RRID:BDSC_64349
DOI: 10.1128/aem.01135-26
Resource: RRID:BDSC_64349
Curator: @scibot
SciCrunch record: RRID:BDSC_64349
RRID:BDSC_64349
DOI: 10.1128/aem.01135-26
Resource: RRID:BDSC_64349
Curator: @scibot
SciCrunch record: RRID:BDSC_64349
RRID:SCR_003070
DOI: 10.1126/sciadv.aeg2327
Resource: ImageJ (RRID:SCR_003070)
Curator: @scibot
SciCrunch record: RRID:SCR_003070
RRID:SCR_002798
DOI: 10.1111/jnc.70563
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
SciCrunch record: RRID:SCR_002798
RRID:AB_10624867
DOI: 10.1111/jnc.70563
Resource: (Cell Signaling Technology Cat# 5832, RRID:AB_10624867)
Curator: @scibot
SciCrunch record: RRID:AB_10624867
RRID:SCR_019037
DOI: 10.1111/jnc.70563
Resource: Bio Rad ChemiDoc MP Imaging System (RRID:SCR_019037)
Curator: @scibot
SciCrunch record: RRID:SCR_019037
RRID:SCR_001905
DOI: 10.1111/jnc.70563
Resource: R Project for Statistical Computing (RRID:SCR_001905)
Curator: @scibot
SciCrunch record: RRID:SCR_001905
RRID:SCR_023716
DOI: 10.1111/jnc.70563
Resource: LipidSearch (RRID:SCR_023716)
Curator: @scibot
SciCrunch record: RRID:SCR_023716
RRID:AB_476697
DOI: 10.1111/jnc.70563
Resource: (Sigma-Aldrich Cat# A2228, RRID:AB_476697)
Curator: @scibot
SciCrunch record: RRID:AB_476697
RRID:SCR_025713
DOI: 10.1111/jnc.70563
Resource: Thermo Scientific Vanquish Horizon UHPLC System (RRID:SCR_025713)
Curator: @scibot
SciCrunch record: RRID:SCR_025713
RRID:AB_331250
DOI: 10.1111/jnc.70563
Resource: (Cell Signaling Technology Cat# 2535, RRID:AB_331250)
Curator: @scibot
SciCrunch record: RRID:AB_331250
RRID:MMRRC_034832-JAX
DOI: 10.1111/jnc.70563
Resource: (MMRRC Cat# 034832-JAX,RRID:MMRRC_034832-JAX)
Curator: @scibot
SciCrunch record: RRID:MMRRC_034832-JAX
RRID:IMSR_JAX:000664
DOI: 10.1111/jnc.70563
Resource: RRID:IMSR_JAX:000664
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:000664
RRID:SCR_000441
DOI: 10.1111/jnc.70563
Resource: EthoVision XT (RRID:SCR_000441)
Curator: @scibot
SciCrunch record: RRID:SCR_000441
RRID:SCR_013726
DOI: 10.1111/jnc.70563
Resource: G*Power (RRID:SCR_013726)
Curator: @scibot
SciCrunch record: RRID:SCR_013726
RRID:SCR_020425
DOI: 10.1111/jnc.70563
Resource: Q Exactive HF Hybrid Quadrupole-Orbitrap Mass Spectrometer Q Exactive (RRID:SCR_020425)
Curator: @scibot
SciCrunch record: RRID:SCR_020425
RRID:SCR_022444
DOI: 10.1111/jnc.70551
Resource: University of Pennsylvania Penn Vet Extracellular Vesicle Core Facility (RRID:SCR_022444)
Curator: @scibot
SciCrunch record: RRID:SCR_022444
RRID:SCR_022373
DOI: 10.1111/jnc.70551
Resource: University of Pennsylvania Perelman School of Medicine Cell and Developmental Biology Microscopy Core Facility (RRID:SCR_022373)
Curator: @scibot
SciCrunch record: RRID:SCR_022373
RRID:AB_2138181
DOI: 10.1111/jnc.70551
Resource: (Synaptic Systems Cat# 188 004, RRID:AB_2138181)
Curator: @scibot
SciCrunch record: RRID:AB_2138181
RRID:AB_2622239
DOI: 10.1111/jnc.70551
