cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2gkpyp
title: CCDC 2281133: Experimental Crystal Structure Determination

description: Related Article: Tatiana Gridneva, Ayumu Karimata, Richa Bansal, Robert R. Fayzullin, Serhii Vasylevskyi, Andrew Bruhacs, Julia R. Khusnutdinova|2023|Chem.Commun.|60|212|doi:10.1039/D3CC04643E
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.cc2gl4nw
title: CCDC 2281558: Experimental Crystal Structure Determination

description: Related Article: Rong Zhang, Jiajing Lan, Fei Wang, Shumei Chen, Jian Zhang|2024|Chemical Science|15|726|doi:10.1039/D3SC05824G
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: 10.6084/m9.figshare.22810280
title: <strong>Motion state-dependent motor learning based on explicit visual feedback has limited spatiotemporal properties compared to adaptation to physical perturbations </strong>

description: All data used for analysis in the paper.
DOI: 10.6084/m9.figshare.22810280.v1
title: <strong>Motion state-dependent motor learning based on explicit visual feedback has limited spatiotemporal properties compared to adaptation to physical perturbations </strong>

description: All data used for analysis in the paper.
DOI: 10.5281/zenodo.10426250
title: NOAA NCCOS Mesophotic and Deep Benthic Communities: Mapping, Ground-truthing, and Predictive Habitat Modeling: Priority areas suggested for restoration and protection of mesophotic and deep benthic communities injured by the Deepwater Horizon oil spill in the Gulf of Mexico, 2021-10-12 to 2021-11-05

description: The National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process, and online application (Buja and Christensen 2019) to identify information needs in the northern Gulf of Mexico to restore mesophotic and deep benthic communities (MDBC) affected by the Deepwater Horizon oil spill. Sixty-four participants from local federal, state, academic, and other institutions entered their priorities in an online participatory Geographic Information System (pGIS) using virtual coins to denote their priorities in 25 x 25 kilometer (km) grid cells overlaid on the study area. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed using a pre-set list of options. Results were compiled, summarized, and mapped to identify high priority areas, reasons for those priorities, and information needs. The largest concentrations of high priority cells were located in two groups, one in the vicinity of the Deepwater Horizon/Macondo wellhead site, and another along the topography associated with the Flower Garden Banks National Marine Sanctuary (FGBNMS). Isolated or smaller groups of high priority cells occurred elsewhere in the study region and included South Texas Banks, parts of the Mississippi, DeSoto, and Green Canyons, the northern edges of the Sigsbee and West Florida Escarpments, and also Madison-Swanson and Steamboat Lumps on the West Florida Shelf. Participants consistently selected (1) Protection or management, (2) Exploration to identify new MDBC areas and (3) Selection of monitoring sites as their top reasons (i.e., justifications) for prioritizing locations.

They also selected the following three items as their top data or requirement needs (1) Identification of species of corals and their local environments, (2) Delineations of large topographic features, and (3) Documentation of individual corals. This ESRI shapefile summarizes the results from the study. Identifying these high priority areas is among the first steps in planning the 5-year Implementation Phase of the MDBC portfolio. Specific locations for implementing project activities will be identified based on this analysis as well as additional information such as the size and depth of seafloor features, presently available data as determined through gap analysis, requirements for new data acquisitions including model resolution and accuracy, as well as available ship time and survey equipment.
DOI: 10.5281/zenodo.10426249
title: NOAA NCCOS Mesophotic and Deep Benthic Communities: Mapping, Ground-truthing, and Predictive Habitat Modeling: Priority areas suggested for restoration and protection of mesophotic and deep benthic communities injured by the Deepwater Horizon oil spill in the Gulf of Mexico, 2021-10-12 to 2021-11-05

description: The National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process, and online application (Buja and Christensen 2019) to identify information needs in the northern Gulf of Mexico to restore mesophotic and deep benthic communities (MDBC) affected by the Deepwater Horizon oil spill. Sixty-four participants from local federal, state, academic, and other institutions entered their priorities in an online participatory Geographic Information System (pGIS) using virtual coins to denote their priorities in 25 x 25 kilometer (km) grid cells overlaid on the study area. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed using a pre-set list of options. Results were compiled, summarized, and mapped to identify high priority areas, reasons for those priorities, and information needs. The largest concentrations of high priority cells were located in two groups, one in the vicinity of the Deepwater Horizon/Macondo wellhead site, and another along the topography associated with the Flower Garden Banks National Marine Sanctuary (FGBNMS). Isolated or smaller groups of high priority cells occurred elsewhere in the study region and included South Texas Banks, parts of the Mississippi, DeSoto, and Green Canyons, the northern edges of the Sigsbee and West Florida Escarpments, and also Madison-Swanson and Steamboat Lumps on the West Florida Shelf. Participants consistently selected (1) Protection or management, (2) Exploration to identify new MDBC areas and (3) Selection of monitoring sites as their top reasons (i.e., justifications) for prioritizing locations.

