cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13130/rd_unimi/wm3dgm/tls0ah
title: cecr1b-LoF+dmso (47).tif
DOI: 10.13130/rd_unimi/wm3dgm/njfobg
title: ctrl+dmso (11).tif
DOI: 10.15468/dl.qqsbhy
title: Occurrence Download

description: A dataset containing 44300 species occurrences available in GBIF matching the query:
{
 "and" : [
 "MediaType is Image",
 "OccurrenceStatus is Present",
 "VerbatimScientificName is Sturnus vulgaris",
 "Year 2025-2025"
 ]
}

 The dataset includes 44300 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039863-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13130/rd_unimi/wm3dgm/8vivjx
title: ctrl+dmso (20).tif
DOI: 10.13130/rd_unimi/wm3dgm/fmnbcw
title: cecr1b-LoF+CGS (8).tif
DOI: 10.13130/rd_unimi/wm3dgm/a9nctf
title: ctrl+dmso (29).tif
DOI: 10.13130/rd_unimi/wm3dgm/d1esmn
title: ctrl+dmso (5).tif
DOI: 10.13130/rd_unimi/wm3dgm/wvr67k
title: cecr1b-LoF+dmso (22).tif
DOI: 10.13130/rd_unimi/wm3dgm/clpujo
title: cecr1b-LoF+dmso (7).tif
DOI: 10.13130/rd_unimi/wm3dgm/i4rb2w
title: ctrl+dmso (22).tif
DOI: 10.13130/rd_unimi/wm3dgm/nskrx5
title: cecr1b-LoF+dmso (25).tif
DOI: 10.13130/rd_unimi/wm3dgm/p9zrja
title: cecr1b-LoF+dmso (26).tif
DOI: 10.13130/rd_unimi/wm3dgm/qesdar
title: cecr1b-LoF+CGS (7).tif
DOI: 10.13130/rd_unimi/wm3dgm/0mjehc
title: cecr1b-LoF+CGS (22).tif
DOI: 10.13130/rd_unimi/wm3dgm/hiiiro
title: cecr1b-LoF+dmso (2).tif
DOI: 10.13130/rd_unimi/wm3dgm/hfa41t
title: cecr1b-LoF+dmso (20).tif
DOI: 10.13130/rd_unimi/wm3dgm/sblsov
title: cecr1b-LoF+dmso (18).tif
DOI: 10.13130/rd_unimi/wm3dgm/livptv
title: cecr1b-LoF+dmso (9).tif
DOI: 10.13130/rd_unimi/wm3dgm/xfn0kf
title: ctrl+dmso (26).tif
DOI: 10.13130/rd_unimi/wm3dgm/7r6yqp
title: ctrl+dmso (36).tif
DOI: 10.13130/rd_unimi/wm3dgm/dxsskb
title: cecr1b-LoF+dmso (6).tif
DOI: 10.13130/rd_unimi/wm3dgm/mtdr5f
title: cecr1b-LoF+CGS (17).tif
DOI: 10.13130/rd_unimi/wm3dgm/ixbhf8
title: cecr1b-LoF+dmso (2).tif
DOI: 10.13130/rd_unimi/wm3dgm/dttelx
title: cecr1b-LoF+CGS (20).tif
DOI: 10.13130/rd_unimi/wm3dgm/krmuxq
title: cecr1b-LoF+CGS (18).tif
DOI: 10.13130/rd_unimi/wm3dgm/y0n4za
title: cecr1b-LoF+dmso (37).tif
DOI: 10.13130/rd_unimi/wm3dgm/z63f4m
title: ctrl+dmso (8).tif
DOI: 10.13130/rd_unimi/wm3dgm/pdpbh1
title: ctrl+dmso (13).tif
DOI: 10.13130/rd_unimi/wm3dgm/eho98l
title: ctrl+dmso (20).tif
DOI: 10.13130/rd_unimi/wm3dgm/5noeo8
title: cecr1b-LoF+CGS (16).tif
DOI: 10.15468/dl.zvvz26
title: Occurrence Download

description: A dataset containing 6216355 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Carduelis carduelis"
 ]
}