Resource: (Synaptic Systems Cat# 101 006, RRID:AB_2622239)
Curator: @scibot
SciCrunch record: RRID:AB_2622239
RRID:AB_221569
DOI: 10.1111/jnc.70551
Resource: (Molecular Probes Cat# A-11122, RRID:AB_221569)
Curator: @scibot
SciCrunch record: RRID:AB_221569
RRID:CVCL_0045
DOI: 10.1111/jnc.70551
Resource: (DSMZ Cat# ACC-305, RRID:CVCL_0045)
Curator: @scibot
SciCrunch record: RRID:CVCL_0045
RRID:AB_10805139
DOI: 10.1111/jnc.70551
Resource: (Synaptic Systems Cat# 106 011C2, RRID:AB_10805139)
Curator: @scibot
SciCrunch record: RRID:AB_10805139
RRID:AB_2716249
DOI: 10.1111/bph.70690
Resource: (Cell Signaling Technology Cat# 8889, RRID:AB_2716249)
Curator: @scibot
SciCrunch record: RRID:AB_2716249
RRID:AB_10694704
DOI: 10.1111/bph.70690
Resource: (Cell Signaling Technology Cat# 4408, RRID:AB_10694704)
Curator: @scibot
SciCrunch record: RRID:AB_10694704
RRID:AB_2716568
DOI: 10.1111/bph.70690
Resource: (Proteintech Cat# 25859-1-AP, RRID:AB_2716568)
Curator: @scibot
SciCrunch record: RRID:AB_2716568
AB_1904025
DOI: 10.1111/bph.70690
Resource: (Cell Signaling Technology Cat# 4412, RRID:AB_1904025)
Curator: @scibot
SciCrunch record: RRID:AB_1904025
RRID:AB_2665387
DOI: 10.1111/bph.70690
Resource: (Novus Cat# NBP2-50037, RRID:AB_2665387)
Curator: @scibot
SciCrunch record: RRID:AB_2665387
RRID:AB_10859369
DOI: 10.1111/bph.70676
Resource: (Cell Signaling Technology Cat# 8242, RRID:AB_10859369)
Curator: @scibot
SciCrunch record: RRID:AB_10859369
RRID:AB_3086883
DOI: 10.1111/bph.70676
Resource: RRID:AB_3086883
Curator: @scibot
SciCrunch record: RRID:AB_3086883
RRID:CVCL_0002
DOI: 10.1111/bph.70676
Resource: (RRID:CVCL_0002)
Curator: @scibot
SciCrunch record: RRID:CVCL_0002
RRID:AB_2087568
DOI: 10.1111/bph.70676
Resource: (Proteintech Cat# 12335-1-AP, RRID:AB_2087568)
Curator: @scibot
SciCrunch record: RRID:AB_2087568
RRID:AB_2879037
DOI: 10.1111/bph.70676
Resource: (Proteintech Cat# 22225-1-AP, RRID:AB_2879037)
Curator: @scibot
SciCrunch record: RRID:AB_2879037
RRID:AB_2750839
DOI: 10.1111/bph.70676
Resource: (Cell Signaling Technology Cat# 58169, RRID:AB_2750839)
Curator: @scibot
SciCrunch record: RRID:AB_2750839
RRID:AB_2797976
DOI: 10.1111/bph.70676
Resource: (Cell Signaling Technology Cat# 12644, RRID:AB_2797976)
Curator: @scibot
SciCrunch record: RRID:AB_2797976
RRID:CVCL_0063
DOI: 10.1111/bph.70655
Resource: (RRID:CVCL_0063)
Curator: @scibot
SciCrunch record: RRID:CVCL_0063
RRID:CVCL_0123
DOI: 10.1111/bph.70655
Resource: (ATCC Cat# CL-173, RRID:CVCL_0123)
Curator: @scibot
SciCrunch record: RRID:CVCL_0123
plasmid_53583
DOI: 10.1111/acel.70711
Resource: RRID:Addgene_53583
Curator: @scibot
SciCrunch record: RRID:Addgene_53583
Addgene_19328
DOI: 10.1098/rstb.2025.0362
Resource: RRID:Addgene_19328
Curator: @scibot
SciCrunch record: RRID:Addgene_19328
RRID:SCR_022689
DOI: 10.1098/rstb.2025.0362
Resource: Drexel University Cell Imaging Center Core Facility (RRID:SCR_022689)
