cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35077/age-egg
title: Taste_Fly_10: Whole brain confocal imaging of fly driver line dBMN-spGAL4 crossed with UAS-MCFO-3 responder for labeling individual neurons (single channel shown)

description: Confocal z-stacks (.tiff stacks) of single labeled mechanosensory neurons that innervate specific bristle populations on the Drosophila melanogaster head. The bristle mechanosensory neurons (BMNs) were stochastically labeled using driver lines that express in specific subsets of BMNs and the multicolor flipout reagent (Nern et al., 2015). The processed images of the BMNs are published in Figure 4 and Figure 4 – figure supplements 6-8 of the manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”. The confocal stacks used to produce these images are organized based on the specific bristle labelled (e.g. InOc or Taste bristles) and the individual fly that was tested (e.g. Fly_1 and Fly_2).
description: A full protocol describing this work is available in the associated manuscript at manuscript “Somatotopic organization among parallel sensory pathways that promote a grooming sequence in Drosophila (https://doi.org/10.1101/2023.02.11.528119)”.
description: DATASET METADATA: title:'Taste_Fly_10: Whole brain confocal imaging of fly driver line dBMN-spGAL4 crossed with UAS-MCFO-3 responder for labeling individual neurons (single channel shown)', GeneralModality:'other', Technique:'other', Other:'Stochastic labelling' SPECIMEN METADATA: LocalID:'Taste_Fly_10', Species:'Fruit fly', NCBITaxonomy:'NCBI:txid7227', Age:'4 to 6', Ageunit:'Days', Sex:'Male', Genotype:'w, R57C10-FLPG5.PEST (su(Hw)attP8); VT023783_AD (attP40)/+; R11D02_DBD (attP2)/10xUAS(FRT.stop)myr::smGdP-HA (VK00005), 10xUAS(FRT.stop)myr::smGdP-V5-THS-10xUAS(FRT.stop)myr::smGdP-FLAG (su(Hw)attP1)', OrganName:'Brain', SampleLocalID:'C1_20201028_11D02_DBD_VT02378_AD_MCFO_1_to_2_days_20x_brain_3', Locations:'Subesophageal zone (SEZ)' INSTRUMENT METADATA: MicroscopeType:'Confocal', MicroscopeManufacturerAndModel:'Zeiss LSM 800 Axio Imager.Z2', ObjectiveName:'Zeiss 20x objective (Plan-Apochromat 20x/0.8)', ObjectiveNA:'0.8', ObjectiveMagnification:'20x' IMAGE METADATA: xAxis:'left-to-right', yAxis:'left-to-right', zAxis:'left-to-right', Number:'1,red', displayColor:'(1.0, 0.0, 0.0)', stepSizeX:'0.6239', stepSizeY:'0.6239'
DOI: 10.57830/2300463
title: Modeling Potential Changes in Seagrass Coverage, Biomass and Vulnerability Under Increased Temperature

description: Increased water temperature from global climate change may exacerbate existing stresses to eelgrass (Zostera marina) meadows throughout the northeastern United States, possibly leading to declines in populations. In this study, we developed a data-driven model to estimate how seagrass distribution and abundance will likely change with expected temperature increases under climate-change scenarios and applied the model to Pleasant Bay, Massachusetts. Long-term seagrass and water quality monitoring data along with satellite temperature data were used to generate the spatial distribution of environmental drivers across the Bay. These data were then used in a 0-D point-model that incorporated both empirical and mechanistic relationships to predict future spatial seagrass distribution and abundance assuming increases of 1.2°C and 1.95°C by the year 2050. The model demonstrated decline in distribution and abundance with increasing temperature alongside estimated 49 to 93% reductions in biomass. There was a complete loss of regions able to sustain seagrass under the highest temperature scenario. Most of the predicted loss occurred along the shallow and deep edges of the meadows effectively squeezing eelgrass into a narrow depth range where both light and temperature conditions remain favorable for eelgrass growth. These results are intended to help inform the development of targeted conservation and management actions to address the region-wide downward trajectory and facilitate recovery.
DOI: 10.5517/cctrdwt
title: CCDC 767277: Experimental Crystal Structure Determination

description: Related Article: C.Thone, N.Narro, P.G.Jones|2010|CSD Communication|||
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/cctrdyw
title: CCDC 767279: Experimental Crystal Structure Determination

