cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25625/r64r8h/ifo2n8
title: Wissenschaft-Technik-Verwaltung Personal Übersicht_neu_JS11_4_2023.tab

description: :unav
DOI: 10.25625/r64r8h/zymieb
title: personal_in_der_mpg-angepasst.svg

description: :unav
DOI: 10.25625/r64r8h
title: Wissenschaftler*innen Techniker*innen und Verwaltungspersonal auf Planstellen

description: Wissenschaftler*innen Techniker*innen und Verwaltungspersonal auf Planstellen
DOI: 10.25625/x9lxh3/v4zdg1
title: 670_BioDB-Dossier_Revision__Neuberufene.tab
DOI: 10.25625/3lqprm/xftwx2
title: gmpg.mplstyle

description: :unav
DOI: 10.25625/x9lxh3/ktpnaw
title: common_plot_tools.py
DOI: 10.25625/3lqprm/2wc0at
title: script.py

description: :unav
DOI: 10.25625/3lqprm/hiytqu
title: script.ipynb

description: :unav
DOI: 10.25625/x9lxh3/48dlap
title: gmpg.mplstyle
DOI: 10.25625/x9lxh3/tcm0si
title: BioDB-Dossier_3_GSHS.svg
DOI: 10.25625/3lqprm/e0qdsp
title: requirements.txt

description: :unav
DOI: 10.25625/x9lxh3/tvlfnr
title: BioDB-Dossier_3_BMS.svg
DOI: 10.25625/3lqprm/qnbjwk
title: stipendiaten_innen_und_wissenschaftler_innen.svg

description: :unav
DOI: 10.25625/x9lxh3/5lrfnd
title: BioDB-Dossier_1.svg
DOI: 10.25625/x9lxh3/fz3aif
title: BioDB-Dossier_2.svg
DOI: 10.25625/3lqprm/exfiox
title: SOZSTAT_ZP_Personal_Allgemein_2.tab

description: :unav
DOI: 10.25625/3lqprm/nfknwh
title: common_plot_tools.py

description: :unav
DOI: 10.25625/x9lxh3/7pngme
title: BioDB-Dossier_3_CPTS.svg
DOI: 10.25625/3lqprm/hotkl6
title: Beschäftigung_MPG_gesamt_neu08.05.18.xlsx

description: :unav
DOI: 10.25625/x9lxh3/bvtaun
title: 11-16_Neuberufene_revision.ipynb
DOI: 10.25625/3lqprm/cosxic
title: stipendiaten_innen_und_wissenschaftler_innen.pdf

description: :unav
DOI: 10.25625/x9lxh3/ffouon
title: 11-16_Neuberufene_revision.py
DOI: 10.25625/x9lxh3/v8ouzq
title: 11-16_Neuberufene.pdf
DOI: 10.25625/3lqprm/5dofui
title: stipendiaten_m_w.pdf

description: :unav
DOI: 10.25625/3lqprm/tkyvzi
title: stipendiaten_m_w.svg

description: :unav
DOI: 10.25625/x9lxh3/lxufbw
title: requirements.txt
DOI: 10.25625/3lqprm/rk3hae
title: stipendiaten_m_w.png

description: :unav
DOI: 10.25625/dl3r72/5i8t5o
title: common_plot_tools.py

description: :unav
DOI: 10.25625/dl3r72/gsja7h
title: fetchCSVfromDatabase.py

description: :unav
DOI: 10.25625/x9lxh3
title: BioDB Dossier - Abschnitt Neuberufene

description: Berufungen als WM in den Sektionen der MPG in 5-Jahresschritten
DOI: 10.25625/x9lxh3/qfdqup
title: fetchCSVfromDatabase.py
DOI: 10.25345/c5v11vx0f
title: MassIVE MSV000093477 - GNPS_Aviwe Alternaria alternata P02PL2_2
title: Untargeted metabolomics of Alternaria alternata P02PL2, Matandela Aviwe MSc
DOI: 10.25625/3lqprm
title: Stipendiat:innen und Wissenschaftliche Angestellte der MPG 1955–2000

description: Stipendiat:innen und Wissenschaftliche Angestellte der MPG 1955–2000
DOI: 10.25625/dl3r72/8wqqqw
title: institutsleitungen.ipynb