Curator: @scibot
SciCrunch record: RRID:SCR_022689
Addgene_11354
DOI: 10.1096/fj.202602705RR
Resource: RRID:Addgene_11354
Curator: @scibot
SciCrunch record: RRID:Addgene_11354
RRID:SCR_016962
DOI: 10.1096/fj.202602705RR
Resource: fastp (RRID:SCR_016962)
Curator: @scibot
SciCrunch record: RRID:SCR_016962
RRID:CVCL_KB28
DOI: 10.1096/fj.202602705RR
Resource: (RRID:CVCL_KB28)
Curator: @scibot
SciCrunch record: RRID:CVCL_KB28
RRID:SCR_003070
DOI: 10.1096/fj.202602523RR
Resource: ImageJ (RRID:SCR_003070)
Curator: @scibot
SciCrunch record: RRID:SCR_003070
RRID:SCR_016339
DOI: 10.1096/fj.202602523RR
Resource: Monocle2 (RRID:SCR_016339)
Curator: @scibot
SciCrunch record: RRID:SCR_016339
RRID:SCR_005223
DOI: 10.1096/fj.202602523RR
Resource: STRING (RRID:SCR_005223)
Curator: @scibot
SciCrunch record: RRID:SCR_005223
RRID:SCR_016884
DOI: 10.1096/fj.202602523RR
Resource: clusterProfiler (RRID:SCR_016884)
Curator: @scibot
SciCrunch record: RRID:SCR_016884
RRID:RGD_70508
DOI: 10.1096/fj.202602523RR
Resource: (RGD Cat# 70508,RRID:RGD_70508)
Curator: @scibot
SciCrunch record: RRID:RGD_70508
Addgene_111188
DOI: 10.1096/fj.202602184R
Resource: RRID:Addgene_111188
Curator: @scibot
SciCrunch record: RRID:Addgene_111188
Addgene_54467
DOI: 10.1096/fj.202602184R
Resource: RRID:Addgene_54467
Curator: @scibot
SciCrunch record: RRID:Addgene_54467
Addgene_255535
DOI: 10.1093/synbio/ysag010
Resource: RRID:Addgene_255535
Curator: @scibot
SciCrunch record: RRID:Addgene_255535
Addgene_255536
DOI: 10.1093/synbio/ysag010
Resource: RRID:Addgene_255536
Curator: @scibot
SciCrunch record: RRID:Addgene_255536
Addgene_163756
DOI: 10.1093/synbio/ysag010
Resource: RRID:Addgene_163756
Curator: @scibot
SciCrunch record: RRID:Addgene_163756
plasmid_87377
DOI: 10.1093/nar/gkag854
Resource: RRID:Addgene_87377
Curator: @scibot
SciCrunch record: RRID:Addgene_87377
RRID:AB_330288
DOI: 10.1080/20002297.2026.2739029
Resource: (Cell Signaling Technology Cat# 4967, RRID:AB_330288)
Curator: @scibot
SciCrunch record: RRID:AB_330288
RRID:SCR_002798
DOI: 10.1080/1028415X.2026.2738370
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
SciCrunch record: RRID:SCR_002798
RRID:SCR_018042
DOI: 10.1080/1028415X.2026.2738370
Resource: Thermo Scientific Nanodrop 2000 microvolume spectrophotometer (RRID:SCR_018042)
Curator: @scibot
SciCrunch record: RRID:SCR_018042
RRID:SCR_025096
DOI: 10.1080/1028415X.2026.2738370
Resource: Roche Diagnostics (RRID:SCR_025096)
Curator: @scibot
SciCrunch record: RRID:SCR_025096
RRID:SCR_014289
DOI: 10.1080/1028415X.2026.2738370
Resource: ANY-maze (RRID:SCR_014289)
Curator: @scibot
SciCrunch record: RRID:SCR_014289
RRID:RGD_13508588
DOI: 10.1080/1028415X.2026.2738370
Resource: (RGD Cat# 13508588,RRID:RGD_13508588)
Curator: @scibot
SciCrunch record: RRID:RGD_13508588
RRID:Addgene_137132
DOI: 10.1073/pnas.2611533123
Resource: RRID:Addgene_137132
