README.md
In pole-national-donnees-biodiversite/OB1.metadata: Ocean hackathon défi B1
Défi OB1
Elie Arnaud, Yvan Le Bras, Oussama Chaib, Juliette Silhol, Laura Leroi, Clément Grandidier, Dorian Cazau
Git de l'équipe OB1 de l'Ocean Hackathon 2019 - Brest.
Description du défi
De plus en plus de données scientifiques sont aujourd'hui accessibles en accès ouvert. Cette mise à disposition est un premier pas essentiel vers un meilleur partage des connaissances et une réutilisation massives des données. Malheureusement, le partage du "matériel brut" ne suffit pas, et aujourd'hui plus que jamais se pose la question de la structuration de ces données. Comment faire en sorte que les données soient suffisamment bien décrites pour pouvoir être compréhensibles et réutilisées le plus largement possible ? Dans le cadre du projet "Pôle national de données de biodiversité", des actions en cours propose l'utilisation d'un standard de metadonnees, l'EML (Ecological Metadata Language), comme standard pivot d'outils et services qui faciliteraient cette structuration des données écologique et leur réutilisation. Des questionnements particuliers ont émergé concernant les données marines et ocean hackathon représente une initiative parfaite pour avancer sur certains aspects et laisser de potentiels hackers proposer d'améliorer ou/et créer des produits d'intérêt.
Objectifs
En termes d'outils, nous travaillons sur le développement d'une application interactive R Shiny nommée MetaSHARK. Nous aimerions développer des “modules” en R ou autre langage permettant d’automatiser les traitements de données marines hétérogènes pour par exemple parcourir des données d'écologie (séquences d'ADN ou données de présence d'espèces) et environnementales (données satellitaires ou autres données décrivant les conditions physico-chimiques du milieu) pour remplir de manière automatique et/ou assistée les champs de métadonnées pouvant l’être (étendues taxonomique, géographique et temporelle de l’étude ...).
Pistes envisagées
- Blockchain & Peer to Peer. Pour distribuer
- A1 les données et/ou métadonnées (MTD)
- A2 logiciels notamment ceux développés en B1 et B2
- Deux modules R Shiny
- B0 Upload de fichiers
- B1 "Inférence" automatique de MTD, notamment basé sur utilisation d'expression régulière (RegEx)
- B2 "Sémantique" sur attributs (aussi nommés variables) : faire un lien entre les attributs et des ressoruces terminologiques exitantes notamment ontologies du domaine
- Récupérer des données hétérogènes proposées au Océan hackathon + extérieures avec leurs MTD et proposer
- C1 d'y ajouter des MTD -> structuration / qualification des MTD
- C2 Faire remonter aux entrepôts des données d'origine les améliorations de MTD
- "Mapping" manuel de standards entre
- D1 ISO19139 / EML / Darwin-Core / Tethys
- Tethys
- Business Model
- Indicateurs / métriques autour de la "complétude" des données en MTD
- Indicateurs / métriques autour des données stockées inutilement car non rétuilisable notamment car MTD pas assez détaillées
Gestion métadonnées
- datapackage https://github.com/frictionlessdata/datapackage-r notamment fonction dataPackage$infer
datapackage=datapackage.r::infer("OBSMAM1.csv",getwd())
ne semble que fonctionner avec des fichiers .csv
créer une liste composée de sous-listes décrivant les métadonnées du fichier : path, profile, encoding, name, format, mediatype et schema. Dans la sous-liste "schema" sont distingués les "fields" du dataset (nom des headers), auxquels sont attribués un "name",un "type" et "format", et les missing values.
- metadatar https://github.com/annakrystalli/metadatar
- Apparently, not so "intelligent" than datapackage::infer as it seems there is no inference of min/max, format, attibute type...
- taxonomyCleanr (A workflow and set of functions to clean your taxonomy data using R) https://github.com/EDIorg/taxonomyCleanr
- dataspice https://github.com/ropenscilabs/dataspice dont déjà des choses côté EML : https://github.com/ropenscilabs/dataspice/blob/master/R/eml_to_spice.R
- dataCleanR (A collection of user friendly data cleaning functions - EDIorg/dataCleanr) https://github.com/EDIorg/dataCleanr/blob/master/README.md
- time format management: iso8601_convert converts date and time strings to standard ISO 8601 formatted strings, https://ediorg.github.io/dataCleanr/articles/iso8601_functions.html
Contaminants2 =read.table("Contaminants2.csv", header=TRUE, quote="", sep =";")
dateContaminants2 = Contaminants2$Passage...Date #extration des données "date"
dataContaminants2_cvn= iso8601_convert(dateContaminants2, orders = 'mdy')
ici orders='mdy' car les données "date" sont de format jj/mm/aa
mettre orders='mdy HM' si l'heure est rensignée en plus.
- taxize " The EML R package can leverage existing functions in the R package taxize to automatically generate the rank classification metadata" / rOpenSci | taxize tutorial / taxa - taxonomic classes for R
- https://ropensci.org/tutorials/taxize_tutorial/
-
to have taxonomic IDS from many sources (worms, bold, itis)
- itis :
nompoissons = poissons$ESP_LAT[2]
get_tsn(searchterm = as.character(nompoissons), searchtype = "scientific")
Output :
```
get_tsn(searchterm = as.character(nompoissons), searchtype = "scientific")
== 1 queries ===============
Retrieving data for taxon 'Scophthalmus rhombus'
√ Found: Scophthalmus rhombus
== Results =================
- Total: 1
- Found: 1
- Not Found: 0
[1] "172749"
attr(,"class")
[1] "tsn"
attr(,"match")
[1] "found"
attr(,"multiple_matches")
[1] FALSE
attr(,"pattern_match")
[1] FALSE
attr(,"uri")
[1] "https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=172749"
```
-
get synonyms
- retrieve classification :
nompoissons = poissons$ESP_LAT[2]
classification(as.character(nompoissons), db="gbif")
you can specify the data source with the db argument like ITIS or NCBI (No ENTREZ API key provided with NCBI)
Output :
```
classification(as.character(nompoissons), db="gbif")
== 1 queries ===============
Retrieving data for taxon 'Scophthalmus rhombus'
√ Found: Scophthalmus rhombus
== Results =================
* Total: 1
* Found: 1
* Not Found: 0
$Scophthalmus rhombus
name rank id
1 Animalia kingdom 1
2 Chordata phylum 44
3 Actinopterygii class 204
4 Pleuronectiformes order 588
5 Scophthalmidae family 5085
6 Scophthalmus genus 2409400
7 Scophthalmus rhombus species 2409403
attr(,"class")
[1] "classification"
attr(,"db")
[1] "gbif
```
- to have scientific names from common names or vice versa
-
scrubr https://cran.r-project.org/web/packages/scrubr/index.html
-
ritis - Integrated Taxonomic Information Service (ITIS) R client
- https://github.com/ropensci/ritis
- taxa (notably evolution from binomen - taxonomic name classes and parsing methods package) defines taxonomic classes and functions to manipulate them. The goal is to use these classes as low level fundamental taxonomic classes that other R packages can build on and supply robust manipulation functions (e.g. subsetting) that are broadly useful.