description: :unav
DOI: 10.25625/dl3r72/9b8ymw
title: institutsleitungen.png

description: :unav
DOI: 10.25625/dl3r72/tzgitm
title: institutsleitungen.tab

description: :unav
DOI: 10.25625/dl3r72/qh0yxr
title: gmpg.mplstyle

description: :unav
DOI: 10.25625/dl3r72/z5p1pd
title: institutsleitungen.pdf

description: :unav
DOI: 10.25625/dl3r72/86yqpo
title: requirements.txt

description: :unav
DOI: 10.25625/dl3r72
title: Entwicklung Institutsleitungen

description: Entwicklung Institutsleitungen Übersicht, AMPG, 2. Abt., Rep. 1, Nr. 234, Mappe 2, fol. 368.
DOI: 10.25625/dl3r72/1fuetq
title: institutsleitungen.svg

description: :unav
DOI: 10.25625/z7xdbl/01ns8i
title: part_2000-2005_pubs.json

description: :unav
DOI: 10.25625/z7xdbl/9y0od4
title: requirements.txt

description: :unav
DOI: 10.25625/2z9xs3/ibg4ja
title: finanzielle_Antragshoehe.png

description: :unav
DOI: 10.25625/z7xdbl/8g42iu
title: collaboration_net.R

description: :unav
DOI: 10.25625/2z9xs3/7igbda
title: Sektionsverhältnis_AW.jpeg

description: :unav
DOI: 10.25625/2z9xs3/wlilmq
title: Anschaffungswert_all_1.jpeg

description: :unav
DOI: 10.25625/z7xdbl/plghh8
title: part_1945-1985_pubs.json

description: :unav
DOI: 10.25625/z7xdbl/uoxh19
title: part_2000-2005_authors.json

description: :unav
DOI: 10.25625/2z9xs3/degjiu
title: Antragshäufigkeit.png

description: :unav
DOI: 10.25625/z7xdbl/m3rasw
title: Abbildung_1.svg

description: :unav
DOI: 10.25625/2z9xs3/refxzw
title: Institutsanschaffungsgeschichten_MPI.jpeg

description: :unav
DOI: 10.25625/2z9xs3/zgg4pd
title: Antragshäufigkeit_all_2.jpeg

description: :unav
DOI: 10.25625/z7xdbl/j9uaaf
title: 2005.graphml

description: :unav
DOI: 10.25625/z7xdbl/m57hai
title: 01_Kooperationen_Datenabfrage.ipynb

description: :unav
DOI: 10.25625/2z9xs3/530qjc
title: Antragshäufigkeit_MPI.jpeg

description: :unav
DOI: 10.25625/z7xdbl/trlgbm
title: grid2sektion_mappedFuzzy.csv

description: :unav
DOI: 10.25625/2z9xs3/hschgm
title: Antragshäufigkeit_alle_absteigend.svg

description: :unav
DOI: 10.25625/z7xdbl/ujc4va
title: Abb-6_7_FOR-Code_Netzwerke.gephi

description: :unav
DOI: 10.25625/2z9xs3/hkethh
title: Antragshäufigkeit_alle_absteigend_prozent.png

description: :unav
DOI: 10.25625/2z9xs3/sp0ih6
title: finanzielle_Antragshoehe.svg

description: :unav
DOI: 10.25625/z7xdbl/gywjfj
title: Abbildung_3.svg

description: :unav
DOI: 10.25625/z7xdbl/lzqbqg
title: part_1986-1999_pubs.json

description: :unav
DOI: 10.25625/2z9xs3/b5egm8
title: Forschungsgruppen_AN.jpeg

description: :unav
DOI: 10.25625/z7xdbl/42akqp
title: part_1986-1999_authors.json

description: :unav
DOI: 10.25625/2z9xs3/dvodrz
title: BAR_gmpg_style.ipynb

description: :unav
DOI: 10.25625/z7xdbl/g1ex9u
title: grid2sektion_2.csv

description: :unav
DOI: 10.25625/z7xdbl/seq2pg
title: 1990.graphml

description: :unav
DOI: 10.25625/2z9xs3/yxo8em
title: Antragshäufigkeit_all_1.jpeg