Curator: @scibot
SciCrunch record: RRID:Addgene_137132
plasmid_22612
DOI: 10.1073/pnas.2522111123
Resource: RRID:Addgene_22612
Curator: @scibot
SciCrunch record: RRID:Addgene_22612
plasmid_27793
DOI: 10.1073/pnas.2424514123
Resource: RRID:Addgene_27793
Curator: @scibot
SciCrunch record: RRID:Addgene_27793
plasmid_117273
DOI: 10.1073/pnas.2424514123
Resource: RRID:Addgene_117273
Curator: @scibot
SciCrunch record: RRID:Addgene_117273
Addgene_12253
DOI: 10.1039/d6cb00175k
Resource: RRID:Addgene_12253
Curator: @scibot
SciCrunch record: RRID:Addgene_12253
Addgene_8454
DOI: 10.1039/d6cb00175k
Resource: RRID:Addgene_8454
Curator: @scibot
SciCrunch record: RRID:Addgene_8454
Addgene_44363
DOI: 10.1038/s44321-026-00496-4
Resource: RRID:Addgene_44363
Curator: @scibot
SciCrunch record: RRID:Addgene_44363
RRID:AB_2536183
DOI: 10.1038/s44318-026-00914-w
Resource: (Thermo Fisher Scientific Cat# A-31573, RRID:AB_2536183)
Curator: @scibot
SciCrunch record: RRID:AB_2536183
RRID:AB_839504
DOI: 10.1038/s44318-026-00914-w
Resource: (Wako Cat# 019-19741, RRID:AB_839504)
Curator: @scibot
SciCrunch record: RRID:AB_839504
RRID:AB_2247211
DOI: 10.1038/s44318-026-00914-w
Resource: (Cell Signaling Technology Cat# 2250, RRID:AB_2247211)
Curator: @scibot
SciCrunch record: RRID:AB_2247211
RRID:AB_2491009
DOI: 10.1038/s44318-026-00914-w
Resource: (Cell Signaling Technology Cat# 9145, RRID:AB_2491009)
Curator: @scibot
SciCrunch record: RRID:AB_2491009
RRID:AB_2861054
DOI: 10.1038/s44318-026-00914-w
Resource: (BioLegend Cat# 682205, RRID:AB_2861054)
Curator: @scibot
SciCrunch record: RRID:AB_2861054
RRID:CVCL_0059
DOI: 10.1038/s42003-026-10435-1
Resource: (IZSLER Cat# BS CL 86, RRID:CVCL_0059)
Curator: @scibot
SciCrunch record: RRID:CVCL_0059
RRID:CVCL_0166
DOI: 10.1038/s42003-026-10435-1
Resource: RRID:CVCL_0166
Curator: @scibot
SciCrunch record: RRID:CVCL_0166
RRID:CVCL_0063
DOI: 10.1038/s42003-026-10435-1
Resource: (RRID:CVCL_0063)
Curator: @scibot
SciCrunch record: RRID:CVCL_0063
RRID:CVCL_6379
DOI: 10.1038/s42003-026-10435-1
Resource: (ATCC Cat# CRL-2541, RRID:CVCL_6379)
Curator: @scibot
SciCrunch record: RRID:CVCL_6379
RRID:CVCL_2160
DOI: 10.1038/s42003-026-10435-1
Resource: (BCRC Cat# 60057, RRID:CVCL_2160)
Curator: @scibot
SciCrunch record: RRID:CVCL_2160
RRID:CVCL_0006
DOI: 10.1038/s42003-026-10435-1
Resource: (RRID:CVCL_0006)
Curator: @scibot
SciCrunch record: RRID:CVCL_0006
RRID:SCR_006431
DOI: 10.1038/s41746-026-03227-8
Resource: Parkinson's Progression Markers Initiative (RRID:SCR_006431)
Curator: @scibot
SciCrunch record: RRID:SCR_006431
Addgene_105621
DOI: 10.1038/s41698-026-01534-7
Resource: RRID:Addgene_105621
Curator: @scibot
SciCrunch record: RRID:Addgene_105621
RRID:SCR_022375
DOI: 10.1038/s41564-026-02501-5
Resource: University of Pennsylvania Perelman School of Medicine Electron Microscopy Resource Lab Core Facility (RRID:SCR_022375)
Curator: @scibot