-
-
- ecovolve/ph_ecovolve - interface to ecovolve executable, and a higher level interface
- phylomatic/ph_phylomatic - interface to phylomatic executable, and a higher level interface
- phylocom - interface to phylocom executable
- ph_aot - higher level interface to aot
- ph_bladj - higher level interface to bladj
- ph_comdist/ph_comdistnt - higher level interface to comdist
- ph_comstruct - higher level interface to comstruct
- ph_comtrait - higher level interface to comtrait
-
ph_pd - higher level interface to Faith's phylogenetic diversity
-
Données accoustiques
- https://cran.r-project.org/web/packages/bioacoustics/bioacoustics.pdf
# Tests via package R bioacoustics on PNMR AFB Data
install.packages("bioacoustics")
library("bioacoustics")
blob_detection("PNMR_AFB_1786.wav", metadata = TRUE)
threshold_detection("PNMR_AFB_1786.wav", metadata = TRUE)
Output:
$`data`
$`data`$`event_data`
filename starting_time duration freq_max_amp freq_max freq_min bandwidth freq_start
1 PNMR_AFB_1786.wav 00:00:05.646 29.296875 16089.47 16144.78 16063.40 81.38438 16144.78
2 PNMR_AFB_1786.wav 00:00:06.228 27.343750 16096.15 16137.02 16051.26 85.76752 16083.10
3 PNMR_AFB_1786.wav 00:00:14.728 29.296875 16089.90 16136.83 16050.77 86.06028 16091.88
4 PNMR_AFB_1786.wav 00:00:15.312 26.367188 16094.48 16098.43 16045.26 53.17348 16088.76
5 PNMR_AFB_1786.wav 00:00:21.455 5.859375 16096.57 16184.78 16070.83 113.94552 16182.17
6 PNMR_AFB_1786.wav 00:00:23.470 14.648438 16213.01 16213.01 16099.13 113.87569 16101.32
7 PNMR_AFB_1786.wav 00:00:24.010 28.320312 16083.23 16144.83 16045.00 99.82951 16144.83
8 PNMR_AFB_1786.wav 00:00:24.591 26.367188 16083.35 16097.04 16045.17 51.87780 16092.08
freq_center freq_end freq_knee fc freq_bw_knee_fc bin_max_amp pc_freq_max_amp pc_freq_max
1 16073.17 16131.99 16131.99 16072.54 59.443992 16133.52 60.00000 0.000000
2 16072.35 16114.30 16137.02 16091.39 45.632574 16127.97 25.00000 92.857143
3 16090.86 16050.77 16136.83 16080.15 56.680645 16136.18 60.00000 3.333333
4 16088.03 16098.43 16045.26 16093.20 -47.940899 16122.11 55.55556 96.296296
5 16096.57 16144.09 16144.09 16070.83 73.254364 16225.01 50.00000 16.666667
6 16185.08 16196.88 16165.41 16204.47 -39.060591 16337.88 80.00000 80.000000
7 16083.23 16045.00 16089.70 16083.24 6.459109 16135.29 48.27586 0.000000
8 16066.02 16049.63 16049.63 16086.02 -36.389296 16119.45 81.48148 14.814815
pc_freq_min pc_knee temp_bw_knee_fc slope kalman_slope curve_neg curve_pos_start
1 83.33333 96.666667 6.8359375 2.777920 0.81743127 166.99199 -96.36751
2 60.71429 92.857143 1.9531250 3.136641 -0.04311283 170.07980 -11.72001
3 96.66667 3.333333 -25.3906250 2.937524 1.42447332 138.09866 -93.12434
4 77.77778 77.777778 3.9062500 2.016653 -0.15005653 123.92154 -50.71399
5 66.66667 83.333333 0.9765625 19.446702 14.43692359 77.06258 0.00000
6 13.33333 33.333333 -7.8125000 7.773913 -8.67769885 109.52032 -63.07620
7 96.55172 3.448276 -20.5078125 3.525014 0.84390627 160.78292 -94.88639
8 55.55556 96.296296 0.9765625 1.967514 0.84317583 144.93926 -17.38110
curve_pos_end mid_offset snr hd smoothness
1 -70.62448 11.803140 16.40073 -6.345830 267.8748
2 -158.35979 4.478406 13.93181 -5.684036 490.7512
3 -44.97432 3.559369 16.36572 -5.952053 341.5568
4 -73.20755 -7.003559 13.20965 -5.808741 339.9623
5 -77.06258 12.788415 12.39127 -3.891112 195.6713
6 -46.44412 -20.203057 11.45287 -1.140431 244.6858
7 -65.89653 2.335721 16.41052 -5.885345 327.2530
8 -127.55816 11.836430 13.86977 -6.697608 362.6874
$`data`$amp_track
$`data`$amp_track[[1]]
[1] -39.34336 -41.01218 -42.31877 -40.04641 -37.53132 -38.44804 -34.71749 -34.42614 -33.53178
[10] -33.68775 -32.61804 -31.26009 -30.69791 -30.78077 -30.59193 -30.87812 -31.17210 -30.09240
[19] -29.80574 -29.98648 -30.32677 -30.32046 -31.31542 -31.25304 -32.09930 -32.65068 -33.32163
[28] -33.93300 -36.88490 -40.69755
$`data`$amp_track[[2]]
[1] -38.45636 -37.96882 -35.92512 -35.80912 -33.69464 -33.26974 -33.27729 -32.55743 -32.87222
[10] -33.19629 -33.81976 -33.28068 -33.41800 -32.93820 -34.09956 -34.01273 -33.36717 -34.60436
[19] -34.96376 -35.20493 -35.07480 -34.08019 -34.04585 -33.31913 -34.33819 -36.80080 -40.80484
[28] -38.50128
$`data`$amp_track[[3]]
[1] -48.70213 -47.69709 -43.03022 -41.04507 -37.33530 -35.34662 -37.56567 -37.88586 -35.87849
[10] -33.44572 -32.52065 -32.15973 -31.22080 -31.76343 -32.11893 -30.96082 -30.08622 -29.90786
[19] -29.24583 -29.39483 -30.35151 -31.35575 -32.04116 -32.25454 -32.46633 -32.61129 -32.70930
[28] -33.24032 -34.45465 -37.17492
$`data`$amp_track[[4]]
[1] -39.71391 -38.58722 -39.65428 -38.33266 -36.39017 -35.97160 -35.06299 -33.79648 -33.67274
[10] -33.50497 -32.67786 -32.33768 -32.30892 -32.02514 -32.17148 -31.38822 -31.94320 -32.30385
[19] -33.02872 -34.32353 -34.57888 -36.71774 -36.40520 -36.28098 -35.25941 -38.39054 -41.39286
$`data`$amp_track[[5]]
[1] -54.93426 -43.55197 -40.72185 -40.71314 -41.67942 -43.41321
$`data`$amp_track[[6]]
[1] -39.49499 -37.27416 -35.44234 -35.17835 -33.99279 -34.52252 -35.03783 -34.40224 -36.97103
[10] -38.97789 -35.17561 -33.81736 -33.26393 -35.25095 -39.85005
$`data`$amp_track[[7]]
[1] -40.26873 -42.99576 -43.36498 -38.81641 -37.43041 -38.01701 -34.86826 -34.15572 -32.65020
[10] -33.65120 -33.26210 -31.77745 -30.79341 -30.13587 -29.81730 -30.37129 -31.17099 -30.60829
[19] -30.67852 -30.67691 -30.69601 -30.23675 -30.89546 -30.71945 -31.29235 -31.64909 -32.45223
[28] -33.34910 -38.04469
$`data`$amp_track[[8]]
[1] -40.16612 -38.55242 -36.14557 -35.59251 -35.65671 -34.01114 -34.18763 -34.35032 -33.56238
[10] -34.11149 -34.51272 -35.24674 -34.29319 -34.45614 -34.07960 -34.91646 -34.44175 -33.63692
[19] -34.46173 -33.95574 -33.80327 -33.26432 -33.10474 -33.17612 -33.79079 -35.27017 -37.89384
$`data`$freq_track
$`data`$freq_track[[1]]
[1] 16144.78 16119.46 16101.50 16077.16 16080.78 16085.74 16093.73 16085.20 16073.56 16078.01
[11] 16091.62 16094.34 16088.52 16078.95 16076.46 16073.17 16079.68 16091.80 16089.47 16088.42
[21] 16086.23 16082.45 16072.54 16075.42 16069.27 16063.40 16063.90 16072.31 16071.85 16131.99
$`data`$freq_track[[2]]
[1] 16083.10 16077.61 16081.01 16094.58 16095.58 16090.13 16094.43 16096.15 16088.97 16091.70
[11] 16087.92 16092.97 16084.47 16089.14 16072.35 16082.18 16080.78 16051.26 16058.68 16052.98
[21] 16065.53 16072.25 16088.06 16091.07 16091.39 16071.65 16137.02 16114.30
$`data`$freq_track[[3]]
[1] 16091.88 16136.83 16115.15 16109.68 16106.31 16101.83 16088.21 16066.63 16084.54 16116.23
[11] 16098.01 16090.66 16085.33 16085.65 16077.96 16090.86 16092.26 16088.24 16089.90 16081.67
[21] 16068.60 16067.45 16071.62 16072.44 16076.77 16081.18 16082.34 16080.15 16072.97 16050.77
$`data`$freq_track[[4]]
[1] 16088.76 16082.32 16070.69 16077.41 16060.93 16069.48 16074.40 16074.80 16081.17 16083.61
[11] 16091.11 16086.25 16086.80 16088.03 16093.15 16094.48 16085.72 16093.20 16091.76 16085.47
[21] 16086.48 16045.26 16062.93 16071.64 16086.52 16077.02 16098.43
$`data`$freq_track[[5]]
[1] 16182.17 16184.78 16156.48 16096.57 16070.83 16144.09
$`data`$freq_track[[6]]
[1] 16101.32 16118.40 16099.13 16099.46 16124.02 16165.41 16156.90 16185.08 16196.07 16201.33
[11] 16200.44 16211.28 16213.01 16204.47 16196.88
$`data`$freq_track[[7]]
[1] 16144.83 16089.70 16072.96 16075.43 16076.98 16112.56 16105.94 16092.31 16078.48 16075.80
[11] 16086.78 16095.02 16092.92 16090.19 16083.23 16067.74 16065.87 16084.36 16084.20 16088.37
[21] 16089.42 16089.09 16083.24 16085.88 16081.63 16080.21 16079.44 16083.76 16045.00
$`data`$freq_track[[8]]
[1] 16092.08 16091.83 16091.96 16086.13 16097.04 16095.75 16086.18 16089.53 16091.57 16079.39
[11] 16080.38 16064.84 16078.25 16066.02 16069.78 16045.17 16059.80 16055.65 16053.35 16066.80
[21] 16073.00 16084.06 16083.35 16094.76 16089.74 16086.02 16049.63