description: Related Article: C.Thone, F.Vancea, P.G.Jones|2010|CSD Communication|||
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.cc1ljm4w
title: CCDC 1475728: Experimental Crystal Structure Determination

description: Related Article: Mark E. Light, Peter Edwards, Philip A. Gale|2016|CSD Communication|||
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.cc20wp1n
title: CCDC 1873641: Experimental Crystal Structure Determination

description: Related Article: Daniel T. Walters,  Xian B. Powers,  Marilyn M. Olmstead,  Alan L. Balch|2019|Chem.-Eur.J.|25|3849|doi:10.1002/chem.201805247
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.cc20wp0m
title: CCDC 1873640: Experimental Crystal Structure Determination

description: Related Article: Daniel T. Walters,  Xian B. Powers,  Marilyn M. Olmstead,  Alan L. Balch|2019|Chem.-Eur.J.|25|3849|doi:10.1002/chem.201805247
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.cc241fm8
title: CCDC 1967589: Experimental Crystal Structure Determination

description: Related Article: Karel Škoch, Petr Vosáhlo, Ivana Císařová, Petr Štěpnička|2020|Dalton Trans.|49|1011|doi:10.1039/C9DT04521J
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.cc28pgxc
title: CCDC 2106013: Experimental Crystal Structure Determination

description: Related Article: Essi Taipale, Marcel Siepmann, Khai-Nghi Truong, Kari Rissanen|2021|Chem.-Eur.J.|27|17412|doi:10.1002/chem.202103152
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.8271032
title: SuperDARN Grid data in netCDF format (2017-Dec)

description: 2017-Dec SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.8271033
title: SuperDARN Grid data in netCDF format (2017-Dec)

description: 2017-Dec SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.8270944
title: SuperDARN Grid data in netCDF format (1998-Jan)

description: 1998-Jan SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.8270945
title: SuperDARN Grid data in netCDF format (1998-Jan)

description: 1998-Jan SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.8271022
title: SuperDARN Grid data in netCDF format (2007-Jan)

description: 2007-Jan SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.8271023
title: SuperDARN Grid data in netCDF format (2007-Jan)

description: 2007-Jan SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.57982/v50x-0b95
title: Antibiotic Safety in Infants with Complicated Intra-Abdominal Infections - Metronidazole

description: NICHD-2013-ABS01 was a prospective, open-label, partially randomized, multicenter trial. The primary objective of this study was to evaluate the safety of drug regimens consisting of ampicillin, metronidazole, clindamycin, piperacillin-tazobactam, and gentamycin in assigned or standard of care groups of infants with complicated intra-abdominal infections. Secondary objectives included evaluations of efficacy and population pharmacokinetics (PK). Results demonstrate that metronidazole therapy at the protocol-specified doses was safe for premature infants with complicated intra-abdominal infections (Group 1) and late preterm/term infants (Group 4). Therapeutic success was achieved in 81% and 96% of participants in Groups 1 and 4, respectively. Only one AE in each group was attributable to study drug. None of the outcomes of special interest were attributable to metronidazole. Collected PK data validated the protocol-specified dosing regimen.
DOI: 10.6084/m9.figshare.23717673
title: Supplementary Material for: Key prognostic factors create a composite risk score to stratify patients into high- and low treatment benefit groups: A multicenter, retrospective data analysis of 84 mCRC patients treated with regorafenib as part of the CORRECT and CONSIGN trials

