cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25053644
title: Data: Sex-specific growth and lifespan effects of germline removalin the dioecious nematode <i>Caenorhabditis remanei</i>

description: Data for: doi.org/10.1101/2023.12.07.570570Please see manuscript for detailed methods.<b>RNAi treatments</b><br>glp-1 = germline removal<br>control = green flourescent protein<b>Growth data</b><br>dev.time = development time until maturity (days)<br>d0.size = size at maturity (mm2)<br>d4.size = adult size at day 4 (mm2)<b>Lifespan mated</b><br>Kanamycin treatment - many plates were infected by unwanted bacteria and treated with the antibiotic Kanamycin. This also extended the lifespan of treated worms.
DOI: 10.5281/zenodo.10559642
title: Data from: Abrupt boundaries between mountain meadows and forests separate ground-dwelling invertebrate communities: a case study from South Tyrol, Italy

description: Here, we provide raw abundance data from a small-scale case study on the effects of abrupt habitat shifts on ground-dwelling macro-invertebrate communities in extensively managed hay meadows and mixed forest stands in South Tyrol, Italy. The fauna was sampled with the pitfall trap methods in spring 2019. Araneae and Formicidae were identified to species level, the rest – where possible – to family level.

The abundance data (activity densities) can be found as absolute numbers (i.e. individuals per pitfall trap) and as standardised numbers (i.e. individuals per sampling day). Additionally, we provide the Red List statuses for Araneae and Formicidae.
DOI: 10.5281/zenodo.10559643
title: Data from: Abrupt boundaries between mountain meadows and forests separate ground-dwelling invertebrate communities: a case study from South Tyrol, Italy

description: Here, we provide raw abundance data from a small-scale case study on the effects of abrupt habitat shifts on ground-dwelling macro-invertebrate communities in extensively managed hay meadows and mixed forest stands in South Tyrol, Italy. The fauna was sampled with the pitfall trap methods in spring 2019. Araneae and Formicidae were identified to species level, the rest – where possible – to family level.

The abundance data (activity densities) can be found as absolute numbers (i.e. individuals per pitfall trap) and as standardised numbers (i.e. individuals per sampling day). Additionally, we provide the Red List statuses for Araneae and Formicidae.
DOI: 10.6084/m9.figshare.25054550
title: Table S1-S3.docx

description: This study shows the frequency of TB among ILD patients.
DOI: 10.6084/m9.figshare.25054550.v1
title: Table S1-S3.docx

description: This study shows the frequency of TB among ILD patients.
DOI: 10.5281/zenodo.10555601
title: Data from: Facultative migration in two thrush species (Fieldfare and Redwing): rowanberry abundance more important than winter weather.

description: Description of data table on fieldfare and redwing

 

Columns included are:

 

Time period/Year: E.g. 80 = 1980-81 = December 1980-February 1981

 

Fieldfare (Turdus pilaris) individuals/redwing (T. iliacus) individuals: number of individuals reported to the National Biodiversity Information Centre in Norway (www.artsobservasjoner.no) for each time period (December-February) and for each region of Norway (eastern Norway and western Norway; see paper for detailed explanation of each region).

 

Bird records: Total number of records of all bird species reported to artsobservasjoner.no for each time period and for each region. One record contains an observation of one or more individuals of one species at one site at one date. Total number of records was considered to reflect observation effort.

 

Fieldfare index/redwing index: The ratio of number of individuals of fieldfare or redwing to total number of bird records.

 

Fieldfare index/redwing index (log): Log (x) for fieldfare and log (x + 0.0001) for redwing.

 

Rowanberry index: Annual index of rowanberry flowers based on the "Varsling innen PlanteSkadegjørere" project (VIPS, see www.vips-landbruk.no/applefruitmoth/) run by the Norwegian Institute of Bioeconomy Research (NIBIO) and Norsk Landbruksrådgivning (the Norwegian Agricultural Extension Office). See paper for details of calculations.

 

NAO indices: monthly values for November-January and mean of November-January based on data from the National Weather Service in the US (www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml).

