cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8103760
title: thgie/critical-reading-vice: Initial Release

description: These scripts accompanied a case study of distant reading the source code of the VICE emulator. More information on the study can be found via https://phd.thgie.ch/notes/vice.html. A resulting article is to be published in 2024.
DOI: 10.5281/zenodo.10396735
title: anonymous4open
DOI: 10.5281/zenodo.10396734
title: anonymous4open
DOI: 10.5281/zenodo.10817280
title: German Twitter Data on Occupations

description: Background

There are many valuable insights on jobs and professions in different sectors of society based on their imminent and ascribed characteristics. Studying such characteristics traditionally was done by action research, surveys, questionnaires, etc. which typically take much time and resources to be concluded. This dataset accompanies our studies where we examine vocational education and training data on Twitter. We presented a generic framework to retrieve, process and analyze tweets, and discussed  research questions from computational social science: For example, how can we make Twitter data interoperable to otheravailable resources, e.g. classifications of occupations, tools and skills? Second, do we have enough data to process job collocational prestige analysis on a geographical basis? This presents a novel approach towards labor market research, making novel data interoperable which has not been considered in previous literature. Our approach and pipeline is generic and could be easily extended to other languages. It also contributes to prestige research by widening the question of ascribed prestige to the question of how information on occupations is collocated and what these contextualization tell us about how occupations are seen.

Data

This dataset contains the metadata related to 3,407,481 tweets (2007-2023). However, according to Twitter/X rules of data protection, we do not publish any data retrieved by their API. This dataset contains a unique id (not the tweet id), a job_id related to KldB (Klassifikation der Berufe), a job_title linked to that tweet, a timestamp and a sentiment. 

Contact

Please contact us if you have questions or want to use this dataset.
DOI: 10.5281/zenodo.10817279
title: German Twitter Data on Occupations

description: Background

There are many valuable insights on jobs and professions in different sectors of society based on their imminent and ascribed characteristics. Studying such characteristics traditionally was done by action research, surveys, questionnaires, etc. which typically take much time and resources to be concluded. This dataset accompanies our studies where we examine vocational education and training data on Twitter. We presented a generic framework to retrieve, process and analyze tweets, and discussed  research questions from computational social science: For example, how can we make Twitter data interoperable to otheravailable resources, e.g. classifications of occupations, tools and skills? Second, do we have enough data to process job collocational prestige analysis on a geographical basis? This presents a novel approach towards labor market research, making novel data interoperable which has not been considered in previous literature. Our approach and pipeline is generic and could be easily extended to other languages. It also contributes to prestige research by widening the question of ascribed prestige to the question of how information on occupations is collocated and what these contextualization tell us about how occupations are seen.

Data

This dataset contains the metadata related to 3,407,481 tweets (2007-2023). However, according to Twitter/X rules of data protection, we do not publish any data retrieved by their API. This dataset contains a unique id (not the tweet id), a job_id related to KldB (Klassifikation der Berufe), a job_title linked to that tweet, a timestamp and a sentiment. 

Contact

Please contact us if you have questions or want to use this dataset.
DOI: 10.25919/f4zt-zx44
title: Parkes observations for project P276 semester 2009APRS_01

description: Timing of multibeam pulsar discoveries
DOI: 10.21227/f1c6-3722
title: GNSS Recordings for Galileo OSNMA Evaluation

description: GNSS recordings on four distinct scenarios to evaluate the Galileo Open Service Navigation Message Authentication (OSNMA) protocol with different receiver optimizations. However, the recordings are generic and can be used for any other GNSS purpose as they contain PVT measurements and data for GPS, Galileo, Glonass, and BeiDou.The dataset contains three dynamic scenarios recorded with a Septentrio mosaic-X5 with firmware version 4.14.0 and one static scenario recorded with a Septentrio PolaRx5TR with firmware version 5.5.0; all in Septentrio Binary Format. The dynamics scenarios were recorded on December 03, 2023, in Brussels, Belgium; and contain a walk in the European District (hard urban environment), a walk in the old town (hard urban environment), and a walk around the Atomium (soft urban environment). Each dynamic scenario is approximately 30 minutes long.  The static scenario is a 60-minute recording on December 20, 2023, from the Septentrio NV offices.
DOI: 10.5281/zenodo.10118897
title: CZ DEMO KPIs in OneNet Project