They also selected the following three items as their top data or requirement needs (1) Identification of species of corals and their local environments, (2) Delineations of large topographic features, and (3) Documentation of individual corals. This ESRI shapefile summarizes the results from the study. Identifying these high priority areas is among the first steps in planning the 5-year Implementation Phase of the MDBC portfolio. Specific locations for implementing project activities will be identified based on this analysis as well as additional information such as the size and depth of seafloor features, presently available data as determined through gap analysis, requirements for new data acquisitions including model resolution and accuracy, as well as available ship time and survey equipment.
DOI: 10.34740/kaggle/dsv/7332784
title: Balochi Language to English Translation Dataset
DOI: 10.18434/mds2-3085
title: "cookiecutter-nist-python": A template for python packages with NIST specific features.
DOI: 10.5066/p92j89eq
title: Per- and Polyfluoroalkyl Substances in tree swallow gut contents

description: This data set is from a study examining the flow of per- and polyfluoroalkyl substances (PFAS) through food webs. The data are PFAS concentrations in gastrointestinal contents of tree swallows.
DOI: 10.6084/m9.figshare.24938509
title: Regulatory networks of circRNA- centred ceRNAs in sepsis-induced acute kidney injury

description: Sepsis is the primary cause of acute kidney injury (AKI) and is associated with high mortality rates. Growing evidence suggests that noncoding RNAs are vitally involved in kidney illnesses, whereas the role of circular RNAs (circRNAs) in sepsis-induced AKI (SAKI) remains largely unknown. In this present study, caecal ligation and puncture (CLP) in mice was performed to establish an SAKI model. The expression of circRNAs and mRNAs was analysed using circRNA microarray or next-generation sequencing. The results revealed that the expressions of 197 circRNAs and 2509 mRNAs were dysregulated. Validation of the selected circRNAs was performed by qRT-PCR. Bioinformatics analyses and chromatin immunoprecipitation demonstrated that NF-κB/p65 signalling induced the upregulation of circC3, circZbtb16, and circFkbp5 and their linear counterparts by p65 transcription in mouse tubular epithelial cells (mTECs). Furthermore, competitive endogenous RNA (ceRNA) networks demonstrated that some components of NF-κB signalling were potential targets of these dysregulated circRNAs. Among them, Tnf-α was increased by circFkbp5 through the downregulation of miR-760-3p in lipopolysaccharide (LPS)-stimulated mTECs. Knocking down circFkbp5 inhibited the p65 phosphorylation and apoptosis in injured mTECs. These findings suggest that the selected circRNAs and the related ceRNA networks provide new knowledge into the fundamental mechanism of SAKI and circFkbp5/miR-760-3p/Tnf-α axis might be therapeutic targets.
DOI: 10.6084/m9.figshare.24938509.v1
title: Regulatory networks of circRNA- centred ceRNAs in sepsis-induced acute kidney injury

description: Sepsis is the primary cause of acute kidney injury (AKI) and is associated with high mortality rates. Growing evidence suggests that noncoding RNAs are vitally involved in kidney illnesses, whereas the role of circular RNAs (circRNAs) in sepsis-induced AKI (SAKI) remains largely unknown. In this present study, caecal ligation and puncture (CLP) in mice was performed to establish an SAKI model. The expression of circRNAs and mRNAs was analysed using circRNA microarray or next-generation sequencing. The results revealed that the expressions of 197 circRNAs and 2509 mRNAs were dysregulated. Validation of the selected circRNAs was performed by qRT-PCR. Bioinformatics analyses and chromatin immunoprecipitation demonstrated that NF-κB/p65 signalling induced the upregulation of circC3, circZbtb16, and circFkbp5 and their linear counterparts by p65 transcription in mouse tubular epithelial cells (mTECs). Furthermore, competitive endogenous RNA (ceRNA) networks demonstrated that some components of NF-κB signalling were potential targets of these dysregulated circRNAs. Among them, Tnf-α was increased by circFkbp5 through the downregulation of miR-760-3p in lipopolysaccharide (LPS)-stimulated mTECs. Knocking down circFkbp5 inhibited the p65 phosphorylation and apoptosis in injured mTECs. These findings suggest that the selected circRNAs and the related ceRNA networks provide new knowledge into the fundamental mechanism of SAKI and circFkbp5/miR-760-3p/Tnf-α axis might be therapeutic targets.
DOI: 10.6073/pasta/ee9815b41b79c134fd714736ce98676a
title: Continuous precipitation and stream chemistry data, Hubbard Brook Ecosystem Study, 1963 – present.