 

Temp (Temperature) indices: monthly values for November-January and mean of November-January based on data from the Norwegian Meteorological Institute (www.yr.no).

 

Snow: Total number of days with snowcover (≥ 1 cm) for each month during November-January and the sum across the three months based on data from the Norwegian Meteorological Institute (www.yr.no).
DOI: 10.5281/zenodo.10555602
title: Data from: Facultative migration in two thrush species (Fieldfare and Redwing): rowanberry abundance more important than winter weather.

description: Description of data table on fieldfare and redwing

 

Columns included are:

 

Time period/Year: E.g. 80 = 1980-81 = December 1980-February 1981

 

Fieldfare (Turdus pilaris) individuals/redwing (T. iliacus) individuals: number of individuals reported to the National Biodiversity Information Centre in Norway (www.artsobservasjoner.no) for each time period (December-February) and for each region of Norway (eastern Norway and western Norway; see paper for detailed explanation of each region).

 

Bird records: Total number of records of all bird species reported to artsobservasjoner.no for each time period and for each region. One record contains an observation of one or more individuals of one species at one site at one date. Total number of records was considered to reflect observation effort.

 

Fieldfare index/redwing index: The ratio of number of individuals of fieldfare or redwing to total number of bird records.

 

Fieldfare index/redwing index (log): Log (x) for fieldfare and log (x + 0.0001) for redwing.

 

Rowanberry index: Annual index of rowanberry flowers based on the "Varsling innen PlanteSkadegjørere" project (VIPS, see www.vips-landbruk.no/applefruitmoth/) run by the Norwegian Institute of Bioeconomy Research (NIBIO) and Norsk Landbruksrådgivning (the Norwegian Agricultural Extension Office). See paper for details of calculations.

 

NAO indices: monthly values for November-January and mean of November-January based on data from the National Weather Service in the US (www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/nao.shtml).

 

Temp (Temperature) indices: monthly values for November-January and mean of November-January based on data from the Norwegian Meteorological Institute (www.yr.no).

 

Snow: Total number of days with snowcover (≥ 1 cm) for each month during November-January and the sum across the three months based on data from the Norwegian Meteorological Institute (www.yr.no).
DOI: 10.5281/zenodo.4033352
title: Single fiber tensile test - Glass and Carbon fibers

description: Single fiber test data and SEM scan behind the publications 

Kumar, R., L.P. Mikkelsen, H. Lilholt, B. Madsen, Understanding the mechanical response of glass and carbon fibres: stress-strain analysis and modulus determination IOP Conf. Ser.: Mater. Sci. Eng. 942, 012033, https://doi.org/10.1088/1757-899X/942/1/012033, 2020

Rajnish Kumar, Lars P Mikkelsen, Hans Lilholt and Bo Madsen, Experimental Method for Tensile Testing of Unidirectional Carbon Fibre Composites Using Improved Specimen Type and Data Analysis, Materials, 14, 3939, https://doi.org/10.3390/ma14143939, 2021.

and

Rajnish Kumar, Lars P Mikkelsen, Hans Lilholt and Bo Madsen, Weibull parameters determined from a comprehensive dataset of tensile testing fo single carbon fibers, Submitted, 2024

to where a reference should be given. 

A video describing the test-setup can be found in the following link: https://panopto.dtu.dk/Panopto/Pages/Viewer.aspx?id=50c10945-0612-4126-9e99-b00700cfbaf2&start=0 

The data-set cover single fiber test of a carbon and glass fiber in the gauge section range from 20-80 mm for carbon fiber and from 40-80 mm for glass fiber. There are three files for each gauge-section:

... Data.xslx: The individual tensile curves with one sheet for each fiber. There are around 150 fibers in each set

... Results.xslx: One excel-sheet containing a summary of the parameters obtained for the individual fibers.

....Figures.doc: Word file showing a plot of the graphs 

In addition to this, there are for the carbon and glass fiber case a test setup complience calculation saved in the 

.... Compliance.xslx excel-sheet

and SEM scans of a large approximately 2x20 mm cross-section of a pultruded profile based on the carbon fibers. This scan can be used for validating the fiber diameter distribution found in the single fiber testing. The SEM scan is saved in the

Carbon_HyFisyn... files

This set of files also include a matlab file (.m) which are used for determine the fiber volume fraction of the composite. 