description: The file contains data on KPIs from CZ demonstration projects (CZ DEMO) . Data reflect number of flexibility providers included in the register detailing increase of flexibility available for flexibility provisions. Evaluation of the KPI (called “Increase of flexibility providing units”) is provided at the first sheet (first KPI – “Number of FSP”). Data for this particular KPI are included in the same excel file at the section “FSP”. Information on conclusion, data format and  explanation are included in the Deliverable 10.5 section (section 2.1.2).
DOI: 10.5281/zenodo.10255453
title: CZ DEMO KPIs in OneNet Project

description: The file contains data on KPIs from CZ demonstration projects (CZ DEMO) . Data reflect number of flexibility providers included in the register detailing increase of flexibility available for flexibility provisions. Evaluation of the KPI (called “Increase of flexibility providing units”) is provided at the first sheet (first KPI – “Number of FSP”). Data for this particular KPI are included in the same excel file at the section “FSP”. Information on conclusion, data format and  explanation are included in the Deliverable 10.5 section (section 2.1.2).
DOI: 10.25919/d0qg-cx12
title: Parkes observations for project P276 semester 2006OCTS_01

description: Timing of multibeam pulsar discoveries
DOI: 10.25919/xeht-s534
title: Parkes observations for project P276 semester 2009OCTS_01

description: Timing of multibeam pulsar discoveries
DOI: 10.25919/b8zh-wd93
title: Parkes observations for project P276 semester 2008APRS_04

description: Timing of multibeam pulsar discoveries
DOI: 10.5281/zenodo.10816678
title: Raw Data and Evaluation Scripts to "Twisted plywood-like tissue formation in vitro. Does curvature do the twist?" Published by Schamberger et al. in PNASnexus 2024
DOI: 10.5281/zenodo.10816679
title: Raw Data and Evaluation Scripts to "Twisted plywood-like tissue formation in vitro. Does curvature do the twist?" Published by Schamberger et al. in PNASnexus 2024
DOI: 10.5281/zenodo.10809875
title: Temperature and Salinity mooring data from Hakefjorden 2015

description: A mooring with 5 Seabird SBE37 CT loggers and 4 SBE56 T loggers was placed in the Hakefjord on the Swedish west coast, postition N57.998, E11.770, at 28 m depth, from  2015-01-26 to 2015-05-28. The instruments were set up to log data with 10 min intervals.

Files:

The RawData.zip file contains rawdata from the instruments. The nominal depths of the instruments are:

SBE37sm-rs232_03706476_2015_05_28.cnv : 27.5 m

SBE05601433_2015-05-28: 25 m

SBE05601434_2015-05-28: 20 m

SBE37sm-rs232_03706947_2015_05_28.cnv: 17 m

SBE05601432_2015-05-28: 14 m

SBE37sm-rs232_03706475_2015_05_28.cnv: 12 m

SBE05601442_2015-05-28: 10 m

SBE37sm-rs232_03706474_2015_05_28.cnv: 8 m

SBE37sm-rs232_03706944_2015_05_28.cnv: 4.5 m

The mooring_hake.mat file is a matlab file that contains processed data in the shape of a struct array DATA with fields:

t: time (Matlab datenum, number of decimal days since January 0, 0000)

T: Temperature (ITS-90, deg. C)

C: Conductivity (S/m)

P: Pressure (db)

S: Practical Salinity (PSU)

Znom: Nominal depth, i.e. approximate depth for constant sea level and vertical mooring line (m)

Funding:

The observations were made possible with funding from the Fyrbodal association of local authorities.
DOI: 10.5281/zenodo.10809876
title: Temperature and Salinity mooring data from Hakefjorden 2015

description: A mooring with 5 Seabird SBE37 CT loggers and 4 SBE56 T loggers was placed in the Hakefjord on the Swedish west coast, postition N57.998, E11.770, at 28 m depth, from  2015-01-26 to 2015-05-28. The instruments were set up to log data with 10 min intervals.

Files:

The RawData.zip file contains rawdata from the instruments. The nominal depths of the instruments are:

SBE37sm-rs232_03706476_2015_05_28.cnv : 27.5 m

SBE05601433_2015-05-28: 25 m

SBE05601434_2015-05-28: 20 m

SBE37sm-rs232_03706947_2015_05_28.cnv: 17 m

SBE05601432_2015-05-28: 14 m

SBE37sm-rs232_03706475_2015_05_28.cnv: 12 m

SBE05601442_2015-05-28: 10 m

SBE37sm-rs232_03706474_2015_05_28.cnv: 8 m

SBE37sm-rs232_03706944_2015_05_28.cnv: 4.5 m

The mooring_hake.mat file is a matlab file that contains processed data in the shape of a struct array DATA with fields:

t: time (Matlab datenum, number of decimal days since January 0, 0000)