$`data`$event_start
[1] 185025 204097 482625 501761 703041 769089 786785 805825
$`data`$event_end
[1] 185984 204992 483584 502624 703232 769568 787712 806688
$metadata
$metadata$`file`
$metadata$`file`$`sample_rate`
[1] 32768
$metadata$`file`$bit_depth
[1] 16
attr(,"class")
[1] "threshold_detection"
- Données images
install.packages("exiftoolr")
install_exiftool()
library("exiftoolr")
files <- list.files(pattern = "*.JPG")
dat <- exif_read(files)
exif_read(files)
Output
SourceFile ExifToolVersion FileName Directory FileSize
1 PNMR_AFB_10450.JPG 11.7 PNMR_AFB_10450.JPG . 2343731
FileModifyDate FileAccessDate FileCreateDate FilePermissions
1 2019:10:12 17:11:13+02:00 2019:10:12 17:25:40+02:00 2019:10:12 17:25:40+02:00 666
FileType FileTypeExtension MIMEType ExifByteOrder ImageDescription Make Model
1 JPEG JPG image/jpeg MM Transport maritime en Iroise SONY DSLR-A100
Orientation XResolution YResolution ResolutionUnit Software ModifyDate
1 1 72 72 2 DSLR-A100 v1.04 2013:09:14 12:06:14
Artist YCbCrPositioning Copyright
1 Sandrine Quintin 1 Sandrine Quintin / Agence des aires marines protegees
ExposureTime FNumber ExposureProgram ISO ExifVersion DateTimeOriginal CreateDate
1 0.0025 8 2 100 0221 2013:09:14 00:00:00 2013:09:14 00:00:00
ComponentsConfiguration CompressedBitsPerPixel BrightnessValue ExposureCompensation
1 1 2 3 0 8 9.75 0
MaxApertureValue MeteringMode LightSource Flash FocalLength SubjectArea FileFormat
1 5.598343 5 10 24 120 1936 1296 329 393 0 0 0 2
SonyModelID CreativeStyle DynamicRangeOptimizer MakerNoteVersion AFSensorActive
1 256 None 1 MLT0 4
AFStatusActiveSensor AFStatusTop-right AFStatusBottom-right AFStatusBottom
1 31 -32768 94 -32768
AFStatusMiddleHorizontal AFStatusCenterVertical AFStatusTop AFStatusTop-left AFStatusBottom-left
1 186 31 -32768 -32768 -65
FocusLocked AFPoint AFMode AFStatusLeft AFStatusCenterHorizontal AFStatusRight AFAreaMode
1 64 0 3 -32768 180 186 0
DriveMode Rotation ImageStabilizationSetting DynamicRangeOptimizerMode ExposureCompensationMode
1 5 0 1 1 0
WBBracketShotNumber WhiteBalanceBracketing ExposureBracketShotNumber FlashFunction AverageLV
1 0 0 0 0 9.875
FrameNumber WB_RGBLevels WB_GBRGLevels WB_RedLevelsTungsten
1 0 512 256 366 256 366 512 256 249 257 264 271 281 288 298
WB_BlueLevelsTungsten WB_RedLevelsDaylight WB_BlueLevelsDaylight
1 1152 1104 1056 1004 970 928 890 406 419 431 443 457 470 485 534 512 490 471 447 428 410
WB_RedLevelsCloudy WB_BlueLevelsCloudy WB_RedLevelsFlash
1 428 442 454 471 482 496 512 475 454 435 415 399 382 366 433 447 459 473 488 501 518
WB_BlueLevelsFlash WB_RedLevelsFluorescent WB_BlueLevelsFluorescent
1 512 490 470 446 431 412 395 272 291 437 488 451 568 506 1141 906 783 589 509 497 449
WB_RedLevelsShade WB_BlueLevelsShade WB_RedLevel6500K WB_BlueLevel6500K
1 482 498 512 525 544 558 576 470 450 431 411 393 376 360 490 421
WB_RedLevelCustom WB_BlueLevelCustom WB_RedLevel3500K WB_BlueLevel3500K
1 443 471 334 744
WB_RedLevelsKelvin
1 241 252 263 273 282 292 301 309 318 327 334 343 350 358 364 372 378 384 391 397 401 408 412 419 424 428 433 438 442 447 452 454 462 464 470 472 475 480 482 488 490 493 496 498 501 506 509 512 514 518 520 520 524 528 528 532 534 538 540 540 544 546 546 550 552 556 556 558 558 562 564 564 568 568 570
WB_BlueLevelsKelvin
1 1216 1140 1068 1012 960 914 876 834 802 772 744 720 694 674 654 640 622 606 592 576 564 552 544 532 524 514 506 496 490 482 475 470 462 457 450 445 440 435 431 426 421 419 412 410 406 401 399 395 393 388 384 384 380 378 376 372 370 368 364 362 360 358 356 354 352 350 349 347 347 343 341 339 339 337 336
WB_RBLevelsFlash WB_RBLevelsCoolWhiteF WB_RBLevelsTungsten WB_RBLevelsDaylight WB_RBLevelsCloudy
1 473 446 437 783 271 1004 443 471 471 415
WB_RBLevelsShade WB_RBLevels6500K WB_RBLevelsCustom WB_RBLevels3500K
1 525 411 490 421 443 471 334 744
AEMeteringSegments
1 9.875 10 9.875 10.125 10.125 10.125 10 10.25 10 10 10 10 10 10.125 10.125 10.125 10.25 10 10 10.125 10.25 10 10.125 10.125 10.125 9.875 9.875 9.875 9.5 9.875 9.875 9.875 10 9.625 9.625 9.5 9.5 9.375 9.25 9.875
MeasuredLV TiffMeteringImage
1 9.75 base64:SUkqAAgAAAAOAP4ABAABAAAAAAAAAAABBAABAAAAKAAAAAEBBAABA[..9819 more..]
FocusDistance MaxAperture InternalSerialNumber CompressedImageSize PreviewImageStart
1 3.668016 5.656854 702101705900 2284557 27788
PreviewImageLength SceneMode MinoltaQuality FlashExposureComp Teleconverter RawAndJpgRecording
1 26252 16 2 0 0 0
ZoneMatching ColorTemperature LensType ColorCompensationFilter WhiteBalanceFineTune
1 0 0 24 0 3
ImageStabilization ExposureCompensationSetting HighSpeedSync ShutterSpeedSetting ApertureSetting
1 1 0 0 0.00390625 5.656854
DriveMode2 FocusMode AFPointSelected FlashMode FlashExposureCompSet ISOSetting ZoneMatchingMode
1 5 4 1 0 0 0 0
FlashMetering PrioritySetupShutterRelease SelfTimerTime ContinuousBracketing
1 0 0 0 771
SingleFrameBracketing WhiteBalanceSetting PresetWhiteBalance ColorTemperatureSetting
1 770 32769 2 0
CustomWBSetting DynamicRangeOptimizerSetting FreeMemoryCardImages CustomWBRedLevel
1 0 1 99 0
CustomWBGreenLevel CustomWBBlueLevel CustomWBError SonyImageSize SonyQuality InstantPlaybackTime
1 0 0 0 0 32 2
InstantPlaybackSetup NoiseReduction EyeStartAF RedEyeReduction FlashDefault AutoBracketOrder
1 1 1 0 0 0 0
FocusHoldButton AELButton ControlDialSet AFAssist CardShutterLock LensShutterLock
1 0 0 0 0 1 0
AFAreaIllumination MonitorDisplayOff RecordDisplay PlayDisplay ExposureIndicator
1 0 0 0 0 128
AELExposureIndicator ExposureBracketingIndicatorLast MeteringOffScaleIndicator
1 0 0 0
FlashExposureIndicator FlashExposureIndicatorNext FlashExposureIndicatorLast FocusModeSwitch
1 0 0 0 0
FlashType AELock BatteryState WhiteBalance ColorMode UserComment FlashpixVersion ColorSpace
1 0 0 6 32 0 0100 1
ExifImageWidth ExifImageHeight InteropIndex InteropVersion FileSource SceneType CustomRendered
1 3872 2592 R98 0100 3 1 0
ExposureMode DigitalZoomRatio FocalLengthIn35mmFormat SceneCaptureType GainControl Contrast
1 0 0 180 0 0 0
Saturation Sharpness XPTitle XPAuthor
1 0 0 Trafic maritime en Iroise Sandrine Quintin
XPKeywords XPSubject
1 Parc naturel marin d'Iroise, Mer d'Iroise, Agent public, Transport maritime en Iroise
PrintIMVersion Compression ThumbnailOffset ThumbnailLength CurrentIPTCDigest
1 0300 6 25770 2017 0c1465a8a0430b4558f80bbd36992292
Keywords DateCreated
1 Trafic maritime,Bateau, Parc naturel marin d'Iroise, Mer d'Iroise, Agent public, 2013:09:14
DigitalCreationDate By-line Credit
1 2013:09:14 Sandrine Quintin Sandrine Quintin / Agence des aires marines protegees
Source Writer-Editor ApplicationRecordVersion XMPToolkit
1 Sandrine Quintin Sandrine Quintin 4 Image::ExifTool 9.08
LastKeywordIPTC LastKeywordXMP Caption Creator
1 Trafic maritime,Bateau Trafic maritime,Bateau Transport maritime en Iroise Sandrine Quintin
Description Rights
1 Transport maritime en Iroise Sandrine Quintin / Agence des aires marines protegees
Subject Title Author CaptionWriter ImageWidth
1 Trafic maritime,Bateau Trafic maritime en Iroise Sandrine Quintin Sandrine Quintin 3872
ImageHeight EncodingProcess BitsPerSample ColorComponents YCbCrSubSampling Aperture BlueBalance
1 2592 0 8 3 2 1 8 1.429688
ImageSize LensID Megapixels
1 3872 2592 24 10.03622
PreviewImage RedBalance
1 base64:/9j/2wCEAAgFBgcGBQgHBgcJCAgJDBQNDAsLDBgREg4UHRkeHhwZH[..34951 more..] 2
ScaleFactor35efl ShutterSpeed
1 1.5 0.0025
ThumbnailImage CircleOfConfusion
1 base64:/9j/2wDFABAKChAYKDI8DAwOEho6PDYODBAYKDhEOA4QFhwyVlA+/[..2639 more..] 0.02003084
DOF FOV FocalLength35efl
1 3.52869214310477 3.81879436425615 11.0498994677646 0.709603234563736 180
HyperfocalDistance LightValue
1 89.86143 14.64386
- videos
library("exiftoolr")
mov <- exif_read("PNMR_AFB_K7_N01_Seq_27_Bateau_peche_ligneur.mov")
Output:
