manifest under natural or experimentally-imposed “wet years,” suggesting that legacy effects on CO2 efflux are contingent on current conditions. Although total soil C remained similar across treatments, long-term irrigation increased labile soil C and the sensitivity of microbial biomass C to drought. Importantly, the magnitude of legacy effects for all response variables varied with topography, suggesting that landscape can modulate the strength and direction of climate legacies. Our results demonstrate the role of climate history as an important determinant of terrestrial C cycling responses to future climate changes.
DOI: 10.34740/kaggle/dsv/4932702
title: NSE-Scripts-Data
DOI: 10.6073/pasta/b9161d52f701426f19c6dc7f4c492ac9
title: WAT03 Climate legacy effects shape tallgrass prairie nitrogen cycling

description: Climate change is expected to shift precipitation regimes in the North American Central Plains with likely impacts on ecosystem functioning. In tallgrass prairies, water and nitrogen (N) can co-limit ecosystem processes, so changes in precipitation may have complex effects on carbon (C) and N cycling. Rates of N supply such as N mineralization and nitrification respond differently to short- and long-term patterns in water availability, and previous climate patterns may exert legacy effects on current N cycling that could alter ecosystem sensitivity to current precipitation regimes. We used a long-term precipitation manipulation at Konza Prairie (Kansas, USA) to assess how previous and current precipitation influence tallgrass prairie N cycling. Supplemental irrigation was applied across upland and lowland prairie for ~25 years to reduce water deficits; in 2017, we reversed some of these treatments and added a reduced rainfall treatment across both historic rainfall regimes, allowing us to assess how previous climate and current rainfall patterns interact to shape N cycling. In lowland prairie, previous irrigation doubled N mineralization and nitrification rates the year following cessation of irrigation. Reduced microbial C/N ratio and lower relative investment in N-acquiring enzymes in previously irrigated lowlands suggested that a wetter climate created a legacy of increased N availability for microbes. Internal plant N resorption increased under short-term irrigation but recovered to ambient levels following previous irrigation. Together, these results suggest that a history of wetter conditions prairie can create a legacy of accelerated N cycling and with consequences for both plant and microbial functioning.
DOI: 10.5281/zenodo.6616412
title: A Scatter Search approach for the Parallel Row Ordering Problem

description: Instance data and full results used in https://doi.org/10.1007/978-3-031-26504-4_40
DOI: 10.5281/zenodo.6616411
title: A Scatter Search approach for the Parallel Row Ordering Problem

description: Instance data and full results used in https://doi.org/10.1007/978-3-031-26504-4_40
DOI: 10.5281/zenodo.7590916
title: Sketching Sounds Dataset

description: This dataset contains sketches produced by 88 participants in a perceptual study. Participants sketched their associations with 20 synthesised sounds that are included in the dataset. Next to the sketches created on the sounds, the dataset features 2 test sketches (with the keys <em>calmSound</em> and <em>noisySound</em>) that each participant created. The keys for each sound-sketch are in the format <em>drawingInterface_ + sound ID</em> (e.g. <em>drawingInterface_a7ecc7b965e2e7888e2df382514ad101</em>). In addition, sketches for an attention test that asked participants to draw the number 4 are saved under the key <em>drawingInterface_attentionTest</em>. The JSON file contains the raw sketch data is structured as follows: <pre><code>{ participant1_id: { sound1_id: [...], sound2_id: [...], sound3_id: [...], ...}, participant2_id: { sound1_id: [...], sound2_id: [...], sound3_id: [...], ...}, . . . }</code></pre> Sketch data is saved in the format: <pre><code>#Sketch array [#Stroke 1 array [#Recorded stroke points [x0,x1,x2,...], #x positions [y0,y1,y2,...], #y positions [t0,t1,t2,...], #timestamps ], #More Stroke arrays [...], [...], ... ]</code></pre> The dataset also contains rasterised images generated from the sketch data.
DOI: 10.5281/zenodo.7590915
title: Sketching Sounds Dataset