 

Scripts to plot and analysing the data can be found on the following places:

Google Colab: https://colab.research.google.com/drive/1GdmRGOuy6SUuRXdbbyxemczgAUZl8JaM?usp=sharing

Code Ocean:  Lars P. Mikkelsen, Rajnish Kumar (2022) Understanding the mechanical response of glass and carbon fibres: stress-strain analysis and modulus determination [Source Code]. https://doi.org/10.24433/CO.5998905.v1
DOI: 10.5281/zenodo.10535761
title: GEMINI output used to develop volumetric reconstruction technique for EISCAT 3D

description: This dataset is used as a "ground truth" for investigating the performance of a volumetric reconstruction technique of electric current densities, intended to be applied to the EISCAT 3D radar system. The technique is outlined in a mnuscript in preparation, to be referred to here once submitted. The volumetric reconstruction code can be found here: https://github.com/jpreistad/e3dsecs

 

This dataset contain four files:

1) Dataset file 'gemini_dataset.nc'. This is a dump from the end of a GEMINI model run driven with a pair of up/down FAC above the region around the EISCAT 3D facility. Detailes of the GEMINI model can be found here: https://doi.org/10.5281/zenodo.3528915 . This is a NETCDF file, intended to be opened with xarray in python: 

import xaray

dataset = xarray.open_dataset('gemini_dataset.nc')

 

2) Grid file 'gemini_grid.h5'. This file is needed to get information about the grid that the values from GEMINI are represented in. The E3DSECS library (https://github.com/jpreistad/e3dsecs) has the necessary code to open this file and put it into the dictionary structure used in that package. 

 

3) The GEMINI simulation config file 'config.nml' used to produce the simulation.

 

4) The GEMINI boundary file 'fac_said.py' used to produce the boundary conditions for the simulation

Together files 3 and 4 could be used to reproduce the full simulation of the GEMINI model, which is freely available at https://github.com/gemini3d

 

The configuration files for this particular run are also available at this location:

https://github.com/gemini3d/gemini-examples/tree/main/init/aurora_curv
DOI: 10.5281/zenodo.10535762
title: GEMINI output used to develop volumetric reconstruction technique for EISCAT 3D

description: This dataset is used as a "ground truth" for investigating the performance of a volumetric reconstruction technique of electric current densities, intended to be applied to the EISCAT 3D radar system. The technique is outlined in a mnuscript in preparation, to be referred to here once submitted. The volumetric reconstruction code can be found here: https://github.com/jpreistad/e3dsecs

 

This dataset contain three files:

1) Dataset file 'gemini_dataset.nc'. This is a dump from the end of a GEMINI model run driven with a pair of up/down FAC above the region around the EISCAT 3D facility. Detailes of the GEMINI model can be found here: https://doi.org/10.5281/zenodo.3528915 . This is a NETCDF file, intended to be opened with xarray in python: 

import xaray

dataset = xarray.open_dataset('gemini_dataset.nc')

 

2) Grid file 'gemini_grid.h5'. This file is needed to get information about the grid that the values from GEMINI are represented in. The E3DSECS library (https://github.com/jpreistad/e3dsecs) has the necessary code to open this file and put it into the dictionary structure used in that package. 

 

3) The GEMINI simulation config file 'config.nml' used to produce the simulation. This could be used to reproduce the full simulation of the GEMINI model, which is freely available at https://github.com/gemini3d
DOI: 10.5281/zenodo.10561460
title: GEMINI output used to develop volumetric reconstruction technique for EISCAT 3D

description: This dataset is used as a "ground truth" for investigating the performance of a volumetric reconstruction technique of electric current densities, intended to be applied to the EISCAT 3D radar system. The technique is outlined in a mnuscript in preparation, to be referred to here once submitted. The volumetric reconstruction code can be found here: https://github.com/jpreistad/e3dsecs

 

This dataset contain four files:

1) Dataset file 'gemini_dataset.nc'. This is a dump from the end of a GEMINI model run driven with a pair of up/down FAC above the region around the EISCAT 3D facility. Detailes of the GEMINI model can be found here: https://doi.org/10.5281/zenodo.3528915 . This is a NETCDF file, intended to be opened with xarray in python: 

import xaray

dataset = xarray.open_dataset('gemini_dataset.nc')

 

2) Grid file 'gemini_grid.h5'. This file is needed to get information about the grid that the values from GEMINI are represented in. The E3DSECS library (https://github.com/jpreistad/e3dsecs) has the necessary code to open this file and put it into the dictionary structure used in that package. 