SourceFile ExifToolVersion
1 PNMR_AFB_K7_N01_Seq_27_Bateau_peche_ligneur.mov 11.7
FileName Directory FileSize FileModifyDate
1 PNMR_AFB_K7_N01_Seq_27_Bateau_peche_ligneur.mov . 28556655 2019:10:12 17:14:00+02:00
FileAccessDate FileCreateDate FilePermissions FileType FileTypeExtension
1 2019:10:13 11:46:00+02:00 2019:10:12 17:25:40+02:00 666 MOV MOV
MIMEType MajorBrand MinorVersion CompatibleBrands MovieDataSize MovieDataOffset
1 video/quicktime qt 2005.3.0 qt 28540880 48
MovieHeaderVersion CreateDate ModifyDate TimeScale Duration PreferredRate
1 0 2012:04:16 09:26:01 2012:04:16 09:26:01 2500 7.88 1
PreferredVolume PreviewTime PreviewDuration PosterTime SelectionTime SelectionDuration
1 1 0 0 0 0 0
CurrentTime NextTrackID TrackHeaderVersion TrackCreateDate TrackModifyDate TrackID
1 0 5 0 2012:04:16 09:26:01 2012:04:16 09:26:01 1
TrackDuration TrackLayer TrackVolume ImageWidth ImageHeight CleanApertureDimensions
1 7.88 0 0 1440 1080 1888 1062
ProductionApertureDimensions EncodedPixelsDimensions TimeCode GraphicsMode OpColor
1 1920 1080 1440 1080 4 64 32768 32768 32768
CompressorID VendorID SourceImageWidth SourceImageHeight XResolution YResolution CompressorName
1 hdv3 appl 1440 1080 72 72 HDV 1080i50
BitDepth VideoFrameRate PartialSyncSamples Balance
1 24 25 1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 0
AudioFormat AudioChannels AudioBitsPerSample AudioSampleRate LayoutFlags MatrixStructure
1 sowt 1 16 48000 0 1 0 0 0 1 0 0 0 1
MediaHeaderVersion MediaCreateDate MediaModifyDate MediaTimeScale MediaDuration
1 0 2012:04:16 09:26:01 2012:04:16 09:26:01 2500 8.64
GenMediaVersion GenFlags GenGraphicsMode GenOpColor GenBalance TextFont TextFace TextSize
1 0 0 0 0 64 32768 32768 32768 0 22 0 12
TextColor BackgroundColor FontName HandlerClass HandlerVendorID
1 0 0 0 65535 65535 65535 Courier dhlr appl
HandlerDescription OtherFormat OtherName HandlerType
1 base64:R2VzdGlvbm5haXJlIGTVYWxpYXMgQXBwbGU= tmcd 001-B mdta
ComAppleFinalcutstudioMediaUuid ImageSize Megapixels AvgBitrate Rotation
1 3485E89D-EAAD-4E62-A851-69E35C408473 1440 1080 1.5552 28975513 0
- SIG data
install.packages("rgdal")
library(rgdal)
orcounty.poly <- readOGR(dsn=path.expand(file.choose()))
orcounty.poly
Output
...
Slot "plotOrder":
[1] 37 38 31 33 18 19 48 40 10 29 35 15 4 7 51 55 53 50 14 44 21 5 28 26 24 22 11 9 57 2 30 56 13 52 6 49 3 54 46 45 43
[42] 42 41 20 39 47 36 34 17 32 16 27 25 12 23 8 1
Slot "bbox":
min max
x -4.6 -1
y 44.0 48
Slot "proj4string":
CRS arguments:
+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
- geoTIFF data
install.packages("rgdal")
library(rgdal)
GDALinfo(file.choose())
Output
rows 6439
columns 2407
bands 1
lower left origin.x -430971.5
lower left origin.y 6171234
res.x 1.917074
res.y 1.917074
ysign -1
oblique.x 0
oblique.y 0
driver GTiff
projection +proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
file E:\L1\Océan Hackathon 11-13.10.2019\7095_3_gtw.tif
apparent band summary:
GDType hasNoDataValue NoDataValue blockSize1 blockSize2
1 Byte FALSE 0 128 2407
apparent band statistics:
Bmin Bmax Bmean Bsd
1 0 255 NA NA
Metadata:
AREA_OR_POINT=Area
TIFFTAG_COPYRIGHT=SHOM 2017
TIFFTAG_RESOLUTIONUNIT=2 (pixels/inch)
TIFFTAG_XRESOLUTION=300
TIFFTAG_YRESOLUTION=300
- fichiers netCDF :
https://github.com/mdsumner/ncdf4
u <- "http://coastwatch.pfeg.noaa.gov/erddap/griddap/erdQSwind3day"
nc <- ncdf4::nc_open(u)
class(nc)
#> [1] "ncdf4"
## note that "ncdf" argument is required to trigger use of ncdf4
r <- raster::raster(u, ncdf = TRUE, varname = "x_wind")
print(r)
#> class : RasterLayer
#> band : 1 (of 3774 bands)
#> dimensions : 1201, 2881, 3460081 (nrow, ncol, ncell)
#> resolution : 0.125, 0.125 (x, y)
#> extent : -0.0625, 360.0625, -75.0625, 75.0625 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#> data source : http://coastwatch.pfeg.noaa.gov/erddap/griddap/erdQSwind3day
#> names : Zonal.Wind
#> z-value : 932644800
#> zvar : x_wind
#> level : 1
Helper for making maps of species occurrence data
- https://github.com/ropensci/mapr
Petit site sympa pour tester expressions régulières (merci Laura ;) )
- https://regexr.com/
Visualization
vis_dat helps you visualise a dataframe and “get a look at the data” by displaying the variable classes in a dataframe as a plot with vis_dat, and getting a brief look into missing data patterns using vis_miss.
- https://github.com/ropensci/visdat/blob/master/README.md
Count number of points within polygons / Average value of a field for a set of points within a set of polygons .... /
- https://github.com/ropensci/lawn
Utilities / visualization / themes
- https://github.com/ropensci/landscapetools
Création de sites par fiche métadonnées EML
Récupération de données :
- Données du hackathon:
- http://oceanhackathon.indigeo.fr/geocms/maps/oceanhackathon-wpjdbpbc#project
- http://data.ifremer.fr/pdmi/portalssearch/main
- http://donnees-campagnes.flotteoceanographique.fr/
- https://data.shom.fr/donnees#001=eyJjIjpbLTY2MjgwNyw1ODIyOTI3XSwieiI6NiwiciI6MCwibCI6W3sidHlwZSI6IklOVEVSTkFMX0xBWUVSIiwiaWRlbnRpZmllciI6IkZEQ19HRUJDT19QWVItUE5HXzM4NTdfV01UUyIsIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0=
- AFB espèces et habitats -> anniebirolo[@]afbiodiversité.fr
- Génomique / Barcode of life : https://github.com/ropensci/bold/blob/master/README.md
- couches environnementales : http://www.ccafs-climate.org/data/
Standards de métadonnées / Mapping / Spécifications
- EML (https://knb.ecoinformatics.org/#external//emlparser/docs/eml-2.1.1/index.html)
- TETHYS (Passive Accoustic Monitoring) https://tethys.sdsu.edu/
- "Where possible, we use concepts from ISO 19115 or OpenGIS SensorML3, but our emphasis is on meeting the needs of the marine mammal community in the most user-friendly way possible. As a consequence, we deviate from these standards. In addition, there are many concepts that are not covered in these standards such as recording detection effort."
- detection effort -> eml attribute / attributetype / measurementscale / interval / precision ?
- schémas XSD https://github.com/ShekibAziz/Tethys/tree/master/XMLSchema(XSD)/schema
- Darwin-core https://dwc.tdwg.org/
- ISO19139
- http://www.geosource.fr/docs/appendix/glossary/iso19139/index.html
- https://inspire.ec.europa.eu/id/document/tg/metadata-iso19139
- convertisseurs existants
- https://github.com/gbif/eml-profile/blob/master/eml2iso19139.xsl
- https://github.com/NCEAS/iso2eml
- https://github.com/eblondel/geometa
Ontologies et liens avec module semantics
Sources d'ontologies d'intérêt
- https://bioportal.bioontology.org/
- http://vocabs.lter-europe.net/edg/tbl/EnvThes.editor#http%3A%2F%2Fvocabs.lter-europe.net%2FEnvThes%2F21439
- http://semandiv.agroportal.lirmm.fr/
- http://agroportal.lirmm.fr/annotator
- https://www.rdocumentation.org/packages/AtlasRDF/versions/1.8.0/topics/getOntologyMappings
Démarche
- .owl -> .obo via Robot (outil java)
- .obo -> app Shiny
Annotation de données/métadonnées par ontologies :
- annotateur d'ontologies : https://www.bioontology.org/wiki/Annotator_R_Client
- Get mappings for a given class URI from an ontology other than EFO : https://www.rdocumentation.org/packages/AtlasRDF/versions/1.8.0/topics/getOntologyMappings
- ontoCAT: an R package for ontology traversal and search https://academic.oup.com/bioinformatics/article/27/17/2468/223268
Notion de liens avec provenance via ontologie :
- ontologie provenance : https://www.w3.org/TR/prov-o/ -> voir lien avec notion de provenance dans EML
pole-national-donnees-biodiversite/OB1.metadata documentation built on Nov. 5, 2019, 12:59 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Défi OB1
Elie Arnaud, Yvan Le Bras, Oussama Chaib, Juliette Silhol, Laura Leroi, Clément Grandidier, Dorian Cazau
Git de l'équipe OB1 de l'Ocean Hackathon 2019 - Brest.