description: This dataset contains sketches produced by 88 participants in a perceptual study. Participants sketched their associations with 20 synthesised sounds that are included in the dataset. Next to the sketches created on the sounds, the dataset features 2 test sketches (with the keys <em>calmSound</em> and <em>noisySound</em>) that each participant created. The keys for each sound-sketch are in the format <em>drawingInterface_ + sound ID</em> (e.g. <em>drawingInterface_a7ecc7b965e2e7888e2df382514ad101</em>). In addition, sketches for an attention test that asked participants to draw the number 4 are saved under the key <em>drawingInterface_attentionTest</em>. The JSON file contains the raw sketch data is structured as follows: <pre><code>{ participant1_id: { sound1_id: [...], sound2_id: [...], sound3_id: [...], ...}, participant2_id: { sound1_id: [...], sound2_id: [...], sound3_id: [...], ...}, . . . }</code></pre> Sketch data is saved in the format: <pre><code>#Sketch array [#Stroke 1 array [#Recorded stroke points [x0,x1,x2,...], #x positions [y0,y1,y2,...], #y positions [t0,t1,t2,...], #timestamps ], #More Stroke arrays [...], [...], ... ]</code></pre> The dataset also contains rasterised images generated from the sketch data.
DOI: 10.5281/zenodo.7595744
title: A traffic graph dataset for autonomous driving research: Commonroad-Nuplan-Dataset

description: We provide a standardized graph dataset for traffic based on the large-scale NuPlan v1.1 dataset, converted to a PyTorch-Geometric dataset using our CommonRoad-Geometric tool. The dataset is collected from 3 megacities: Singapore, Boston and Pittsburgh.
DOI: 10.5281/zenodo.7566797
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for FloodNet/10-class segmentation of RGB 1024x768 UAV images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for FloodNet/10-class segmentation of RGB 1024x768<strong> </strong>UAV images</strong></em> These Residual-UNet model data are based on [FloodNet](https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021) images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021 Image size used by model: 1024 x 768 x 3 pixels <em>classes:</em><br> 1. Background<br> 2. Building-flooded<br> 3. Building-non-flooded<br> 4. Road-flooded<br> 5. Road-non-flooded<br> 6. Water<br> 7. Tree<br> 8. Vehicle<br> 9. Pool<br> 10. Grass <em>File descriptions</em> For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <em>References</em><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Rahnemoonfar, M., Chowdhury, T., Sarkar, A., Varshney, D., Yari, M. and Murphy, R.R., 2021. Floodnet: A high resolution aerial imagery dataset for post flood scene understanding. IEEE Access, 9, pp.89644-89654.
DOI: 10.5281/zenodo.7566796
title: Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for FloodNet/10-class segmentation of RGB 1024x768 UAV images

description: <em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for FloodNet/10-class segmentation of RGB 1024x768<strong> </strong>UAV images</strong></em> These Residual-UNet model data are based on [FloodNet](https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021) images and associated labels. Models have been created using Segmentation Gym* using the following dataset**: https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021 Image size used by model: 1024 x 768 x 3 pixels <em>classes:</em><br> 1. Background<br> 2. Building-flooded<br> 3. Building-non-flooded<br> 4. Road-flooded<br> 5. Road-non-flooded<br> 6. Water<br> 7. Tree<br> 8. Vehicle<br> 9. Pool<br> 10. Grass <em>File descriptions</em> For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU <em>References</em><br> *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ** Rahnemoonfar, M., Chowdhury, T., Sarkar, A., Varshney, D., Yari, M. and Murphy, R.R., 2021. Floodnet: A high resolution aerial imagery dataset for post flood scene understanding. IEEE Access, 9, pp.89644-89654.
DOI: 10.5281/zenodo.7567683
title: Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"

description: Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"
DOI: 10.5281/zenodo.7567682
title: Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"

description: Data for the paper "Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems"
DOI: 10.25625/afr4sk/j0agch
title: Continuous Integration and Deployment_Development for GWDG Gitlab projects.pdf

description: :unav
DOI: 10.25625/afr4sk
title: Slides for the hands-on mini-workshop session "Continuous Integration and Delivery/Deployment for GWDG Gitlab projects"

description: Slides for the mini-workshop on "Continuous Integration and Delivery/Deployment for GWDG Gitlab projects"
DOI: 10.5281/zenodo.7324854
title: Supplemental Data and Code for "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE"

description: This dataset enables the user to repeat the analyses presented in the manuscript "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE." It is comprised of two compressed archives: one which contains code, and one which contains data.
DOI: 10.5281/zenodo.6461580
title: Supplemental Data and Code for "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE"

description: This dataset enables the user to repeat the analyses presented in the manuscript "An exact version of Life Table Response Experiment analysis, and the R package exactLTRE." It is comprised of two compressed archives: one which contains code, and one which contains data.
DOI: 10.5517/ccdc.csd.cc208zfq
title: CCDC 1855673: Experimental Crystal Structure Determination

description: Related Article: Uttam Mishra|2018|CSD Communication|||
description: Related Article: Uttam K. Mishra,  Kaushalendra Patel,  S. S. V. Ramasastry|2019|Org.Lett.|21|175|doi:10.1021/acs.orglett.8b03537
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.