SciCrunch record: RRID:SCR_022375
plasmid_15550
DOI: 10.1038/s41556-026-02030-7
Resource: RRID:Addgene_15550
Curator: @scibot
SciCrunch record: RRID:Addgene_15550
RRID:SCR_021728
DOI: 10.1038/s41467-026-78166-9
Resource: University of Texas at Austin Biological Mass Spectrometry Proteomics Core Facility (RRID:SCR_021728)
Curator: @scibot
SciCrunch record: RRID:SCR_021728
plasmid_12259
DOI: 10.1038/s41467-026-77465-5
Resource: RRID:Addgene_12259
Curator: @scibot
SciCrunch record: RRID:Addgene_12259
Addgene_69982
DOI: 10.1038/s41467-026-76778-9
Resource: RRID:Addgene_69982
Curator: @scibot
SciCrunch record: RRID:Addgene_69982
Addgene_112094
DOI: 10.1038/s41467-026-76778-9
Resource: RRID:Addgene_112094
Curator: @scibot
SciCrunch record: RRID:Addgene_112094
plasmid_154754
DOI: 10.1038/s41467-026-76680-4
Resource: RRID:Addgene_154754
Curator: @scibot
SciCrunch record: RRID:Addgene_154754
Addgene_185474
DOI: 10.1038/s41467-025-68076-7
Resource: RRID:Addgene_185474
Curator: @scibot
SciCrunch record: RRID:Addgene_185474
Addgene_185473
DOI: 10.1038/s41467-025-68076-7
Resource: RRID:Addgene_185473
Curator: @scibot
SciCrunch record: RRID:Addgene_185473
Addgene_52962
DOI: 10.1038/s41467-025-68076-7
Resource: RRID:Addgene_52962
Curator: @scibot
SciCrunch record: RRID:Addgene_52962
RRID:SCR_000305
DOI: 10.1038/s41416-026-03638-0
Resource: PyMOL (RRID:SCR_000305)
Curator: @scibot
SciCrunch record: RRID:SCR_000305
RRID:SCR_012820
DOI: 10.1038/s41416-026-03638-0
Resource: Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) (RRID:SCR_012820)
Curator: @scibot
SciCrunch record: RRID:SCR_012820
RRID:SCR_002700
DOI: 10.1038/s41416-026-03638-0
Resource: DrugBank (RRID:SCR_002700)
Curator: @scibot
SciCrunch record: RRID:SCR_002700
RRID:SCR_021238
DOI: 10.1038/s41416-026-03638-0
Resource: igraph for R (RRID:SCR_021238)
Curator: @scibot
SciCrunch record: RRID:SCR_021238
RRID:SCR_006511
DOI: 10.1038/s41416-026-03638-0
Resource: PDBsum (RRID:SCR_006511)
Curator: @scibot
SciCrunch record: RRID:SCR_006511
RRID:SCR_014782
DOI: 10.1038/s41416-026-03638-0
Resource: OncoKB (RRID:SCR_014782)
Curator: @scibot
SciCrunch record: RRID:SCR_014782
RRID:SCR_018910
DOI: 10.1038/s41416-026-03638-0
Resource: PAXdb (RRID:SCR_018910)
Curator: @scibot
SciCrunch record: RRID:SCR_018910
RRID:SCR_002260
DOI: 10.1038/s41416-026-03638-0
Resource: COSMIC - Catalogue Of Somatic Mutations In Cancer (RRID:SCR_002260)
Curator: @scibot
SciCrunch record: RRID:SCR_002260
RRID:SCR_005223
DOI: 10.1038/s41416-026-03638-0
Resource: STRING (RRID:SCR_005223)
Curator: @scibot
SciCrunch record: RRID:SCR_005223
RRID:SCR_014583
DOI: 10.1038/s41416-026-03638-0
Resource: FastQC (RRID:SCR_014583)
Curator: @scibot
SciCrunch record: RRID:SCR_014583
RRID:SCR_002344
DOI: 10.1038/s41416-026-03638-0
Resource: Ensembl (RRID:SCR_002344)
Curator: @scibot
SciCrunch record: RRID:SCR_002344
RRID:SCR_002380
DOI: 10.1038/s41416-026-03638-0