T: Temperature (ITS-90, deg. C)

C: Conductivity (S/m)

P: Pressure (db)

S: Practical Salinity (PSU)

Znom: Nominal depth, i.e. approximate depth for constant sea level and vertical mooring line (m)

Funding:

The observations were made possible with funding from the Fyrbodal association of local authorities.
DOI: 10.5281/zenodo.10821816
title: Temperature and Salinity mooring data from Måseskär 2015

description: A mooring with 3 Seabird SBE37 CT loggers and 6 SBE56 T loggers was placed outside Måseskär on the Swedish west coast, postition N58.084 , E11.268, at 48 m depth, from  2015-01-27 to 2015-03-12. The instruments were set up to log data with 10 min intervals.

Files:

The RawData.zip file contains rawdata from the instruments. The nominal depths of the instruments are:

SBE37sm-rs232_03706287_2015_03_12.cnv:  48 m 

SBE05601437_2015-03-12: 44 m

SBE05601440_2015-03-12: 36 m

SBE05601438_2015-03-12: 31 m

SBE05601439_2015-03-12: 26 m

SBE37sm-rs232_03706945_2015_03_12.cnv: 21 m

SBE05601441_2015-03-12: 16 m

SBE37sm-rs232_03706373_2015_03_12.cnv: 11 m

SBE05601436_2015-03-12: 9.5 m

The mooring_maas.mat file is a matlab file that contains processed data in the shape of a struct array DATA with fields:

t: time (Matlab datenum, number of decimal days since January 0, 0000)

T: Temperature (ITS-90, deg. C)

C: Conductivity (S/m)

P: Pressure (db)

S: Practical Salinity (PSU)

Znom: Nominal depth, i.e. approximate depth for constant sea level and vertical mooring line (m)

Z: Approximate actual deph based on pressure sensors (m)

Note that the vertical positions vary quite a lot due to the tilting action of current drag on the mooring.

Funding:

The observations were made possible with funding from the Fyrbodal association of local authorities.
DOI: 10.5281/zenodo.10821815
title: Temperature and Salinity mooring data from Måseskär 2015

description: A mooring with 3 Seabird SBE37 CT loggers and 6 SBE56 T loggers was placed outside Måseskär on the Swedish west coast, postition N58.084 , E11.268, at 48 m depth, from  2015-01-27 to 2015-03-12. The instruments were set up to log data with 10 min intervals.

Files:

The RawData.zip file contains rawdata from the instruments. The nominal depths of the instruments are:

SBE37sm-rs232_03706287_2015_03_12.cnv:  48 m 

SBE05601437_2015-03-12: 44 m

SBE05601440_2015-03-12: 36 m

SBE05601438_2015-03-12: 31 m

SBE05601439_2015-03-12: 26 m

SBE37sm-rs232_03706945_2015_03_12.cnv: 21 m

SBE05601441_2015-03-12: 16 m

SBE37sm-rs232_03706373_2015_03_12.cnv: 11 m

SBE05601436_2015-03-12: 9.5 m

The mooring_maas.mat file is a matlab file that contains processed data in the shape of a struct array DATA with fields:

t: time (Matlab datenum, number of decimal days since January 0, 0000)

T: Temperature (ITS-90, deg. C)

C: Conductivity (S/m)

P: Pressure (db)

S: Practical Salinity (PSU)

Znom: Nominal depth, i.e. approximate depth for constant sea level and vertical mooring line (m)

Z: Approximate actual deph based on pressure sensors (m)

Note that the vertical positions vary quite a lot due to the tilting action of current drag on the mooring.

Funding:

The observations were made possible with funding from the Fyrbodal association of local authorities.
DOI: 10.5281/zenodo.10821745
title: Supplementary material of the article "Nitrogen status estimation of durum wheat derived from Sentinel-2 satellite data in Central Italy"

description: Table S1 Field_data_v1: dataset of winter wheat biophysical  parameters (LAI, LCC and leaf N concentration), measured in the field

Table S2 CI_CCC_v1: Relationship between Chlorophyll indeces and Canopy Chlorophyll Content

Table S3 NA_NC_NNI_v1; results of calculations aimed at monitoring the evolution of the NNI, under different N management and meteorological condition.
DOI: 10.5281/zenodo.7848661
title: Supplementary material of the article "Nitrogen status estimation of durum wheat derived from Sentinel-2 satellite data in Central Italy"

description: Table S1 Field_data_v1: dataset of winter wheat biophysical  parameters (LAI, LCC and leaf N concentration), measured in the field