description: The Hubbard Brook Watershed Stream and Precipitation Chemistry Record
 is a dataset of chemical concentration data for precipitation and
 streamwater samples that have been collected weekly since the summer
 of 1963 from streams and precipitation gauges throughout the Hubbard
 Brook Experimental Forest, a research forest in the White Mountains of
 New Hampshire. HBWatER currently collects weekly samples from nine
 gauged watersheds, the mainstem of the Hubbard Brook into which each
 small stream drains, and two long-term precipitation collection sites.
 HBWatER was begun in the 1960's by Gene E. Likens, F. Herbert Bormann,
 Robert S. Pierce, and Noye M. Johnson who began collecting and
 analyzing stream and precipitation (rain and snow). They had a simple
 idea, that by comparing watershed inputs in rain and snow to watershed
 outputs from streams, they could measure the behavior of entire
 ecosystems in response to atmospheric pollution or forestry practices.
 Stream and precipitation samples have been collected every week from
 1963 to the present day. Insights gained from studying this long-term
 chemical record led to the discovery of acid rain in North America and
 have documented the effectiveness of federal clean air legislation in
 reducing coalfired power plant emissions.

 
 The collection and analysis of HBWatER samples is currently sustained
 by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is
 curated and maintained by a team of researchers: Emma Rosi (Cary IES),
 Emily Bernhardt (Duke), Lindsey Rustad (USFS), John Campbell (USFS),
 Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Mark Green (Case
 Western), and Scott Bailey (USFS). Current Financial Support for
 HBWatER is provided by NSF LTREB # 1907683 and the USDA Forest Service
 Northern Research Station.

 
 These data were gathered as part of the Hubbard Brook Ecosystem Study
 (HBES). The HBES is a collaborative effort at the Hubbard Brook
 Experimental Forest, which is operated and maintained by the USDA
 Forest Service, Northern Research Station.
DOI: 10.5281/zenodo.10456067
title: General purpose Gaussian approximation potential for CO

description: This is a Gaussian approximation potential (GAP [1]) for carbon and oxygen. The potential can be used to model oxygen-containing carbon materials, including oxygenated graphitic and amorphous carbons. It has been fitted with gap_fit [2] by recomputing the a-C database of Deringer and Csányi [3] at the PBE level of theory [4] using the VASP code [5,6] and augmenting it to include other atomic structures, namely:



Dimers at closer interatomic separation than in [3], including O-O and C-O in addition to C-C

C60 isomers

Iteratively generated oxygen-containing carbon structures from low oxygen content up to approx. 50% oxygen content


Environment independent ("fixed C6") vdW corrections are included via tabulated two-body interactions with a damped London-dispersion functional form (1/R^6 decay). The vdW interactions are damped both in the short range (similar to the Tkatchenko-Scheffler scheme [7], but without the environment dependence) and long range (hard cutoff at 20 Angstrom and soft cutoff at 17.5 Angstrom). The parameters are optimized to reproduce the typical vdW interactions in graphitic carbon. Future versions of the CO-GAP may include support for more sophisticated vdW corrections.

For the underlying PBE fit, this potential uses 2-body (distance_2b) and 3-body (angle_3b) descriptors [2] plus SOAP-type descriptors (soap_turbo) [8,9], as implemented in the TurboGAP code [10]. The files can be used both with QUIP/GAP (and LAMMPS via the QUIP interface) and TurboGAP.

The authors acknowledge funding from the Academy of Finland (grants 321713, 330488, 336304, 355301 and 358050) and computational resources from the Finnish Center for Scientific Computing (CSC) and Aalto University's Science IT project.

A reference to the relevant paper with more details and applications of the potential will be published here when they are available.

References



A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).

S. Klawohn, J.P. Darby, J.R. Kermode, G. Csányi, M.A. Caro, and A.P. Bartók. J. Chem. Phys.159, 174108 (2023).

V.L. Deringer and G. Csányi. Phys. Rev. B 95, 094203 (2017).