description: :unav
DOI: 10.25625/z7xdbl/euljlg
title: part_1945-1985_authors.json

description: :unav
DOI: 10.25625/2z9xs3/lf4lca
title: Antragshäufigkeit_alle_absteigend_prozent_afs.svg

description: :unav
DOI: 10.25625/bmqauj/vbu3hd
title: requirements.txt

description: :unav
DOI: 10.25625/z7xdbl/ka6gt3
title: mpi_cluster_long_grouped.tab

description: :unav
DOI: 10.25625/2z9xs3/hm4beh
title: Antragshäufigkeit_alle_absteigend_afs.svg

description: :unav
DOI: 10.25625/2z9xs3/rt8t6n
title: requirements.txt

description: :unav
DOI: 10.25625/bmqauj/is3lyv
title: Tabelle Patente MPG gesamt.tab

description: :unav
DOI: 10.25625/bmqauj/yjyj7z
title: patente_mpg.pdf

description: :unav
DOI: 10.25625/z7xdbl/efjox7
title: Abbildung_2.svg

description: :unav
DOI: 10.25625/z7xdbl/jy6zgx
title: 02_Kooperationen_Abbildungen_1_2_3.ipynb

description: :unav
DOI: 10.25625/2z9xs3/lw1upg
title: andere_Einheiten_AN.jpeg

description: :unav
DOI: 10.25625/bmqauj/ghukdc
title: gmpg.mplstyle

description: :unav
DOI: 10.25625/z7xdbl
title: Kooperation und Interdisziplinarität - Die MPG aus Sicht von wissenschaftlichen Veröffentlichungen

description: Dieser Datensatz enthält alle Skripte und Grunddaten für die Reproduktion der Abbildungen.
DOI: 10.25625/2z9xs3/xggzpn
title: andere_Einheiten_AW.jpeg

description: :unav
DOI: 10.25625/bmqauj/hskomt
title: patente.ipynb

description: :unav
DOI: 10.25625/2z9xs3/ebn4ww
title: Antragshäufigkeit.svg

description: :unav
DOI: 10.25625/2z9xs3/f1t3en
title: Antragshäufigkeit_alle_absteigend_prozent.svg

description: :unav
DOI: 10.25625/bmqauj/byouzh
title: patente_mpg.png

description: :unav
DOI: 10.25625/bmqauj/kflhht
title: Tabelle Patente Zeitskala Materialforschung.tab

description: :unav
DOI: 10.25625/2z9xs3/spc7ve
title: common_plot_tools.py

description: :unav
DOI: 10.25625/bmqauj/fvsf3r
title: patente_material.png

description: :unav
DOI: 10.25625/2z9xs3/eadr8x
title: 2021-12-29_BAR.xlsx

description: :unav
DOI: 10.25625/2z9xs3/ffx609
title: gmpg.mplstyle

description: :unav
DOI: 10.25625/bmqauj/iehbsk
title: patente_material.pdf

description: :unav
DOI: 10.25625/bmqauj/rff2gy
title: patente_material.svg

description: :unav
DOI: 10.25625/2z9xs3/3he8xe
title: Anschaffungswert_all_3.jpeg

description: :unav
DOI: 10.25625/bmqauj/ylf3j0
title: common_plot_tools.py

description: :unav
DOI: 10.25625/2z9xs3/jbygyx
title: Anschaffungswert_all_2.jpeg

description: :unav
DOI: 10.25625/2z9xs3/zf2f9h
title: Kooperation_MPIe_AW.jpeg

description: :unav
DOI: 10.25625/bmqauj/tijy3n
title: patente_mpg.svg

description: :unav
DOI: 10.25625/2z9xs3/5hfssm
title: Kooperation_Staedte_AW.jpeg

description: :unav
DOI: 10.25625/bmqauj/uuwxfb
title: fetchCSVfromDatabase.py

description: :unav
DOI: 10.25625/2z9xs3/ezshg5
title: Antragshäufigkeit_alle_absteigend_afs.png

description: :unav
DOI: 10.25625/bmqauj
title: Entwicklung der von der MPG genommenen Patente