Table S2 CI_CCC_v1: Relationship between Chlorophyll indeces and Canopy Chlorophyll Content

Table S3 NA_NC_NNI_v1; results of calculations aimed at monitoring the evolution of the NNI, under different N management and meteorological condition.
DOI: 10.5519/qd.9gqz937e
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (43 records)
DOI: 10.5281/zenodo.10635470
title: Data: Chromosomal inversions and the demography of speciation in Drosophila montana and Drosophila flavomontana

description: Chromosome-level genome assemblies of Drosophila montana and Drosophila flavomontana that are associated with the publication "Chromosomal inversions and the demography of speciation in Drosophila montana and Drosophila flavomontana" by Poikela et al. (2024).

Dmontana_chromosomes = only D. montana scaffolds assigned to chromosomes

Dmontana_all_regions = all genomic D. montana regions

Dflavomontana_chromosomes = only D. flavomontana scaffolds assigned to chromosomes

Dflavomontana_all_regions = all genomic D. flavomontana regions
DOI: 10.5281/zenodo.10635471
title: Data: Chromosomal inversions and the demography of speciation in Drosophila montana and Drosophila flavomontana

description: Chromosome-level genome assemblies of Drosophila montana and Drosophila flavomontana that are associated with the publication "Chromosomal inversions and the demography of speciation in Drosophila montana and Drosophila flavomontana" by Poikela et al. (2024).

Dmontana_chromosomes = only D. montana scaffolds assigned to chromosomes

Dmontana_all_regions = all genomic D. montana regions

Dflavomontana_chromosomes = only D. flavomontana scaffolds assigned to chromosomes

Dflavomontana_all_regions = all genomic D. flavomontana regions
DOI: 10.5281/zenodo.10037633
title: Images drone, UAV, Ermitage, Saint-Gilles, Réunion - 20230524 - 02_4

description: "Jeu de données composé d'images de drone DJI Mavic 2 Pro UAV acquises sur le site de Ermitage, Saint-Gilles, Réunion à la date suivante : 20230524.
Les vols ont été réalisés dans le but de créer des modèles numériques d'élévations pour cartographier la rugosité récifale du lagon jusqu'à la pente externe. Les données seront utilisées dans le cadre du projet TELEMAC.

Le dépôt est composé des éléments suivants:
    - 00_: Planche d'aperçu des images
    - DCIM.zip: Images brutes issues du drone
    - GPS.zip: Geopackage contenant l'emprise du survol ainsi que la géolocalisation des images accompagnées de leurs miniatures dans la table d'attribut en base64. RINEX et LLH issus d'un Emlid Reach M2 synchronisé avec la LED de navigation (événement envoyé dans le log du récepteur GNSS lors du déclenchement d'une image). Le but est de pouvoir réaliser un PPK (similaire au RTK en post-traitement) et ainsi disposer d'une position centrimétrique sur chaque image.
    - METADATA.zip: Métadonnées au format ISO19115, Rapports avec miniatures des images de drone (dossier tb) et statistiques de vols.

Arborescence d'origine:
│  └─ 20230524_REU-ermitage_UAV-02_4
│--------  └─ DCIM
│--------  └─ GPS
│----------------  └─ base_2023_05_24_pascal
│----------------  └─ reach_2023_05_24_drone
│------------------------  └─ reachsylvai_raw_202305240249_RINEX_3_03
│------------------------  └─ reachsylvai_raw_202305240330_RINEX_3_03
│----------------  └─ reach_2023_05_24_rover
│--------  └─ METADATA
│----------------  └─ tb

Informations de survol:
- Camera model and parameters:
 Make: Hasselblad
 Model: L1D-20c
 Width: 5472
 Height: 3648
 Focal: 28
 WhiteBalance: Manual
 ExposureMode: Auto Exposure
 ColoSpace: sRGB
 EV: -0.7
 MeteringMode: CenterWeightedAverage
 Camera Pitch: -70.00

- Survey informations:
 No Images: 193
 Median height: 70 meters
 Survey area: 4.16 hectares
 Survey from: 2023:05:24 09:53:20 to: 2023:05:24 10:03:57