Description du défi
De plus en plus de données scientifiques sont aujourd'hui accessibles en accès ouvert. Cette mise à disposition est un premier pas essentiel vers un meilleur partage des connaissances et une réutilisation massives des données. Malheureusement, le partage du "matériel brut" ne suffit pas, et aujourd'hui plus que jamais se pose la question de la structuration de ces données. Comment faire en sorte que les données soient suffisamment bien décrites pour pouvoir être compréhensibles et réutilisées le plus largement possible ? Dans le cadre du projet "Pôle national de données de biodiversité", des actions en cours propose l'utilisation d'un standard de metadonnees, l'EML (Ecological Metadata Language), comme standard pivot d'outils et services qui faciliteraient cette structuration des données écologique et leur réutilisation. Des questionnements particuliers ont émergé concernant les données marines et ocean hackathon représente une initiative parfaite pour avancer sur certains aspects et laisser de potentiels hackers proposer d'améliorer ou/et créer des produits d'intérêt.
Objectifs
En termes d'outils, nous travaillons sur le développement d'une application interactive R Shiny nommée MetaSHARK. Nous aimerions développer des “modules” en R ou autre langage permettant d’automatiser les traitements de données marines hétérogènes pour par exemple parcourir des données d'écologie (séquences d'ADN ou données de présence d'espèces) et environnementales (données satellitaires ou autres données décrivant les conditions physico-chimiques du milieu) pour remplir de manière automatique et/ou assistée les champs de métadonnées pouvant l’être (étendues taxonomique, géographique et temporelle de l’étude ...).
Pistes envisagées
- Blockchain & Peer to Peer. Pour distribuer
- A1 les données et/ou métadonnées (MTD)
- A2 logiciels notamment ceux développés en B1 et B2
- Deux modules R Shiny
- B0 Upload de fichiers
- B1 "Inférence" automatique de MTD, notamment basé sur utilisation d'expression régulière (RegEx)
- B2 "Sémantique" sur attributs (aussi nommés variables) : faire un lien entre les attributs et des ressoruces terminologiques exitantes notamment ontologies du domaine
- Récupérer des données hétérogènes proposées au Océan hackathon + extérieures avec leurs MTD et proposer
- C1 d'y ajouter des MTD -> structuration / qualification des MTD
- C2 Faire remonter aux entrepôts des données d'origine les améliorations de MTD
- "Mapping" manuel de standards entre
- D1 ISO19139 / EML / Darwin-Core / Tethys
- Tethys
- Business Model
- Indicateurs / métriques autour de la "complétude" des données en MTD
- Indicateurs / métriques autour des données stockées inutilement car non rétuilisable notamment car MTD pas assez détaillées
Gestion métadonnées
- datapackage https://github.com/frictionlessdata/datapackage-r notamment fonction dataPackage$infer
datapackage=datapackage.r::infer("OBSMAM1.csv",getwd())
ne semble que fonctionner avec des fichiers .csv créer une liste composée de sous-listes décrivant les métadonnées du fichier : path, profile, encoding, name, format, mediatype et schema. Dans la sous-liste "schema" sont distingués les "fields" du dataset (nom des headers), auxquels sont attribués un "name",un "type" et "format", et les missing values.
- metadatar https://github.com/annakrystalli/metadatar
- Apparently, not so "intelligent" than datapackage::infer as it seems there is no inference of min/max, format, attibute type...
- taxonomyCleanr (A workflow and set of functions to clean your taxonomy data using R) https://github.com/EDIorg/taxonomyCleanr
- dataspice https://github.com/ropenscilabs/dataspice dont déjà des choses côté EML : https://github.com/ropenscilabs/dataspice/blob/master/R/eml_to_spice.R
- dataCleanR (A collection of user friendly data cleaning functions - EDIorg/dataCleanr) https://github.com/EDIorg/dataCleanr/blob/master/README.md
- time format management: iso8601_convert converts date and time strings to standard ISO 8601 formatted strings, https://ediorg.github.io/dataCleanr/articles/iso8601_functions.html
Contaminants2 =read.table("Contaminants2.csv", header=TRUE, quote="", sep =";")
dateContaminants2 = Contaminants2$Passage...Date #extration des données "date"
dataContaminants2_cvn= iso8601_convert(dateContaminants2, orders = 'mdy')
ici orders='mdy' car les données "date" sont de format jj/mm/aa mettre orders='mdy HM' si l'heure est rensignée en plus.
- taxize " The EML R package can leverage existing functions in the R package taxize to automatically generate the rank classification metadata" / rOpenSci | taxize tutorial / taxa - taxonomic classes for R
- https://ropensci.org/tutorials/taxize_tutorial/
-
to have taxonomic IDS from many sources (worms, bold, itis)
- itis :
nompoissons = poissons$ESP_LAT[2] get_tsn(searchterm = as.character(nompoissons), searchtype = "scientific")Output : ```get_tsn(searchterm = as.character(nompoissons), searchtype = "scientific") == 1 queries =============== Retrieving data for taxon 'Scophthalmus rhombus' √ Found: Scophthalmus rhombus == Results =================
- Total: 1
- Found: 1
- Not Found: 0 [1] "172749" attr(,"class") [1] "tsn" attr(,"match") [1] "found" attr(,"multiple_matches") [1] FALSE attr(,"pattern_match") [1] FALSE attr(,"uri") [1] "https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=172749" ```
- itis :
-
get synonyms
- retrieve classification :
nompoissons = poissons$ESP_LAT[2] classification(as.character(nompoissons), db="gbif")you can specify the data source with the db argument like ITIS or NCBI (No ENTREZ API key provided with NCBI)
Output : ```
classification(as.character(nompoissons), db="gbif") == 1 queries =============== Retrieving data for taxon 'Scophthalmus rhombus' √ Found: Scophthalmus rhombus == Results ================= * Total: 1 * Found: 1 * Not Found: 0 $
Scophthalmus rhombusname rank id 1 Animalia kingdom 1 2 Chordata phylum 44 3 Actinopterygii class 204 4 Pleuronectiformes order 588 5 Scophthalmidae family 5085 6 Scophthalmus genus 2409400 7 Scophthalmus rhombus species 2409403
attr(,"class") [1] "classification" attr(,"db") [1] "gbif ```
- to have scientific names from common names or vice versa
-
scrubr https://cran.r-project.org/web/packages/scrubr/index.html
-
ritis - Integrated Taxonomic Information Service (ITIS) R client
- https://github.com/ropensci/ritis
- taxa (notably evolution from binomen - taxonomic name classes and parsing methods package) defines taxonomic classes and functions to manipulate them. The goal is to use these classes as low level fundamental taxonomic classes that other R packages can build on and supply robust manipulation functions (e.g. subsetting) that are broadly useful.