description: Entwicklung der von der MPG genommenen Patente
DOI: 10.25625/2z9xs3/f7uexa
title: 2021-12-29_BAR.csv

description: :unav
DOI: 10.25625/2z9xs3/37beqo
title: grid_sektion.csv

description: :unav
DOI: 10.25625/2z9xs3/7jp8dc
title: Antragshäufigkeit_alle_absteigend_prozent_afs.png

description: :unav
DOI: 10.25625/2z9xs3/grm17l
title: fetchCSVfromDatabase.py

description: :unav
DOI: 10.25625/2z9xs3/9bbeid
title: Forschungsgruppen_AW.jpeg

description: :unav
DOI: 10.25625/2z9xs3/puy4hq
title: Antragshäufigkeit_alle_absteigend.png

description: :unav
DOI: 10.25625/2z9xs3/0yqha3
title: Sektionsverhältnis_AN.jpeg

description: :unav
DOI: 10.25625/2z9xs3
title: Auswertungen der in den BAR-Protokollen enthaltenen Informationen

description: Komparative Auswertung der beantragenden MPI im BAR, Häufigkeiten, Antragssummen etc.
DOI: 10.25625/zzmb1p/ysw5s5
title: WM_Phasen.tab

description: :unav
DOI: 10.25625/zzmb1p/7h5lz5
title: nobelpreise_aktive_wm-awm_colored.tab

description: :unav
DOI: 10.25625/zzmb1p/jhppo6
title: common_plot_tools.py

description: :unav
DOI: 10.25625/zzmb1p/7gewy2
title: nobelpreise_mitgliedschaftszeiten.R

description: :unav
DOI: 10.25625/zzmb1p/roe34y
title: Nobelpreise.svg

description: :unav
DOI: 10.25625/zzmb1p/ms3pet
title: nobelpreise.csv

description: :unav
DOI: 10.25625/zzmb1p/yf6qru
title: Nobelpreise_wm.xlsx

description: :unav
DOI: 10.25625/59ymmf/ar6lp2
title: result.tab
DOI: 10.25625/zzmb1p/1dsepv
title: 672_Personen_-_Nobelpreisträger_innen_der_WM_der_MPG_&_KWG_(mit_Institutszugehörigkeit).tab

description: :unav
DOI: 10.25625/59ymmf/4jrkz2
title: notebook.ipynb
DOI: 10.25625/zzmb1p/yhlvw2
title: Nobelpreise.pdf

description: :unav
DOI: 10.25625/59ymmf/laffq9
title: 659_MPG_Mitgliedschaften_mit_Geburtsdatum.tab
DOI: 10.25625/59ymmf/pozjxf
title: notebook.py
DOI: 10.25625/zzmb1p/9pbxss
title: Nobelpreise_awm.xlsx

description: :unav
DOI: 10.25625/zzmb1p/qb1unz
title: Nobel.ipynb

description: :unav
DOI: 10.25625/59ymmf/f1kkr6
title: fetchCSVfromDatabase.py
DOI: 10.25625/zzmb1p/zpfluw
title: fetchCSVfromDatabase.py

description: :unav
DOI: 10.25625/59ymmf
title: Akademisches Alter der WM

description: Akademisches Alter der WM
DOI: 10.25625/zzmb1p/xilxmx
title: 600_Auszeichnungen_der_(auswärtigen)_wissenschaftlichen_Mitglieder_(1948_-_2005).tab

description: :unav
DOI: 10.25625/zzmb1p/dtnbtj
title: Nobelpreisträger_innen.svg

description: :unav
DOI: 10.25625/zzmb1p/uhlctf
title: AWM_Phasen.tab

description: :unav
DOI: 10.25625/zzmb1p/bbzo1y
title: nobelpreise_nicht_aktive_wm-awm.xlsx

description: :unav
DOI: 10.25625/zzmb1p/hkfimt
title: nobelpreise_mitglieschaftszeit.pdf

description: :unav
DOI: 10.25625/zzmb1p/uoexoc
title: Nobelpreisträger_innen.pdf

description: :unav
DOI: 10.25625/zzmb1p/1nfj58
title: Nobel_institutszugehörigkeit.ipynb