 The dataset includes 6216355 records from 5434 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0039841-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fzfx6f
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dolichopteroides Parin, Belyanina & Evseenko, 2009",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((42.8012 -16.5547,42.4342 -16.6376,42.2188 -17.038,42.391 -17.4288,42.6657 -17.5533,43.0375 -17.4752,43.2579 -17.0968,43.0959 -16.69,42.8012 -16.5547))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008553-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.18260514
title: Dataset for "Large Language Models for Quality Control of Large Language Models"

description: This is the dataset for the paper "Large Language Models for Quality Control of Large Language Models" (accepted for publication and presentation at MODELSWARD 2026). It contains all supplementary data relevant to the study (e.g., prompts and output).
DOI: 10.5281/zenodo.18260515
title: Dataset for "Large Language Models for Quality Control of Large Language Models"

description: This is the dataset for the paper "Large Language Models for Quality Control of Large Language Models" (accepted for publication and presentation at MODELSWARD 2026). It contains all supplementary data relevant to the study (e.g., prompts and output).
DOI: 10.7910/dvn/m2ldf2
title: BlendedNet Multi-Fidelity Extension Dataset: HF-LF Aerodynamics for BWB Aircraft

description: &lt;p&gt;This dataset contains 7,871 paired aerodynamic samples for Blended Wing Body (BWB) aircraft, designed to support multi-fidelity machine learning and surrogate modeling research. It extends the original BlendedNet high-fidelity dataset by adding corresponding low-fidelity evaluations.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Dataset Contents:&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;strong&gt;Input:&lt;/strong&gt; 14-dimensional vector (10 geometric variables, 4 flight conditions).&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Output:&lt;/strong&gt; 4 scalar aerodynamic coefficients per sample: Lift (C&lt;sub&gt;L&lt;/sub&gt;) and Drag (C&lt;sub&gt;D&lt;/sub&gt;) for both fidelities (C&lt;sub&gt;L&lt;/sub&gt;&lt;sup&gt;HF&lt;/sup&gt;, C&lt;sub&gt;D&lt;/sub&gt;&lt;sup&gt;HF&lt;/sup&gt;, C&lt;sub&gt;L&lt;/sub&gt;&lt;sup&gt;LF&lt;/sup&gt;, C&lt;sub&gt;D&lt;/sub&gt;&lt;sup&gt;LF&lt;/sup&gt;).&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;High-Fidelity (HF):&lt;/strong&gt; RANS CFD results (NASA FUN3D) from the original BlendedNet dataset.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Low-Fidelity (LF):&lt;/strong&gt; Vortex Lattice Method (VLM) results computed using OpenVSP (VSPAERO).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Usage:&lt;/strong&gt;&lt;br&gt;
This resource is intended for researchers developing multi-fidelity algorithms (e.g., DeepONets, Cokriging, Transfer Learning) who require a large-scale, physics-aligned benchmark.&lt;/p&gt;
description: DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

This material is based upon work supported by the Department of the Air Force under Air Force Contract No. FA8702-15-D-0001 or FA8702-25-D-B002. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of the Air Force.
DOI: 10.6084/m9.figshare.31075375
title: ExoShorkie: Predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning

description: All datasets used in the study <b><i>“ExoShorkie: Predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning”</i></b> are publicly available. These include six primary exogenous genome RNA-seq datasets used for model training and evaluation, as well as a yeast genomic RNA-seq dataset. For each study, we provide a FASTA reference file for the corresponding exogenous genome together with uniformly processed RNA-seq coverage data generated using our standardized preprocessing pipeline.The original datasets were obtained from the following studies:The RNA helicases Dbp2 and Mtr4 regulate the expression of Xrn1-sensitive long non-coding RNAs in yeast (Yeast genomic RNA-seq)Regulatory activity is the default DNA state in eukaryotes (Human chromosome 7)Synthetic reversed sequences reveal default genomic states (HPRT1 and HPRT1R)Sequence-dependent activity and compartmentalization of foreign DNA in a eukaryotic nucleus (M. pneumoniae and M. mycoides)Exogenous artificial DNA forms chromatin structure with active transcription in yeast (Data-storage chromosome)<br><br><br><br><br>
DOI: 10.6084/m9.figshare.31075375.v2
title: ExoShorkie: Predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning

description: All datasets used in the study <b><i>“ExoShorkie: Predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning”</i></b> are publicly available. These include six primary exogenous genome RNA-seq datasets used for model training and evaluation, as well as a yeast genomic RNA-seq dataset. For each study, we provide a FASTA reference file for the corresponding exogenous genome together with uniformly processed RNA-seq coverage data generated using our standardized preprocessing pipeline.The original datasets were obtained from the following studies:The RNA helicases Dbp2 and Mtr4 regulate the expression of Xrn1-sensitive long non-coding RNAs in yeast (Yeast genomic RNA-seq)Regulatory activity is the default DNA state in eukaryotes (Human chromosome 7)Synthetic reversed sequences reveal default genomic states (HPRT1 and HPRT1R)Sequence-dependent activity and compartmentalization of foreign DNA in a eukaryotic nucleus (M. pneumoniae and M. mycoides)Exogenous artificial DNA forms chromatin structure with active transcription in yeast (Data-storage chromosome)<br><br><br><br><br>
DOI: 10.6084/m9.figshare.31075375.v1
title: ExoShorkie: Predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning

description: All datasets used in the study <b><i>“ExoShorkie: Predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning”</i></b> are publicly available. These include six primary exogenous genome RNA-seq datasets used for model training and evaluation, as well as a yeast genomic RNA-seq dataset. For each study, we provide a FASTA reference file for the corresponding exogenous genome together with uniformly processed RNA-seq coverage data generated using our standardized preprocessing pipeline.The original datasets were obtained from the following studies:The RNA helicases Dbp2 and Mtr4 regulate the expression of Xrn1-sensitive long non-coding RNAs in yeast (Yeast genomic RNA-seq)Regulatory activity is the default DNA state in eukaryotes (Human chromosome 7)Synthetic reversed sequences reveal default genomic states (HPRT1 and HPRT1R)Sequence-dependent activity and compartmentalization of foreign DNA in a eukaryotic nucleus (M. pneumoniae and M. mycoides)Exogenous artificial DNA forms chromatin structure with active transcription in yeast (Data-storage chromosome)<br><br><br><br><br>
DOI: 10.15468/dl.yr4pc3
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scardinius erythrophthalmus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1860",
 "Year is less than or equal to 1864"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012370-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mzn7eq
title: Occurrence Download

description: A dataset containing 2942 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "Country is United States of America",
 "StateProvince is California",
 "Year is 2013",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 2942 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012371-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/ul7ibn
title: Second order Synaptic Plasticity with multistate memory in flexible MoO3-Aloe vera composite Devices for low energy Neuromorphic applications

description: Human brain can adopt to the signals and evaluate them accordingly to create short term and long-
term memories. The prior activity can influence such short-term and long-term memory. Artificial
synapse which can modulate its weight updates depending on the history of stimulation is analogues
to human brain. Here, we show that such second order memristive can be fabricated using a simple
pencil-on-paper (POP) technique with MoO3 - Aloe vera composite as active layer and few layer
graphene (FLG) as electrodes. Apart from achieving fundamental synaptic characteristics in the
device, strain dependent synaptic performance is also evaluated. FLG/MoO3 - Aloe vera/FLG de-
vice demonstrates very stable memory states, with multi-bit memory capabilities when configured to
various conductance states under both flat and strained conditions. The devices can be operated
at low energy (∼ pJ) which is essential for low energy consuming neuromorphic devices. Synaptic
weight updates strongly depend on the prior activity which helps to control learning and forgetting
characteristics. Further, the nonlinearity factors (NLF) in neuromorphic characteristics can be con-
trolled by optimizing the stimulation parameters. This work proposes a platform for developing an
environmentally friendly, flexible neuromorphic devices for next generation low energy neuromorphic
applications.
DOI: 10.25934/pr00010193.0
title: Available datapackage for study 'A PHASE 3 RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED, MULTICENTER STUDY OF THE ANALGESIC EFFICACY AND SAFETY OF THE SUBCUTANEOUS ADMINISTRATION OF TANEZUMAB IN SUBJECTS WITH OSTEOARTHRITIS OF THE HIP OR KNEE'

description: Tanezumab is a monoclonal antibody that binds to and inhibits the actions of nerve growth factor (NGF). The Nerve Growth Factor Inhibitor (NGFI) class may offer an important breakthrough in the treatment of chronic pain and is under clinical investigation for the treatment of pain associated with osteoarthritis or other chronic pain conditions. The primary objective of this study is to demonstrate superior efficacy of tanezumab 5 mg and 2.5 mg administered subcutaneously (SC) every 8 weeks versus placebo at Week 24 in subjects with osteoarthritis of the knee or hip. The 2.5 mg dose was shown to provide efficacy benefits with a favorable safety profile when administered intravenously in previous Phase 3 clinical trials. The 5 mg dose is expected to provide added efficacy benefit over the 2.5 mg dose based on data from previous studies.
DOI: 10.5281/zenodo.18260503
title: SuperDARN data in netCDF format (1994-Sep)

description: 1994-Sep SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, 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/rst



The 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)



Recommended form of acknowledgement for the use of SuperDARN data:



'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.6833045
title: SuperDARN data in netCDF format (1994-Sep)

description: 1994-Sep SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, 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/rst



The 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)



Recommended form of acknowledgement for the use of SuperDARN data:



'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: 10.15468/dl.f9mt7b
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scardinius erythrophthalmus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1865",
 "Year is less than or equal to 1869"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012372-260108223611665/datasets/export for details.
DOI: 10.5281/zenodo.17994717
title: Curated Experimental MOF Structures with Assigned Charges for Simulation

description: This dataset contains curated experimental metal–organic framework (MOF) structures with assigned atomic charges, prepared for adsorption and binding-site simulations. It was used in the study “How well do conventional atomistic simulations predict adsorption binding sites in MOFs compared to experiment?” The dataset enables direct benchmarking of common force fields and charge-assignment schemes.