- ecovolve/ph_ecovolve - interface to ecovolve executable, and a higher level interface
- phylomatic/ph_phylomatic - interface to phylomatic executable, and a higher level interface
- phylocom - interface to phylocom executable
- ph_aot - higher level interface to aot
- ph_bladj - higher level interface to bladj
- ph_comdist/ph_comdistnt - higher level interface to comdist
- ph_comstruct - higher level interface to comstruct
- ph_comtrait - higher level interface to comtrait
-
ph_pd - higher level interface to Faith's phylogenetic diversity
-
Données accoustiques
- https://cran.r-project.org/web/packages/bioacoustics/bioacoustics.pdf
# Tests via package R bioacoustics on PNMR AFB Data
install.packages("bioacoustics")
library("bioacoustics")
blob_detection("PNMR_AFB_1786.wav", metadata = TRUE)
threshold_detection("PNMR_AFB_1786.wav", metadata = TRUE)
Output:
$`data`
$`data`$`event_data`
filename starting_time duration freq_max_amp freq_max freq_min bandwidth freq_start
1 PNMR_AFB_1786.wav 00:00:05.646 29.296875 16089.47 16144.78 16063.40 81.38438 16144.78
2 PNMR_AFB_1786.wav 00:00:06.228 27.343750 16096.15 16137.02 16051.26 85.76752 16083.10
3 PNMR_AFB_1786.wav 00:00:14.728 29.296875 16089.90 16136.83 16050.77 86.06028 16091.88
4 PNMR_AFB_1786.wav 00:00:15.312 26.367188 16094.48 16098.43 16045.26 53.17348 16088.76
5 PNMR_AFB_1786.wav 00:00:21.455 5.859375 16096.57 16184.78 16070.83 113.94552 16182.17
6 PNMR_AFB_1786.wav 00:00:23.470 14.648438 16213.01 16213.01 16099.13 113.87569 16101.32
7 PNMR_AFB_1786.wav 00:00:24.010 28.320312 16083.23 16144.83 16045.00 99.82951 16144.83
8 PNMR_AFB_1786.wav 00:00:24.591 26.367188 16083.35 16097.04 16045.17 51.87780 16092.08
freq_center freq_end freq_knee fc freq_bw_knee_fc bin_max_amp pc_freq_max_amp pc_freq_max
1 16073.17 16131.99 16131.99 16072.54 59.443992 16133.52 60.00000 0.000000
2 16072.35 16114.30 16137.02 16091.39 45.632574 16127.97 25.00000 92.857143
3 16090.86 16050.77 16136.83 16080.15 56.680645 16136.18 60.00000 3.333333
4 16088.03 16098.43 16045.26 16093.20 -47.940899 16122.11 55.55556 96.296296
5 16096.57 16144.09 16144.09 16070.83 73.254364 16225.01 50.00000 16.666667
6 16185.08 16196.88 16165.41 16204.47 -39.060591 16337.88 80.00000 80.000000
7 16083.23 16045.00 16089.70 16083.24 6.459109 16135.29 48.27586 0.000000
8 16066.02 16049.63 16049.63 16086.02 -36.389296 16119.45 81.48148 14.814815
pc_freq_min pc_knee temp_bw_knee_fc slope kalman_slope curve_neg curve_pos_start
1 83.33333 96.666667 6.8359375 2.777920 0.81743127 166.99199 -96.36751
2 60.71429 92.857143 1.9531250 3.136641 -0.04311283 170.07980 -11.72001
3 96.66667 3.333333 -25.3906250 2.937524 1.42447332 138.09866 -93.12434
4 77.77778 77.777778 3.9062500 2.016653 -0.15005653 123.92154 -50.71399
5 66.66667 83.333333 0.9765625 19.446702 14.43692359 77.06258 0.00000
6 13.33333 33.333333 -7.8125000 7.773913 -8.67769885 109.52032 -63.07620
7 96.55172 3.448276 -20.5078125 3.525014 0.84390627 160.78292 -94.88639
8 55.55556 96.296296 0.9765625 1.967514 0.84317583 144.93926 -17.38110
curve_pos_end mid_offset snr hd smoothness
1 -70.62448 11.803140 16.40073 -6.345830 267.8748
2 -158.35979 4.478406 13.93181 -5.684036 490.7512
3 -44.97432 3.559369 16.36572 -5.952053 341.5568
4 -73.20755 -7.003559 13.20965 -5.808741 339.9623
5 -77.06258 12.788415 12.39127 -3.891112 195.6713
6 -46.44412 -20.203057 11.45287 -1.140431 244.6858
7 -65.89653 2.335721 16.41052 -5.885345 327.2530
8 -127.55816 11.836430 13.86977 -6.697608 362.6874
$`data`$amp_track
$`data`$amp_track[[1]]
[1] -39.34336 -41.01218 -42.31877 -40.04641 -37.53132 -38.44804 -34.71749 -34.42614 -33.53178
[10] -33.68775 -32.61804 -31.26009 -30.69791 -30.78077 -30.59193 -30.87812 -31.17210 -30.09240
[19] -29.80574 -29.98648 -30.32677 -30.32046 -31.31542 -31.25304 -32.09930 -32.65068 -33.32163
[28] -33.93300 -36.88490 -40.69755
$`data`$amp_track[[2]]
[1] -38.45636 -37.96882 -35.92512 -35.80912 -33.69464 -33.26974 -33.27729 -32.55743 -32.87222
[10] -33.19629 -33.81976 -33.28068 -33.41800 -32.93820 -34.09956 -34.01273 -33.36717 -34.60436
[19] -34.96376 -35.20493 -35.07480 -34.08019 -34.04585 -33.31913 -34.33819 -36.80080 -40.80484
[28] -38.50128
$`data`$amp_track[[3]]
[1] -48.70213 -47.69709 -43.03022 -41.04507 -37.33530 -35.34662 -37.56567 -37.88586 -35.87849
[10] -33.44572 -32.52065 -32.15973 -31.22080 -31.76343 -32.11893 -30.96082 -30.08622 -29.90786
[19] -29.24583 -29.39483 -30.35151 -31.35575 -32.04116 -32.25454 -32.46633 -32.61129 -32.70930
[28] -33.24032 -34.45465 -37.17492
$`data`$amp_track[[4]]
[1] -39.71391 -38.58722 -39.65428 -38.33266 -36.39017 -35.97160 -35.06299 -33.79648 -33.67274
[10] -33.50497 -32.67786 -32.33768 -32.30892 -32.02514 -32.17148 -31.38822 -31.94320 -32.30385
[19] -33.02872 -34.32353 -34.57888 -36.71774 -36.40520 -36.28098 -35.25941 -38.39054 -41.39286
$`data`$amp_track[[5]]
[1] -54.93426 -43.55197 -40.72185 -40.71314 -41.67942 -43.41321
$`data`$amp_track[[6]]
[1] -39.49499 -37.27416 -35.44234 -35.17835 -33.99279 -34.52252 -35.03783 -34.40224 -36.97103
[10] -38.97789 -35.17561 -33.81736 -33.26393 -35.25095 -39.85005
$`data`$amp_track[[7]]
[1] -40.26873 -42.99576 -43.36498 -38.81641 -37.43041 -38.01701 -34.86826 -34.15572 -32.65020
[10] -33.65120 -33.26210 -31.77745 -30.79341 -30.13587 -29.81730 -30.37129 -31.17099 -30.60829
[19] -30.67852 -30.67691 -30.69601 -30.23675 -30.89546 -30.71945 -31.29235 -31.64909 -32.45223
[28] -33.34910 -38.04469
$`data`$amp_track[[8]]
[1] -40.16612 -38.55242 -36.14557 -35.59251 -35.65671 -34.01114 -34.18763 -34.35032 -33.56238
[10] -34.11149 -34.51272 -35.24674 -34.29319 -34.45614 -34.07960 -34.91646 -34.44175 -33.63692
[19] -34.46173 -33.95574 -33.80327 -33.26432 -33.10474 -33.17612 -33.79079 -35.27017 -37.89384
$`data`$freq_track
$`data`$freq_track[[1]]
[1] 16144.78 16119.46 16101.50 16077.16 16080.78 16085.74 16093.73 16085.20 16073.56 16078.01
[11] 16091.62 16094.34 16088.52 16078.95 16076.46 16073.17 16079.68 16091.80 16089.47 16088.42
[21] 16086.23 16082.45 16072.54 16075.42 16069.27 16063.40 16063.90 16072.31 16071.85 16131.99
$`data`$freq_track[[2]]
[1] 16083.10 16077.61 16081.01 16094.58 16095.58 16090.13 16094.43 16096.15 16088.97 16091.70
[11] 16087.92 16092.97 16084.47 16089.14 16072.35 16082.18 16080.78 16051.26 16058.68 16052.98
[21] 16065.53 16072.25 16088.06 16091.07 16091.39 16071.65 16137.02 16114.30
$`data`$freq_track[[3]]
[1] 16091.88 16136.83 16115.15 16109.68 16106.31 16101.83 16088.21 16066.63 16084.54 16116.23
[11] 16098.01 16090.66 16085.33 16085.65 16077.96 16090.86 16092.26 16088.24 16089.90 16081.67
[21] 16068.60 16067.45 16071.62 16072.44 16076.77 16081.18 16082.34 16080.15 16072.97 16050.77
$`data`$freq_track[[4]]
[1] 16088.76 16082.32 16070.69 16077.41 16060.93 16069.48 16074.40 16074.80 16081.17 16083.61
[11] 16091.11 16086.25 16086.80 16088.03 16093.15 16094.48 16085.72 16093.20 16091.76 16085.47
[21] 16086.48 16045.26 16062.93 16071.64 16086.52 16077.02 16098.43
$`data`$freq_track[[5]]
[1] 16182.17 16184.78 16156.48 16096.57 16070.83 16144.09
$`data`$freq_track[[6]]
[1] 16101.32 16118.40 16099.13 16099.46 16124.02 16165.41 16156.90 16185.08 16196.07 16201.33
[11] 16200.44 16211.28 16213.01 16204.47 16196.88
$`data`$freq_track[[7]]
[1] 16144.83 16089.70 16072.96 16075.43 16076.98 16112.56 16105.94 16092.31 16078.48 16075.80
[11] 16086.78 16095.02 16092.92 16090.19 16083.23 16067.74 16065.87 16084.36 16084.20 16088.37
[21] 16089.42 16089.09 16083.24 16085.88 16081.63 16080.21 16079.44 16083.76 16045.00
$`data`$freq_track[[8]]
[1] 16092.08 16091.83 16091.96 16086.13 16097.04 16095.75 16086.18 16089.53 16091.57 16079.39
[11] 16080.38 16064.84 16078.25 16066.02 16069.78 16045.17 16059.80 16055.65 16053.35 16066.80