D'autres versions plus récentes de ce dépôt sont disponibles. Ces dernières comprennent notamment les données traitées avec OpenDroneMap"
description: This study was funded by the European Regional Development Fund (ERDF) within the programme Interreg V 2014-2020 through the project G2OI
DOI: 10.5281/zenodo.10821824
title: Data for: Upcycling Chips‐Bags for Passive Daytime Cooling

description: The data include all raw data regarding the employed characterization techniques as discussed in the affected publication. These comprise: optical spectroscopy, indoor and field test measurements for passive cooling characterization, RAMAN spectroscopy and scanning electron microscopy
DOI: 10.5281/zenodo.10821823
title: Data for: Upcycling Chips‐Bags for Passive Daytime Cooling

description: The data include all raw data regarding the employed characterization techniques as discussed in the affected publication. These comprise: optical spectroscopy, indoor and field test measurements for passive cooling characterization, RAMAN spectroscopy and scanning electron microscopy
DOI: 10.5281/zenodo.10823098
title: CNF Encoded Hard Miters from IWLS'22 Paper

description: Five Hard Miters from an IWLS'22 Paper Encoded into CNF in two Ways

The IWLS'22 paper [1] by He-Teng Zhang, Jie-Hong R. Jiang, Alan Mishchenko,and Luca Amaru, which can be considered an update on their DAC'21 [2] paperwas focusing on a hybrid approach to SAT-sweeping, i.e., using a SAT solverincrementally, taking the circuit structure into account.

Experiments in [1] used a subset of the benchmarks from [2] which alsocontains five miters `n01`, `n04`, `n06`, and `test01` and `test02` providedto us by Alan Mishchenko and which were considered hard for SAT sweeping,and particularly for monolithic CNF-level SAT solving.

  [1] H.-T. Zhang, J.-H. R. Jiang, A. Mishchenko, and L. Amaru.  "Improved  large-scale SAT sweeping", In Proc. Intl. Work. on Logic Synthesis  (IWLS'22).

  [2] H.-T. Zhang, J.-H. R. Jiang, L. Amaru, A. Mishchenko, and R. Brayton.  "Deep integration of circuit simulator and SAT solver". In Proc. of Design  Automation Conference (DAC'21), 2021.

It turned out, confirmed by Alan Mishchenko, that the outputs of `test01`and `test02`, were flipped in the process they were generated.  This doesnot invalidate the SAT sweeping experiments in [1] and [2] at all, but needsto be taken care off, when encoding them into CNF with `aigtocnf`, by simplyfirst negating the outputs with `aigflip`.  Further note, that the otherthree AIGs `n01`, `n04`, and `n06` are not negated but have multiple outputsand thus we joined them by disjunction with `aigor`.

We have extended `aigtocnf` to detect XOR and ITE gates in AIGER circuits,i.e., during Tseitin encoding we check whether and an AND gate has twonegated AND gates as children and actually implements an XOR or ITE gate.In this case we use a direct CNF encoding of four clauses, instead of 9clauses for three AND gates, skipping the two child AND gates of the top ANDgate.  This reduces not only the number of clauses and variables but haspositive effects on solving time.  Thus all the CNF benchmarks come in twoflavors tagged `xits` with special treatment of XOR and ITE gates duringTseitin encoding and `ands` without.
DOI: 10.5281/zenodo.10408334
title: Wide-field cortical calcium imaging data from behaving mice

description: This data release contains simultaneous recordings of fluorescentsignals of the mouse cortex by wide-field calcium imaging and behavioralactivity from 5 animals.These data were used in

Ajioka T, Nakai N, Yamashita O, Takumi T (2024) End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging. PLoS Comput Biol 20(3): e1011074. https://doi.org/10.1371/journal.pcbi.1011074

https://doi.org/10.1371/journal.pcbi.1011074

 

The code to make the figures in the paper is available athttps://github.com/atakehiro/Neural_Decoding_from_Calcium_Imaging_DataIf you use these data in a paper, please cite the original researchpaper, as well as this dataset using the Zenodo doi.
DOI: 10.5281/zenodo.10408333
title: Wide-field cortical calcium imaging data from behaving mice

description: This data release contains simultaneous recordings of fluorescentsignals of the mouse cortex by wide-field calcium imaging and behavioralactivity from 5 animals.These data were used in

Ajioka T, Nakai N, Yamashita O, Takumi T (2024) End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging. PLoS Comput Biol 20(3): e1011074. https://doi.org/10.1371/journal.pcbi.1011074

https://doi.org/10.1371/journal.pcbi.1011074

 

The code to make the figures in the paper is available athttps://github.com/atakehiro/Neural_Decoding_from_Calcium_Imaging_DataIf you use these data in a paper, please cite the original researchpaper, as well as this dataset using the Zenodo doi.