Directory Naming Convention

Each directory label encodes the key details of the corresponding simulation. The label is composed of sections separated by a double underscore (__). The directory labels closely match the MOF names reported in Table 1, using filesystem-compatible formatting, following the convention:

<Structure_Name>__<Guest_Molecule>__<Temperature>__<Pressure>

Structure Preprocessing

All structures were obtained from experimental crystallographic data, either from the Cambridge Structural Database (CSD) or from the supplementary information of the original publications. Each structure was carefully corrected to remove artifacts such as missing ligands, disordered fragments, and missing protons. The corrected structures were converted to P1 symmetry and their protons were geometry-optimized using DFT, as detailed in the Methods section of the paper. Atomic charges were then assigned using several commonly employed charge-assignment methods in adsorption simulations.

Each folder contains the following charge-assigned cif files:

Optimized structure: *_opt.cifDDEC6 charges (DFT-fitted via Chargemol): *_opt_ddec6.cifREPEAT charges (DFT-fitted): *_opt_repeat.cifMEPO-QEQ empirical charges: *_opt_mepoqeq.cifMEPO-ML machine-learned charges (trained on REPEAT): *_opt_mepoml.cif

Reference CSV File

These charge-assigned experimental structures are ready for GCMC, isotherm prediction, or binding-site localization studies. The dataset also includes a reference index file named Experimental_MOF_Dataset.csv. This CSV file provides a readable mapping between each directory and its reference DOI. In addition, the columns below were added as well:



In_Table1_Set: Identifies the MOFs included in Table 1 of the main text, where the core binding-site analyses were conducted using UFF and REPEAT.

In_RMSE_RMSD_Set: Marks the MOFs used to calculate RMSE and RMSD differences across all force-field and charge-method combinations.

Has_Isotherms: Indicates the structures for which adsorption isotherms were computed and included in the study.
DOI: 10.5281/zenodo.17459735
title: Curated Experimental MOF Structures with Assigned Charges for Simulation

description: This dataset contains curated experimental metal–organic framework (MOF) structures with assigned atomic charges, prepared for adsorption and binding-site simulations. It was used in the study “How well do conventional atomistic simulations predict adsorption binding sites in MOFs compared to experiment?” The dataset enables direct benchmarking of common force fields and charge-assignment schemes.

Directory Naming Convention

Each directory label encodes the key details of the corresponding simulation. The label is composed of sections separated by a double underscore (__). The directory labels closely match the MOF names reported in Table 1, using filesystem-compatible formatting, following the convention:

<Structure_Name>__<Guest_Molecule>__<Temperature>__<Pressure>

Structure Preprocessing

All structures were obtained from experimental crystallographic data, either from the Cambridge Structural Database (CSD) or from the supplementary information of the original publications. Each structure was carefully corrected to remove artifacts such as missing ligands, disordered fragments, and missing protons. The corrected structures were converted to P1 symmetry and their protons were geometry-optimized using DFT, as detailed in the Methods section of the paper. Atomic charges were then assigned using several commonly employed charge-assignment methods in adsorption simulations.

Each folder contains the following charge-assigned cif files:

Optimized structure: *_opt.cifDDEC6 charges (DFT-fitted via Chargemol): *_opt_ddec6.cifREPEAT charges (DFT-fitted): *_opt_repeat.cifMEPO-QEQ empirical charges: *_opt_mepoqeq.cifMEPO-ML machine-learned charges (trained on REPEAT): *_opt_mepoml.cif

Reference CSV File

These charge-assigned experimental structures are ready for GCMC, isotherm prediction, or binding-site localization studies. The dataset also includes a reference index file named Experimental_MOF_Dataset.csv. This CSV file provides a readable mapping between each directory and its reference DOI. In addition, the columns below were added as well:



In_Table1_Set: Identifies the MOFs included in Table 1 of the main text, where the core binding-site analyses were conducted using UFF and REPEAT.

In_RMSE_RMSD_Set: Marks the MOFs used to calculate RMSE and RMSD differences across all force-field and charge-method combinations.

Has_Isotherms: Indicates the structures for which adsorption isotherms were computed and included in the study.
DOI: 10.15468/dl.8aa85u
title: Occurrence Download

description: A dataset containing 126 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Thailand",
 "OccurrenceStatus is Present",
 "TaxonKey is Colletotrichum gloeosporioides"
 ]
}

 The dataset includes 126 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012373-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.