J.P. Perdew, K. Burke, and M. Ernzerhof. Phys Rev. Lett. 77, 3865 (1996).

VASP: http://vasp.at

G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).

A. Tkatchenko and M. Scheffler. Phys. Rev. Lett. 102, 073005 (2009).

A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).

M.A. Caro. Phys. Rev. B 100, 024112 (2019).

TurboGAP: http://turbogap.fi


Contact

Miguel A. Caro: mcaroba@gmail.com or miguel.caro@aalto.fi
DOI: 10.5281/zenodo.10456133
title: COI rCRUX filtered metabarcoding reference database and naive-bayes classifier

description: COI metabarcoding database and naive-bayes classifier in QIIME2 .qza format, with Insecta and Amphibia sequences removed. Original database downloaded from here, built with rCRUX using the Leray CO1 primers.

rCRUX details

rCRUX generated by combining and de-replicating the following databases: 

Leray CO1-ncbi-mitochondrial (https://doi.org/10.5281/zenodo.8407603)

Leray CO1-embl (https://doi.org/10.5281/zenodo.8407606)

Leray CO1-searchterm (https://doi.org/10.5281/zenodo.8407620)

 

Primer Name:  Leray CO1Gene:   CO1Length of Target:    ~313Forward Sequence (5'-3'):   GGWACWGGWTGAACWGTWTAYCCYCCReverse Sequence (5'-3'):    TANACYTCnGGRTGNCCRAARAAYCAReference:   Leray, M., Yang, J. Y., Meyer, C. P., Mills, S. C., Agudelo, N., Ranwez, V., ... & Machida, R. J. (2013). A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Frontiers in zoology, 10(1), 34.

 

Details to filter database and train classifier:



1. Pull out all taxonomic identifiers from the matching to terms 'Insecta' or 'Amphibia' using grep

grep 'Insecta' CO1_combined_derep_and_clean_taxonomy.txt > CO1_combined_derep_and_clean_taxonomy-Insecta.txt

grep 'Amphibia' CO1_combined_derep_and_clean_taxonomy.txt > CO1_combined_derep_and_clean_taxonomy-Amphibia.txt

cat CO1_combined_derep_and_clean_taxonomy-Insecta.txt CO1_combined_derep_and_clean_taxonomy-Amphibia.txt > CO1_combined_derep_and_clean_taxonomy-Insecta-Amphibia.txt

 

2. Use this list of taxonomic identifiers to filter the taxonomy file for those two groups (Python script "grep-vf_Python.py"; attached here)

 

3. From the output file of grep-vf_Python.py, only the first column is the actual fasta header, so extract that column with awk:

awk '{print $1}' CO1_combined_derep_and_clean_taxonomy-noInsectaAmphibia.txt > filtered_taxa_toextract.txt4. Use this list of non-Amphibia or Insecta taxa to filter the original COI database fastaseqkit grep -f filtered_taxa_toextract.txt CO1_combined_derep_and_clean.fasta > CO1_combined_derep_and_clean-noInsectaAmphibia.fa

 

5. Convert the filtered fasta and taxonomy files to QIIME2 .qza format:

qiime tools import --type 'FeatureData[Sequence]' \--input-path CO1_combined_derep_and_clean-noInsectaAmphibia.fasta \--output-path COI_rCRUX_filt_20231110.qzaqiime tools import --type 'FeatureData[Taxonomy]' \--input-path CO1_combined_derep_and_clean_taxonomy-noInsectaAmphibia.txt \--output-path COI_rCRUX_taxonomy_filt_20231110.qza \--input-format 'HeaderlessTSVTaxonomyFormat'

 

6. Finally, train the classifier:

qiime feature-classifier fit-classifier-naive-bayes \--i-reference-reads COI_rCRUX_filt_20231110.qza \--i-reference-taxonomy COI_rCRUX_taxonomy_filt_20231110.qza \--p-classify--chunk-size 5000 \--o-classifier COI_rCRUX_filt_20231110-classifier.qza
DOI: 10.5281/zenodo.10456134
title: COI rCRUX filtered metabarcoding reference database and naive-bayes classifier

description: COI metabarcoding database and naive-bayes classifier in QIIME2 .qza format, with Insecta and Amphibia sequences removed. Original database downloaded from here, built with rCRUX using the Leray CO1 primers.

rCRUX details

rCRUX generated by combining and de-replicating the following databases: 

Leray CO1-ncbi-mitochondrial (https://doi.org/10.5281/zenodo.8407603)

Leray CO1-embl (https://doi.org/10.5281/zenodo.8407606)

Leray CO1-searchterm (https://doi.org/10.5281/zenodo.8407620)

 

Primer Name:  Leray CO1Gene:   CO1Length of Target:    ~313Forward Sequence (5'-3'):   GGWACWGGWTGAACWGTWTAYCCYCCReverse Sequence (5'-3'):    TANACYTCnGGRTGNCCRAARAAYCAReference:   Leray, M., Yang, J. Y., Meyer, C. P., Mills, S. C., Agudelo, N., Ranwez, V., ... & Machida, R. J. (2013). A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Frontiers in zoology, 10(1), 34.