 

3) The GEMINI simulation config file 'config.nml' used to produce the simulation.

 

4) The GEMINI boundary file 'fac_said.py' used to produce the boundary conditions for the simulation

Together files 3 and 4 could be used to reproduce the full simulation of the GEMINI model, which is freely available at https://github.com/gemini3d

 

The configuration files for this particular run is also available at this location:

https://github.com/gemini3d/gemini-examples/tree/main/init/aurora_curv
DOI: 10.4121/d9706a2a-b77b-412f-a3aa-6e22bd8ddf4a.v1
title: Data and models used for paper 'The impact of future climate projections and anthropogenic activities on basin-scale groundwater availability'

description: Data and models used in the paper with the title mentioned above (readme file is available)The analysis starts with future climate projection followed by hydrological simulation using the wflow_sbm model. It is followed by groundwater flow simulation using MODFLOW model, driven by the groundwater recharge produced by hydrological simulation, and bounded by future groundwater abstraction scenarios. At last, the figures in the paper are produced.
DOI: 10.34683/sr1108
title: StoneRounds Case: SR1108
title: ECORD

description: StoneRounds Case SR1108 was diagnosed with ECORD likely caused by mutations in the gene: RPGR.
DOI: 10.34808/pkbz-2v37
title: The ab initio and experimental study of the spectroscopic and magnetic properties of Ho(III)-EDTA

description: In this dataset, the ab initio calculations of the electronic structure and the magnetic properties are discussed in the context of the experimental data for the Ho–EDTA complex. In the calculations different models of the cluster have been applied to examine the influence of various parts of the environment of the Ho(III)-EDTA complex on its properties. In particular, the calculations support the expectation that the presence of the water molecules in the first coordination sphere has considerable impact on the oscillator strengths of the electronic transitions and the magnetic susceptibility of the material under study. We have reconsidered the effect of the dynamical electron correlation on the magnetic susceptibility.
DOI: 10.35081/s706-y441
title: Database Punaises Comores
DOI: 10.15128/r26395w717f
title: Disappearing Polymorphs Reappear in the Mill: The Case of Ritonavir [dataset]

description: Organic compounds can crystallise in different forms known as polymorphs. Discovery and control of polymorphism is of key importance to the pharmaceutical industry since polymorphs can have significantly different physical properties impacting, therefore, their utilisation in drug delivery. Certain polymorphs have been reported to ‘disappear’ from the physical world leading to polymorphs. These unwanted polymorph conversions (initially prevented by the slow nucleation kinetics of the stable forms) are driven by significant gains in thermodynamic stabilities. The most infamous of these cases is that of the HIV drug ritonavir: once its reluctant form was unwillingly nucleated for the first time, its desired form could not be produced again with the same manufacturing process. Here we show that Ritonavir’s extraordinary disappearing polymorph as well as its reluctant form can be consistently produced by ball-milling under different environmental conditions. We demonstrate that the significant difference in stability between its polymorphs can be changed and reversed in the mill -a process we show (with the aid of computer simulations) is driven by crystal size as well as crystal shape and conformational effects. We also show that those effects can be controlled through careful changes in the milling conditions since they dictate the kinetics of crystal breakage, dissolution, and growth processes that eventually lead to steady-state crystal sizes and shapes in the mill. This work highlights the huge potential of mechanochemistry in polymorph discovery of forms initially hard to nucleate, recovery of disappearing polymorphs, and polymorph control of complex flexible drug compounds such as Ritonavir.
description: Powder diffraction data for milled products and reference materials.
DOI: 10.6084/m9.figshare.22335301
title: Data for: Effects of the temperature during embryonic development on adult reproduction and the phenotype of the second generation in zebra finches