[21] 16073.00 16084.06 16083.35 16094.76 16089.74 16086.02 16049.63
$`data`$event_start
[1] 185025 204097 482625 501761 703041 769089 786785 805825
$`data`$event_end
[1] 185984 204992 483584 502624 703232 769568 787712 806688
$metadata
$metadata$`file`
$metadata$`file`$`sample_rate`
[1] 32768
$metadata$`file`$bit_depth
[1] 16
attr(,"class")
[1] "threshold_detection"
- Données images
install.packages("exiftoolr")
install_exiftool()
library("exiftoolr")
files <- list.files(pattern = "*.JPG")
dat <- exif_read(files)
exif_read(files)
Output
SourceFile ExifToolVersion FileName Directory FileSize
1 PNMR_AFB_10450.JPG 11.7 PNMR_AFB_10450.JPG . 2343731
FileModifyDate FileAccessDate FileCreateDate FilePermissions
1 2019:10:12 17:11:13+02:00 2019:10:12 17:25:40+02:00 2019:10:12 17:25:40+02:00 666
FileType FileTypeExtension MIMEType ExifByteOrder ImageDescription Make Model
1 JPEG JPG image/jpeg MM Transport maritime en Iroise SONY DSLR-A100
Orientation XResolution YResolution ResolutionUnit Software ModifyDate
1 1 72 72 2 DSLR-A100 v1.04 2013:09:14 12:06:14
Artist YCbCrPositioning Copyright
1 Sandrine Quintin 1 Sandrine Quintin / Agence des aires marines protegees
ExposureTime FNumber ExposureProgram ISO ExifVersion DateTimeOriginal CreateDate
1 0.0025 8 2 100 0221 2013:09:14 00:00:00 2013:09:14 00:00:00
ComponentsConfiguration CompressedBitsPerPixel BrightnessValue ExposureCompensation
1 1 2 3 0 8 9.75 0
MaxApertureValue MeteringMode LightSource Flash FocalLength SubjectArea FileFormat
1 5.598343 5 10 24 120 1936 1296 329 393 0 0 0 2
SonyModelID CreativeStyle DynamicRangeOptimizer MakerNoteVersion AFSensorActive
1 256 None 1 MLT0 4
AFStatusActiveSensor AFStatusTop-right AFStatusBottom-right AFStatusBottom
1 31 -32768 94 -32768
AFStatusMiddleHorizontal AFStatusCenterVertical AFStatusTop AFStatusTop-left AFStatusBottom-left
1 186 31 -32768 -32768 -65
FocusLocked AFPoint AFMode AFStatusLeft AFStatusCenterHorizontal AFStatusRight AFAreaMode
1 64 0 3 -32768 180 186 0
DriveMode Rotation ImageStabilizationSetting DynamicRangeOptimizerMode ExposureCompensationMode
1 5 0 1 1 0
WBBracketShotNumber WhiteBalanceBracketing ExposureBracketShotNumber FlashFunction AverageLV
1 0 0 0 0 9.875
FrameNumber WB_RGBLevels WB_GBRGLevels WB_RedLevelsTungsten
1 0 512 256 366 256 366 512 256 249 257 264 271 281 288 298
WB_BlueLevelsTungsten WB_RedLevelsDaylight WB_BlueLevelsDaylight
1 1152 1104 1056 1004 970 928 890 406 419 431 443 457 470 485 534 512 490 471 447 428 410
WB_RedLevelsCloudy WB_BlueLevelsCloudy WB_RedLevelsFlash
1 428 442 454 471 482 496 512 475 454 435 415 399 382 366 433 447 459 473 488 501 518
WB_BlueLevelsFlash WB_RedLevelsFluorescent WB_BlueLevelsFluorescent
1 512 490 470 446 431 412 395 272 291 437 488 451 568 506 1141 906 783 589 509 497 449
WB_RedLevelsShade WB_BlueLevelsShade WB_RedLevel6500K WB_BlueLevel6500K
1 482 498 512 525 544 558 576 470 450 431 411 393 376 360 490 421
WB_RedLevelCustom WB_BlueLevelCustom WB_RedLevel3500K WB_BlueLevel3500K
1 443 471 334 744
WB_RedLevelsKelvin
1 241 252 263 273 282 292 301 309 318 327 334 343 350 358 364 372 378 384 391 397 401 408 412 419 424 428 433 438 442 447 452 454 462 464 470 472 475 480 482 488 490 493 496 498 501 506 509 512 514 518 520 520 524 528 528 532 534 538 540 540 544 546 546 550 552 556 556 558 558 562 564 564 568 568 570
WB_BlueLevelsKelvin
1 1216 1140 1068 1012 960 914 876 834 802 772 744 720 694 674 654 640 622 606 592 576 564 552 544 532 524 514 506 496 490 482 475 470 462 457 450 445 440 435 431 426 421 419 412 410 406 401 399 395 393 388 384 384 380 378 376 372 370 368 364 362 360 358 356 354 352 350 349 347 347 343 341 339 339 337 336
WB_RBLevelsFlash WB_RBLevelsCoolWhiteF WB_RBLevelsTungsten WB_RBLevelsDaylight WB_RBLevelsCloudy
1 473 446 437 783 271 1004 443 471 471 415
WB_RBLevelsShade WB_RBLevels6500K WB_RBLevelsCustom WB_RBLevels3500K
1 525 411 490 421 443 471 334 744
AEMeteringSegments
1 9.875 10 9.875 10.125 10.125 10.125 10 10.25 10 10 10 10 10 10.125 10.125 10.125 10.25 10 10 10.125 10.25 10 10.125 10.125 10.125 9.875 9.875 9.875 9.5 9.875 9.875 9.875 10 9.625 9.625 9.5 9.5 9.375 9.25 9.875
MeasuredLV TiffMeteringImage
1 9.75 base64:SUkqAAgAAAAOAP4ABAABAAAAAAAAAAABBAABAAAAKAAAAAEBBAABA[..9819 more..]
FocusDistance MaxAperture InternalSerialNumber CompressedImageSize PreviewImageStart
1 3.668016 5.656854 702101705900 2284557 27788
PreviewImageLength SceneMode MinoltaQuality FlashExposureComp Teleconverter RawAndJpgRecording
1 26252 16 2 0 0 0
ZoneMatching ColorTemperature LensType ColorCompensationFilter WhiteBalanceFineTune
1 0 0 24 0 3
ImageStabilization ExposureCompensationSetting HighSpeedSync ShutterSpeedSetting ApertureSetting
1 1 0 0 0.00390625 5.656854
DriveMode2 FocusMode AFPointSelected FlashMode FlashExposureCompSet ISOSetting ZoneMatchingMode
1 5 4 1 0 0 0 0
FlashMetering PrioritySetupShutterRelease SelfTimerTime ContinuousBracketing
1 0 0 0 771
SingleFrameBracketing WhiteBalanceSetting PresetWhiteBalance ColorTemperatureSetting
1 770 32769 2 0
CustomWBSetting DynamicRangeOptimizerSetting FreeMemoryCardImages CustomWBRedLevel
1 0 1 99 0
CustomWBGreenLevel CustomWBBlueLevel CustomWBError SonyImageSize SonyQuality InstantPlaybackTime
1 0 0 0 0 32 2
InstantPlaybackSetup NoiseReduction EyeStartAF RedEyeReduction FlashDefault AutoBracketOrder
1 1 1 0 0 0 0
FocusHoldButton AELButton ControlDialSet AFAssist CardShutterLock LensShutterLock
1 0 0 0 0 1 0
AFAreaIllumination MonitorDisplayOff RecordDisplay PlayDisplay ExposureIndicator
1 0 0 0 0 128
AELExposureIndicator ExposureBracketingIndicatorLast MeteringOffScaleIndicator
1 0 0 0
FlashExposureIndicator FlashExposureIndicatorNext FlashExposureIndicatorLast FocusModeSwitch
1 0 0 0 0
FlashType AELock BatteryState WhiteBalance ColorMode UserComment FlashpixVersion ColorSpace
1 0 0 6 32 0 0100 1
ExifImageWidth ExifImageHeight InteropIndex InteropVersion FileSource SceneType CustomRendered
1 3872 2592 R98 0100 3 1 0
ExposureMode DigitalZoomRatio FocalLengthIn35mmFormat SceneCaptureType GainControl Contrast
1 0 0 180 0 0 0
Saturation Sharpness XPTitle XPAuthor
1 0 0 Trafic maritime en Iroise Sandrine Quintin
XPKeywords XPSubject
1 Parc naturel marin d'Iroise, Mer d'Iroise, Agent public, Transport maritime en Iroise
PrintIMVersion Compression ThumbnailOffset ThumbnailLength CurrentIPTCDigest
1 0300 6 25770 2017 0c1465a8a0430b4558f80bbd36992292
Keywords DateCreated
1 Trafic maritime,Bateau, Parc naturel marin d'Iroise, Mer d'Iroise, Agent public, 2013:09:14
DigitalCreationDate By-line Credit
1 2013:09:14 Sandrine Quintin Sandrine Quintin / Agence des aires marines protegees
Source Writer-Editor ApplicationRecordVersion XMPToolkit
1 Sandrine Quintin Sandrine Quintin 4 Image::ExifTool 9.08
LastKeywordIPTC LastKeywordXMP Caption Creator
1 Trafic maritime,Bateau Trafic maritime,Bateau Transport maritime en Iroise Sandrine Quintin
Description Rights
1 Transport maritime en Iroise Sandrine Quintin / Agence des aires marines protegees
Subject Title Author CaptionWriter ImageWidth
1 Trafic maritime,Bateau Trafic maritime en Iroise Sandrine Quintin Sandrine Quintin 3872
ImageHeight EncodingProcess BitsPerSample ColorComponents YCbCrSubSampling Aperture BlueBalance
1 2592 0 8 3 2 1 8 1.429688
ImageSize LensID Megapixels
1 3872 2592 24 10.03622
PreviewImage RedBalance
1 base64:/9j/2wCEAAgFBgcGBQgHBgcJCAgJDBQNDAsLDBgREg4UHRkeHhwZH[..34951 more..] 2
ScaleFactor35efl ShutterSpeed
1 1.5 0.0025
ThumbnailImage CircleOfConfusion
1 base64:/9j/2wDFABAKChAYKDI8DAwOEho6PDYODBAYKDhEOA4QFhwyVlA+/[..2639 more..] 0.02003084
DOF FOV FocalLength35efl
1 3.52869214310477 3.81879436425615 11.0498994677646 0.709603234563736 180