 

Details to filter database and train classifier:



1. Pull out all taxonomic identifiers from the matching to terms 'Insecta' or 'Amphibia' using grep

grep 'Insecta' CO1_combined_derep_and_clean_taxonomy.txt > CO1_combined_derep_and_clean_taxonomy-Insecta.txt

grep 'Amphibia' CO1_combined_derep_and_clean_taxonomy.txt > CO1_combined_derep_and_clean_taxonomy-Amphibia.txt

cat CO1_combined_derep_and_clean_taxonomy-Insecta.txt CO1_combined_derep_and_clean_taxonomy-Amphibia.txt > CO1_combined_derep_and_clean_taxonomy-Insecta-Amphibia.txt

 

2. Use this list of taxonomic identifiers to filter the taxonomy file for those two groups (Python script "grep-vf_Python.py"; attached here)

 

3. From the output file of grep-vf_Python.py, only the first column is the actual fasta header, so extract that column with awk:

awk '{print $1}' CO1_combined_derep_and_clean_taxonomy-noInsectaAmphibia.txt > filtered_taxa_toextract.txt4. Use this list of non-Amphibia or Insecta taxa to filter the original COI database fastaseqkit grep -f filtered_taxa_toextract.txt CO1_combined_derep_and_clean.fasta > CO1_combined_derep_and_clean-noInsectaAmphibia.fa

 

5. Convert the filtered fasta and taxonomy files to QIIME2 .qza format:

qiime tools import --type 'FeatureData[Sequence]' \--input-path CO1_combined_derep_and_clean-noInsectaAmphibia.fasta \--output-path COI_rCRUX_filt_20231110.qzaqiime tools import --type 'FeatureData[Taxonomy]' \--input-path CO1_combined_derep_and_clean_taxonomy-noInsectaAmphibia.txt \--output-path COI_rCRUX_taxonomy_filt_20231110.qza \--input-format 'HeaderlessTSVTaxonomyFormat'

 

6. Finally, train the classifier:

qiime feature-classifier fit-classifier-naive-bayes \--i-reference-reads COI_rCRUX_filt_20231110.qza \--i-reference-taxonomy COI_rCRUX_taxonomy_filt_20231110.qza \--p-classify--chunk-size 5000 \--o-classifier COI_rCRUX_filt_20231110-classifier.qza
DOI: 10.6084/m9.figshare.24938505
title: Delirium current practice among Intensive Care Units Doctors, Khartoum- Underlying data

description: Delirium is a brain dysfunction that is characterized by attention and cognition disturbances in a fluctuating pattern. International guidelines recommend daily screening of delirium. The Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) and the Intensive Care Delirium Screening Checklist (ICDSC) were the most commonly used for delirium assessment. The reasons why this study was developed were to identify the barriers and gaps in knowledge and practice. Multi-center Hospital-based Cross-Sectional study was developed. Multistage sampling technique was used. 72 ICU doctors were randomly selected. Statistical analysis was performed through chi-square test to determine association among variables. It was considered significant when p ≤ 0.05. More than 70% of doctors were ≤ 30 years and females. 69.4% had &lt; 1year experience. 94.4% worked in medical ICUs. Less than 20% of the doctors used delirium assessment tools with a statistically significant difference based on experience (p=0.012). 13.9% had not assessed delirium regularly. Non-pharmacological management was applied by 76.4% of the doctors, communication with patients was the most frequent (75%). Haloperidol was the most used agent (83.3%). 40.3% of doctors had not stopped delirium medications on ICU discharge. Regular assessment of delirium was applied. Yet, the use of validated assessment tools was not common. Non-pharmacological management of delirium is important which is mostly done. Our doctors prescribed anti-psychotics for treatment of both forms of delirium and almost half of them did not stop medications on ICU discharge. Medication reconciliation and contact with the next in-charge of patients is important.