Resource: Universal Protein Resource (RRID:SCR_002380)
Curator: @scibot
SciCrunch record: RRID:SCR_002380
RRID:SCR_016582
DOI: 10.1038/s41416-026-03638-0
Resource: kallisto (RRID:SCR_016582)
Curator: @scibot
SciCrunch record: RRID:SCR_016582
RRID:SCR_011848
DOI: 10.1038/s41416-026-03638-0
Resource: Trimmomatic (RRID:SCR_011848)
Curator: @scibot
SciCrunch record: RRID:SCR_011848
RRID:SCR_014601
DOI: 10.1038/s41416-026-03638-0
Resource: ggplot2 (RRID:SCR_014601)
Curator: @scibot
SciCrunch record: RRID:SCR_014601
RRID:SCR_014514
DOI: 10.1038/s41416-026-03638-0
Resource: Genomic Data Commons Data Portal (GDC Data Portal) (RRID:SCR_014514)
Curator: @scibot
SciCrunch record: RRID:SCR_014514
RRID:SCR_007425
DOI: 10.1038/s41416-026-03638-0
Resource: RRID:SCR_007425
Curator: @scibot
SciCrunch record: RRID:SCR_007425
RRID:SCR_024350
DOI: 10.1038/s41416-026-03638-0
Resource: sra-toolkit (RRID:SCR_024350)
Curator: @scibot
SciCrunch record: RRID:SCR_024350
RRID:SCR_022865
DOI: 10.1038/s41416-026-03638-0
Resource: DIA-NN (RRID:SCR_022865)
Curator: @scibot
SciCrunch record: RRID:SCR_022865
RRID:SCR_017683
DOI: 10.1038/s41416-026-03638-0
Resource: TCGAbiolinks (RRID:SCR_017683)
Curator: @scibot
SciCrunch record: RRID:SCR_017683
RRID:SCR_013042
DOI: 10.1038/s41416-026-03638-0
Resource: Genotype-Tissue Expression (RRID:SCR_013042)
Curator: @scibot
SciCrunch record: RRID:SCR_013042
Addgene_21075
DOI: 10.1038/s41413-026-00560-2
Resource: RRID:Addgene_21075
Curator: @scibot
SciCrunch record: RRID:Addgene_21075
CVCL_0035
DOI: 10.1021/acsomega.6c06421
Resource: (ECACC Cat# 90112714, RRID:CVCL_0035)
Curator: @scibot
SciCrunch record: RRID:CVCL_0035
RRID:SCR_003070
DOI: 10.1021/acsomega.6c06421
Resource: ImageJ (RRID:SCR_003070)
Curator: @scibot
SciCrunch record: RRID:SCR_003070
RRID:CVCL_0027
DOI: 10.1021/acsomega.6c06421
Resource: (KCLB Cat# 88065, RRID:CVCL_0027)
Curator: @scibot
SciCrunch record: RRID:CVCL_0027
RRID:AB_2810155
DOI: 10.1021/acsomega.6c06421
Resource: (Thermo Fisher Scientific Cat# MA5-33062, RRID:AB_2810155)
Curator: @scibot
SciCrunch record: RRID:AB_2810155
RRID:AB_143165
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Curator: @scibot
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Curator: @scibot
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Curator: @scibot
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Curator: @scibot
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Curator: @scibot
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SCR_020987
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Curator: @scibot
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Resource: RRID:MMRRC_030406-MU
Curator: @scibot
SciCrunch record: RRID:MMRRC_030406-MU
AB_330248
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AB_2534079
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Curator: @scibot