description: Introduction: In further-line mCRC treatment, median progression-free survival (PFS) is rather short, and many patients do not benefit from any anti-tumor treatment and should therefore treated according to best-supportive care (BSC). A risk score based on standard laboratory values using markers of tumor inflammation aims to define a patient cohort with high treatment benefit and might offer insights into tumor biology. As regorafenib has been dropped off the German market due to an unfavorable risk-benefit ratio, patient selection is key for any further-line treatment option. Methods: We used cox regression analysis to determine lab markers that are independent prognostic factors of OS and PFS outcome. The influence of these variables was weighted using an estimator, which was calculated using cox regression analysis. The estimators were implemented as multiplication factors, resulting in a risk score. A cut-off value for the resulting risk values was then determined via cox regression analysis resulting in a low- and high-risk subgroup. Results: Using data of 82 patients, a risk score identifying long-term survival in patients with last line mCRC treatment could be calculated. The following parameters were associated with significantly longer survival in multivariate analysis: NLR ≤ 5 (p = &lt;0.001), AP ≤200 U/L (p = 0.001), CRP ≤3.2mg/dl (p = &lt;0.001). The following estimator values were used to calculate a risk score: NLR: 0.132 (p=0.046), AP: 0.004 (p=0.014), and CRP: 0.032 (p=0.039). Implementing the estimators as multiplication factors yielded the following risk score: 0.132*NLR + 0.004*AP + 0.032*CRP = Risk value. Cox regression resulted in low- and high-risk subgroups with risk values below and above 1.4, respectively. In the group with a low-risk score (&lt;1.4), patients had a median OS of 10.5 months after initiating regorafenib. Patients with a high-risk score (&gt;1.4) survived only 3.3 months after starting therapy with regorafenib (n=43, p&lt;0.001, HR=3.76). Discussion/Conclusions: The presented composite risk score stratifies patients into two prognostic subgroups characterized by standard laboratory values. Patients with signs of systemic characterized by elevated NLR, AP, and CRP have a high composite risk score and a significant shorter overall survival. Although this score needs to be prospectively validated in larger cohorts, it may be used to stratify patients suitable for further-line treatment studies.
DOI: 10.6084/m9.figshare.23717673.v1
title: Supplementary Material for: Key prognostic factors create a composite risk score to stratify patients into high- and low treatment benefit groups: A multicenter, retrospective data analysis of 84 mCRC patients treated with regorafenib as part of the CORRECT and CONSIGN trials

description: Introduction: In further-line mCRC treatment, median progression-free survival (PFS) is rather short, and many patients do not benefit from any anti-tumor treatment and should therefore treated according to best-supportive care (BSC). A risk score based on standard laboratory values using markers of tumor inflammation aims to define a patient cohort with high treatment benefit and might offer insights into tumor biology. As regorafenib has been dropped off the German market due to an unfavorable risk-benefit ratio, patient selection is key for any further-line treatment option. Methods: We used cox regression analysis to determine lab markers that are independent prognostic factors of OS and PFS outcome. The influence of these variables was weighted using an estimator, which was calculated using cox regression analysis. The estimators were implemented as multiplication factors, resulting in a risk score. A cut-off value for the resulting risk values was then determined via cox regression analysis resulting in a low- and high-risk subgroup. Results: Using data of 82 patients, a risk score identifying long-term survival in patients with last line mCRC treatment could be calculated. The following parameters were associated with significantly longer survival in multivariate analysis: NLR ≤ 5 (p = &lt;0.001), AP ≤200 U/L (p = 0.001), CRP ≤3.2mg/dl (p = &lt;0.001). The following estimator values were used to calculate a risk score: NLR: 0.132 (p=0.046), AP: 0.004 (p=0.014), and CRP: 0.032 (p=0.039). Implementing the estimators as multiplication factors yielded the following risk score: 0.132*NLR + 0.004*AP + 0.032*CRP = Risk value. Cox regression resulted in low- and high-risk subgroups with risk values below and above 1.4, respectively. In the group with a low-risk score (&lt;1.4), patients had a median OS of 10.5 months after initiating regorafenib. Patients with a high-risk score (&gt;1.4) survived only 3.3 months after starting therapy with regorafenib (n=43, p&lt;0.001, HR=3.76). Discussion/Conclusions: The presented composite risk score stratifies patients into two prognostic subgroups characterized by standard laboratory values. Patients with signs of systemic characterized by elevated NLR, AP, and CRP have a high composite risk score and a significant shorter overall survival. Although this score needs to be prospectively validated in larger cohorts, it may be used to stratify patients suitable for further-line treatment studies.
DOI: 10.25403/upresearchdata.23681127
title: Smallholder farmers on the impact of adoption of conservation agriculture in sub-Saharan African countries: Kenya, Zimbabwe, Tanzania and Zambia

description: This dataset shows the data used in the study to inform the impact of adoption of conservation agriculture in areas of Sub-Suharan Africa i.e. Kenya, Tanzania, Zambia and Zimbabwe. The dataset contains the self-reflective responses by smallholder farmers in the areas of Laikipia county (Kenya) and Mashonaland (Zimbabwe) upon their adoption of conservation agriculture practices on their livelihoods outcomes such as farm production, food security, income, gender disparity, social cohesion, soil fertility and sustainability. The dataset was collected following a joint collaboration project to encourage adoption of conservation agriculture by The Common Market for Eastern and Southern Africa (<em>COMESA</em>) and the African Conservation Tillage network (ACT).