description: :unav
DOI: 10.25625/zzmb1p/7ox1a8
title: requirements.txt

description: :unav
DOI: 10.25625/zzmb1p/tkr496
title: nobelpreise_aktive_wm-awm.xlsx

description: :unav
DOI: 10.25625/zzmb1p/f3vrtb
title: gmpg.mplstyle

description: :unav
DOI: 10.25625/zzmb1p/xnevum
title: nobelpreise_nicht_aktive_wm-awm_colored.tab

description: :unav
DOI: 10.25625/zzmb1p
title: Übersicht zu Nobelpreisträger:innen der MPG

description: Übersicht zu Nobelpreisträger:innen der MPG
DOI: 10.25625/zj5qfv/igagif
title: 2_RunAnalysis.ipynb

description: :unav
DOI: 10.25625/zj5qfv/4rslhf
title: 1_QueryDatasets.ipynb

description: :unav
DOI: 10.25625/zj5qfv/njnznx
title: Kapitel_IV_MPG_Abb_4.svg

description: :unav
DOI: 10.25625/zj5qfv/sgo9of
title: Kapitel_IV_MPG_Abb_4.png

description: :unav
DOI: 10.25625/zj5qfv
title: Entwicklung dominierender Wissenschaften in der MPG im Spiegel ihrer Publikationstätigkeit zwischen 1965 und 2004

description: Dargestellt ist die Anzahl von Publikationen in einem Bereich pro Jahr relativ zur Anzahl aller Publikationen weltweit. Ausgewählt sind die der Publikationstätigkeit nach zehn größten Wissenschaftsbereiche in der MPG.
DOI: 10.5281/zenodo.7551552
title: IDMT-Traffic Dataset

description: The IDMT-TRAFFIC dataset includes 17,506 2-second long stereo audio excerpts of recorded vehicle passings as well as different background sounds alongside streets. The dataset includes recordings from 4 different recording locations, 4 different vehicle types (bus, car, motorcycle, and truck), three different tempo limit areas, as well as dry and wet weather/road conditions. The direction of movement is annotated as well. Recordings with both high-quality sE8 microphones as well as medium-quality MEMS microphones are included.
DOI: 10.5281/zenodo.7551553
title: IDMT-Traffic Dataset

description: The IDMT-TRAFFIC dataset includes 17,506 2-second long stereo audio excerpts of recorded vehicle passings as well as different background sounds alongside streets. The dataset includes recordings from 4 different recording locations, 4 different vehicle types (bus, car, motorcycle, and truck), three different tempo limit areas, as well as dry and wet weather/road conditions. The direction of movement is annotated as well. Recordings with both high-quality sE8 microphones as well as medium-quality MEMS microphones are included.
DOI: 10.5281/zenodo.7551583
title: IDMT-FL Dataset

description: The IDMT-DESED-FL and IDMT-URBAN-FL datasets enable research in sound event detection (SED) within a federated learning (FL) context. IDMT-DESED-FL and IDMT-URBAN-FL consist of sound events sourced from well-known DESED and URBAN-8K datasets.  Each source dataset contains sound events from ten classes for the use cases of SED in domestic and urban environments, respectively. To simulate an FL scenario, the source events are mixed with background noise to generate 30.000 ten-second soundscapes which are partitioned to 100 edge devices. Each soundscape is generated by mixing up to five sound events (possibly overlapping) with background noise. Both datasets contain independent and identically distributed (IID) and non-IID versions, to provide a more real-world like distribution of event classes. 
 



	
IDMT-DESED-FL sound event classes include alarm/bell/ringing, blender, cat, dog, dishes, electric shaver/toothbrush, frying, running water, speech, and vacuum cleaner. The background classes include apartment room, computer interior, computer lab, emergency staircase, and library.
	 




	
IDMT-URBAN-FL sound event classes include air conditioner, car horn, children playing, dog bark, drilling, engine idling, gun shot, jackhammer, siren, and street music. Background classes for IDMT-URBAN-FL are sourced from the Isolated Urban Sound Database (IUSB), and include birds, crowd, fountain, rain, and traffic.



Due to the size of the datasets, with this download we provide the scripts and details necessary to generate the FL datasets using the source material from DESED, URBAN-8K, and IUSB.