HyperfocalDistance LightValue
1 89.86143 14.64386
- videos
library("exiftoolr")
mov <- exif_read("PNMR_AFB_K7_N01_Seq_27_Bateau_peche_ligneur.mov")
Output:
SourceFile ExifToolVersion
1 PNMR_AFB_K7_N01_Seq_27_Bateau_peche_ligneur.mov 11.7
FileName Directory FileSize FileModifyDate
1 PNMR_AFB_K7_N01_Seq_27_Bateau_peche_ligneur.mov . 28556655 2019:10:12 17:14:00+02:00
FileAccessDate FileCreateDate FilePermissions FileType FileTypeExtension
1 2019:10:13 11:46:00+02:00 2019:10:12 17:25:40+02:00 666 MOV MOV
MIMEType MajorBrand MinorVersion CompatibleBrands MovieDataSize MovieDataOffset
1 video/quicktime qt 2005.3.0 qt 28540880 48
MovieHeaderVersion CreateDate ModifyDate TimeScale Duration PreferredRate
1 0 2012:04:16 09:26:01 2012:04:16 09:26:01 2500 7.88 1
PreferredVolume PreviewTime PreviewDuration PosterTime SelectionTime SelectionDuration
1 1 0 0 0 0 0
CurrentTime NextTrackID TrackHeaderVersion TrackCreateDate TrackModifyDate TrackID
1 0 5 0 2012:04:16 09:26:01 2012:04:16 09:26:01 1
TrackDuration TrackLayer TrackVolume ImageWidth ImageHeight CleanApertureDimensions
1 7.88 0 0 1440 1080 1888 1062
ProductionApertureDimensions EncodedPixelsDimensions TimeCode GraphicsMode OpColor
1 1920 1080 1440 1080 4 64 32768 32768 32768
CompressorID VendorID SourceImageWidth SourceImageHeight XResolution YResolution CompressorName
1 hdv3 appl 1440 1080 72 72 HDV 1080i50
BitDepth VideoFrameRate PartialSyncSamples Balance
1 24 25 1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 0
AudioFormat AudioChannels AudioBitsPerSample AudioSampleRate LayoutFlags MatrixStructure
1 sowt 1 16 48000 0 1 0 0 0 1 0 0 0 1
MediaHeaderVersion MediaCreateDate MediaModifyDate MediaTimeScale MediaDuration
1 0 2012:04:16 09:26:01 2012:04:16 09:26:01 2500 8.64
GenMediaVersion GenFlags GenGraphicsMode GenOpColor GenBalance TextFont TextFace TextSize
1 0 0 0 0 64 32768 32768 32768 0 22 0 12
TextColor BackgroundColor FontName HandlerClass HandlerVendorID
1 0 0 0 65535 65535 65535 Courier dhlr appl
HandlerDescription OtherFormat OtherName HandlerType
1 base64:R2VzdGlvbm5haXJlIGTVYWxpYXMgQXBwbGU= tmcd 001-B mdta
ComAppleFinalcutstudioMediaUuid ImageSize Megapixels AvgBitrate Rotation
1 3485E89D-EAAD-4E62-A851-69E35C408473 1440 1080 1.5552 28975513 0
- SIG data
install.packages("rgdal")
library(rgdal)
orcounty.poly <- readOGR(dsn=path.expand(file.choose()))
orcounty.poly
Output
...
Slot "plotOrder":
[1] 37 38 31 33 18 19 48 40 10 29 35 15 4 7 51 55 53 50 14 44 21 5 28 26 24 22 11 9 57 2 30 56 13 52 6 49 3 54 46 45 43
[42] 42 41 20 39 47 36 34 17 32 16 27 25 12 23 8 1
Slot "bbox":
min max
x -4.6 -1
y 44.0 48
Slot "proj4string":
CRS arguments:
+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
- geoTIFF data
install.packages("rgdal")
library(rgdal)
GDALinfo(file.choose())
Output
rows 6439
columns 2407
bands 1
lower left origin.x -430971.5
lower left origin.y 6171234
res.x 1.917074
res.y 1.917074
ysign -1
oblique.x 0
oblique.y 0
driver GTiff
projection +proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
file E:\L1\Océan Hackathon 11-13.10.2019\7095_3_gtw.tif
apparent band summary:
GDType hasNoDataValue NoDataValue blockSize1 blockSize2
1 Byte FALSE 0 128 2407
apparent band statistics:
Bmin Bmax Bmean Bsd
1 0 255 NA NA
Metadata:
AREA_OR_POINT=Area
TIFFTAG_COPYRIGHT=SHOM 2017
TIFFTAG_RESOLUTIONUNIT=2 (pixels/inch)
TIFFTAG_XRESOLUTION=300
TIFFTAG_YRESOLUTION=300
- fichiers netCDF : https://github.com/mdsumner/ncdf4
u <- "http://coastwatch.pfeg.noaa.gov/erddap/griddap/erdQSwind3day"
nc <- ncdf4::nc_open(u)
class(nc)
#> [1] "ncdf4"
## note that "ncdf" argument is required to trigger use of ncdf4
r <- raster::raster(u, ncdf = TRUE, varname = "x_wind")
print(r)
#> class : RasterLayer
#> band : 1 (of 3774 bands)
#> dimensions : 1201, 2881, 3460081 (nrow, ncol, ncell)
#> resolution : 0.125, 0.125 (x, y)
#> extent : -0.0625, 360.0625, -75.0625, 75.0625 (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
#> data source : http://coastwatch.pfeg.noaa.gov/erddap/griddap/erdQSwind3day
#> names : Zonal.Wind
#> z-value : 932644800
#> zvar : x_wind
#> level : 1
Helper for making maps of species occurrence data - https://github.com/ropensci/mapr
Petit site sympa pour tester expressions régulières (merci Laura ;) ) - https://regexr.com/
Visualization
vis_dat helps you visualise a dataframe and “get a look at the data” by displaying the variable classes in a dataframe as a plot with vis_dat, and getting a brief look into missing data patterns using vis_miss. - https://github.com/ropensci/visdat/blob/master/README.md Count number of points within polygons / Average value of a field for a set of points within a set of polygons .... / - https://github.com/ropensci/lawn Utilities / visualization / themes - https://github.com/ropensci/landscapetools
Création de sites par fiche métadonnées EML
Récupération de données :
- Données du hackathon:
- http://oceanhackathon.indigeo.fr/geocms/maps/oceanhackathon-wpjdbpbc#project
- http://data.ifremer.fr/pdmi/portalssearch/main
- http://donnees-campagnes.flotteoceanographique.fr/
- https://data.shom.fr/donnees#001=eyJjIjpbLTY2MjgwNyw1ODIyOTI3XSwieiI6NiwiciI6MCwibCI6W3sidHlwZSI6IklOVEVSTkFMX0xBWUVSIiwiaWRlbnRpZmllciI6IkZEQ19HRUJDT19QWVItUE5HXzM4NTdfV01UUyIsIm9wYWNpdHkiOjEsInZpc2liaWxpdHkiOnRydWV9XX0=
- AFB espèces et habitats -> anniebirolo[@]afbiodiversité.fr
- Génomique / Barcode of life : https://github.com/ropensci/bold/blob/master/README.md
- couches environnementales : http://www.ccafs-climate.org/data/
Standards de métadonnées / Mapping / Spécifications
- EML (https://knb.ecoinformatics.org/#external//emlparser/docs/eml-2.1.1/index.html)
- TETHYS (Passive Accoustic Monitoring) https://tethys.sdsu.edu/
- "Where possible, we use concepts from ISO 19115 or OpenGIS SensorML3, but our emphasis is on meeting the needs of the marine mammal community in the most user-friendly way possible. As a consequence, we deviate from these standards. In addition, there are many concepts that are not covered in these standards such as recording detection effort."
- detection effort -> eml attribute / attributetype / measurementscale / interval / precision ?
- schémas XSD https://github.com/ShekibAziz/Tethys/tree/master/XMLSchema(XSD)/schema
- Darwin-core https://dwc.tdwg.org/
- ISO19139
- http://www.geosource.fr/docs/appendix/glossary/iso19139/index.html
- https://inspire.ec.europa.eu/id/document/tg/metadata-iso19139
- convertisseurs existants
- https://github.com/gbif/eml-profile/blob/master/eml2iso19139.xsl
- https://github.com/NCEAS/iso2eml
- https://github.com/eblondel/geometa
Ontologies et liens avec module semantics
Sources d'ontologies d'intérêt
- https://bioportal.bioontology.org/
- http://vocabs.lter-europe.net/edg/tbl/EnvThes.editor#http%3A%2F%2Fvocabs.lter-europe.net%2FEnvThes%2F21439
- http://semandiv.agroportal.lirmm.fr/
- http://agroportal.lirmm.fr/annotator
- https://www.rdocumentation.org/packages/AtlasRDF/versions/1.8.0/topics/getOntologyMappings
Démarche
- .owl -> .obo via Robot (outil java)
- .obo -> app Shiny
Annotation de données/métadonnées par ontologies :
- annotateur d'ontologies : https://www.bioontology.org/wiki/Annotator_R_Client
- Get mappings for a given class URI from an ontology other than EFO : https://www.rdocumentation.org/packages/AtlasRDF/versions/1.8.0/topics/getOntologyMappings
- ontoCAT: an R package for ontology traversal and search https://academic.oup.com/bioinformatics/article/27/17/2468/223268
Notion de liens avec provenance via ontologie :
- ontologie provenance : https://www.w3.org/TR/prov-o/ -> voir lien avec notion de provenance dans EML
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