description: Data from the publication: Effects of the temperature during embryonic development on adult reproduction and the phenotype of the second generation in zebra finches. All methods to produce the data and statistical analyses used to analyze the data are described in the manuscript.Please see the readme file for descriptions of column names.Statistical analyses can be accessed in the "script.R" file.
DOI: 10.6084/m9.figshare.22335301.v1
title: Data for: Effects of the temperature during embryonic development on adult reproduction and the phenotype of the second generation in zebra finches

description: Data from the publication: Effects of the temperature during embryonic development on adult reproduction and the phenotype of the second generation in zebra finches. All methods to produce the data and statistical analyses used to analyze the data are described in the manuscript.Please see the readme file for descriptions of column names.Statistical analyses can be accessed in the "script.R" file.
DOI: 10.6084/m9.figshare.22606030
title: MAGs genome sequences (high-medium quality dereplicated)

description: MAGs genome sequences (4568 high-medium quality dereplicated).
DOI: 10.5281/zenodo.7515489
title: Analysis of the Operational and Safety Features of the in-core Bubbling System of the Molten Salt Fast Reactor - Dataset

description: Dataset associated to the journal paper "Analysis of the Operational and Safety Features of the In-core Bubbling System of the Molten Salt Fast Reactor"
DOI: 10.5281/zenodo.7515490
title: Analysis of the Operational and Safety Features of the in-core Bubbling System of the Molten Salt Fast Reactor - Dataset

description: Dataset associated to the journal paper "Analysis of the Operational and Safety Features of the In-core Bubbling System of the Molten Salt Fast Reactor"
DOI: 10.5281/zenodo.8272090
title: MotionMiners Missplacement Dataset

description: The MotionMiners Miss-placement Dataset 𝑀𝑃1 is composed of recordings of seven subjects carrying out different activities in the intralogistics, using a sensor set-up of On-Body Devices (OBDs) for industrial applications. Here, the position and orientation of the OBD change with respect to the recording-and-usage guidelines. The OBDs are labeled with respect to their expected location on the human body, namely, 𝑂𝐵𝐷𝑅 , 𝑂𝐵𝐷𝐿 and 𝑂𝐵𝐷𝑇 on the right arm, left arm, and frontal torso. Tab. 1 (see manuscript) presents the different miss-placement classes of the dataset. This dataset considers the miss-placement as a classification problem; however, differently, the 𝑀𝑃 dataset considers rotations miss-placements—commonly appear on deployment from practitioners experience.
The 𝑀𝑃 dataset contains recordings of seven subjects performing six activities: Standing, Walking, Handling Centred, Handling Upwards, Handling Downwards, and an additional Synchronisation. Each subject carried out each activity under the case of up to 15 different miss-placement situations (soon updating to 20 different miss-placement situations), including a correct set-up of the devices. The 𝑀𝑃 dataset is divided in two subsets, 𝑀𝑃_A and 𝑀𝑃_B. 
Each recording of a subject contains:

raw data of Acc, Gyr, and Mag in 3D for a certain number of samples, making a matrix of size [Samples times 27]
annotated data of Acc, Gyr, and Mag in 3D for a certain number of samples, making a matrix of size [Samples, Act class, [27 channels]]

for MP_B, it includes the synchronized recording of the correct sensor set-up, so the matrix becomes  [Samples, class, [27 channels of the miss-placed setup], [27 channels of the correct set up]]
the miss-placement annotations [Samples, Miss-placement class]
the activity annotations [Samples, activity class, [19 semantic attributes]]

the semantic attributes are given following the following paper: "LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes", Sensors 2020, DOI: 10.3390/s20154083.
If you use this dataset for research, please cite the following paper: "Miss-placement Prediction of Multiple On-body Devices for Human Activity Recognition", Sensors 2020, DOI: 10.1145/3615834.3615838.
For any questions about the dataset, please contact Fernando Moya Rueda at fernando.moya@motionminers.com.