See the above referenced paper and README contained with the data folder for further details.
DOI: 10.5281/zenodo.7551584
title: IDMT-FL Dataset

description: The IDMT-DESED-FL and IDMT-URBAN-FL datasets enable research in sound event detection (SED) within a federated learning (FL) context. IDMT-DESED-FL and IDMT-URBAN-FL consist of sound events sourced from well-known DESED and URBAN-8K datasets.  Each source dataset contains sound events from ten classes for the use cases of SED in domestic and urban environments, respectively. To simulate an FL scenario, the source events are mixed with background noise to generate 30.000 ten-second soundscapes which are partitioned to 100 edge devices. Each soundscape is generated by mixing up to five sound events (possibly overlapping) with background noise. Both datasets contain independent and identically distributed (IID) and non-IID versions, to provide a more real-world like distribution of event classes. 
 



	
IDMT-DESED-FL sound event classes include alarm/bell/ringing, blender, cat, dog, dishes, electric shaver/toothbrush, frying, running water, speech, and vacuum cleaner. The background classes include apartment room, computer interior, computer lab, emergency staircase, and library.
	 




	
IDMT-URBAN-FL sound event classes include air conditioner, car horn, children playing, dog bark, drilling, engine idling, gun shot, jackhammer, siren, and street music. Background classes for IDMT-URBAN-FL are sourced from the Isolated Urban Sound Database (IUSB), and include birds, crowd, fountain, rain, and traffic.



Due to the size of the datasets, with this download we provide the scripts and details necessary to generate the FL datasets using the source material from DESED, URBAN-8K, and IUSB.


See the above referenced paper and README contained with the data folder for further details.
DOI: 10.5281/zenodo.7551260
title: IDMT-ISA-Electric-Engine Dataset

description: The IDMT-ISA-ELECTRIC-ENGINE dataset contains sound files of three similar units of an electrical engine (2ACT Motor Brushless DC 42BLF01, 4000 RPM, 24VDC), which simulate different acoustic conditions. The operational states “good”, “heavy load” and “broken” were provoked by a change of supply voltage and loading weight leading to a change of the operating sound. In March 2017 the IDMT_ISA_ELECTRIC_ENGINE dataset was recorded at Fraunhofer Institute for Digital Media Technology (IDMT). In each file, only one of the engines is active at the same time assuming an engine can only have one of the three operational states. The dataset consists of recordings of the electric engine plus the following background noise types:



	
File duration:  42.32 minutes
	
# Operational State “good”: 774
	
# Operational State “broken”: 789
	
# Operational State “heavyload”: 815
	
# Total WAV Files: 2378
	
Sampling rate: 44.1KHz
	
Resolution: 32-bit
	
Mono audio
DOI: 10.5281/zenodo.7551261
title: IDMT-ISA-Electric-Engine Dataset

description: The IDMT-ISA-ELECTRIC-ENGINE dataset contains sound files of three similar units of an electrical engine (2ACT Motor Brushless DC 42BLF01, 4000 RPM, 24VDC), which simulate different acoustic conditions. The operational states “good”, “heavy load” and “broken” were provoked by a change of supply voltage and loading weight leading to a change of the operating sound. In March 2017 the IDMT_ISA_ELECTRIC_ENGINE dataset was recorded at Fraunhofer Institute for Digital Media Technology (IDMT). In each file, only one of the engines is active at the same time assuming an engine can only have one of the three operational states. The dataset consists of recordings of the electric engine plus the following background noise types:



	
File duration:  42.32 minutes
	
# Operational State “good”: 774
	
# Operational State “broken”: 789
	
# Operational State “heavyload”: 815
	
# Total WAV Files: 2378
	
Sampling rate: 44.1KHz
	
Resolution: 32-bit
	
Mono audio
DOI: 10.5281/zenodo.7551339
title: IDMT-ISA-Tubes Dataset

description: The IDMT_ISA_TUBES dataset contains sound files of ten different bulk materials running down a plastic slide in a tube. These bulk materials consist of five types of sweets and five kinds of nuts and screws. Two tubes, identical in construction, were used for both, collecting data and also testing the robustness of the recognition algorithms. Each bulk material has been poured 50 to 60 times in each tube. Due to the small amount of examples per class for each tube no separate training and test sets are provided and cross-validation is recommended. The distribution that was used for each crossvalidation step is provided with the dataset.



	
File duration: 55.25 minutes
	
# Files for coffee_beans: 110
	
# Files for m4_nuts: 98
	
# Files for m6_nuts: 109
	
# Files for mentos_full_fruit: 106
	
# Files for mentos_white: 112
	
# Files for nails: 113
	
# Files for pulmoll: 111
	
# Files for screws: 106
	
# Files for tictac: 111
	
# Files for washers: 100
	
# Total WAV Files: 1076
	
Sampling rate: 44.1KHz
	
Resolution: 32-bit
	
Mono audio
DOI: 10.5281/zenodo.7551340
title: IDMT-ISA-Tubes Dataset

description: The IDMT_ISA_TUBES dataset contains sound files of ten different bulk materials running down a plastic slide in a tube. These bulk materials consist of five types of sweets and five kinds of nuts and screws. Two tubes, identical in construction, were used for both, collecting data and also testing the robustness of the recognition algorithms. Each bulk material has been poured 50 to 60 times in each tube. Due to the small amount of examples per class for each tube no separate training and test sets are provided and cross-validation is recommended. The distribution that was used for each crossvalidation step is provided with the dataset.



	
File duration: 55.25 minutes
	
# Files for coffee_beans: 110
	
# Files for m4_nuts: 98
	
# Files for m6_nuts: 109
	
# Files for mentos_full_fruit: 106
	
# Files for mentos_white: 112
	
# Files for nails: 113
	
# Files for pulmoll: 111
	
# Files for screws: 106
	
# Files for tictac: 111
	
# Files for washers: 100
	
# Total WAV Files: 1076
	
Sampling rate: 44.1KHz
	
Resolution: 32-bit
	
Mono audio
DOI: 10.34683/sr568
title: StoneRounds Case: SR568
title: AR Retinitis Pigmentosa

description: StoneRounds Case SR568 was diagnosed with AR Retinitis Pigmentosa likely caused by mutations in the gene: CDHR1.
DOI: 10.5281/zenodo.7551321
title: IDMT-ISA-Metal-Balls Dataset

description: The IDMT_ISA_METAL_BALLS dataset contains sound files of three differently coated types of metal balls running down a metal slide (K1, K2, K3). The different surfaces cause a hearable difference of the rolling sounds, which we consider as indicator regarding the condition of the coating. The dataset was created using a low-cost microphone as sensor. Industrial background noise was played via external loudspeakers during the recording process for simulating a realistic acoustic environment.


We provide both an unbalanced split, which resulted from the original recording process, with a total size of 2267 recordings as well as a balanced split for reproducibility of experiments. Two additional test sets with more than 250 recordings each are provided where general settings like the angle of the slide and the position of microphone have been altered for testing the robustness of the system to changes in the setup.



	
File duration:  18.87 minutes
	
# K1: 987
	
# K2: 1198
	
# K3: 647
	
# Total WAV Files: 2832
	
Sampling rate: 44.1KHz
	
Resolution: 32-bit
	
Mono audio
DOI: 10.5281/zenodo.7551322
title: IDMT-ISA-Metal-Balls Dataset

description: The IDMT_ISA_METAL_BALLS dataset contains sound files of three differently coated types of metal balls running down a metal slide (K1, K2, K3). The different surfaces cause a hearable difference of the rolling sounds, which we consider as indicator regarding the condition of the coating. The dataset was created using a low-cost microphone as sensor. Industrial background noise was played via external loudspeakers during the recording process for simulating a realistic acoustic environment.


We provide both an unbalanced split, which resulted from the original recording process, with a total size of 2267 recordings as well as a balanced split for reproducibility of experiments. Two additional test sets with more than 250 recordings each are provided where general settings like the angle of the slide and the position of microphone have been altered for testing the robustness of the system to changes in the setup.



	
File duration:  18.87 minutes
	
# K1: 987
	
# K2: 1198
	
# K3: 647
	
# Total WAV Files: 2832
	
Sampling rate: 44.1KHz
	
Resolution: 32-bit
	
Mono audio