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AB_2534069
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Curator: @scibot
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Curator: @scibot
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Curator: @scibot
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Curator: @scibot
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Curator: @scibot
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DOI: 10.1016/j.isci.2026.115716
Resource: (Jackson ImmunoResearch Labs Cat# 111-035-144, RRID:AB_2307391)
Curator: @scibot
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Curator: @scibot
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DOI: 10.1016/j.isci.2026.115716
Resource: (Jackson ImmunoResearch Labs Cat# 115-035-068, RRID:AB_2338505)
Curator: @scibot
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Curator: @scibot
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DOI: 10.1007/s00784-026-07198-8
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Curator: @scibot
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DOI: 10.1007/s00784-026-07198-8
Resource: cutadapt (RRID:SCR_011841)
Curator: @scibot
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DOI: 10.1007/s00784-026-07198-8
Resource: CellProfiler Image Analysis Software (RRID:SCR_007358)
Curator: @scibot
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DOI: 10.1007/s00784-026-07198-8
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Curator: @scibot
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DOI: 10.1007/s00784-026-07198-8
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
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RRID:Addgene_109251
DOI: 10.1007/s00449-026-03433-4
Resource: RRID:Addgene_109251
Curator: @scibot
SciCrunch record: RRID:Addgene_109251
RRID:SCR_019060
DOI: 10.1002/hipo.70134
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Curator: @scibot
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DOI: 10.1002/cbdv.71645
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Curator: @scibot
SciCrunch record: RRID:CVCL_0462
Addgene_118014
DOI: 10.1002/advs.77726
Resource: RRID:Addgene_118014
Curator: @scibot
SciCrunch record: RRID:Addgene_118014
Addgene_52961
DOI: 10.1002/advs.77726
Resource: RRID:Addgene_52961
Curator: @scibot
SciCrunch record: RRID:Addgene_52961
Addgene_48138
DOI: 10.1002/advs.77726
Resource: RRID:Addgene_48138
Curator: @scibot
SciCrunch record: RRID:Addgene_48138
Addgene_11801
DOI: 10.1002/advs.77726
Resource: RRID:Addgene_118017
Curator: @bandrow
SciCrunch record: RRID:Addgene_118017
Addgene_171693
DOI: 10.1002/advs.77657
Resource: RRID:Addgene_171693
Curator: @scibot
SciCrunch record: RRID:Addgene_171693
Addgene_171691
DOI: 10.1002/advs.77657
Resource: RRID:Addgene_171691
Curator: @scibot
SciCrunch record: RRID:Addgene_171691
Addgene_112093
DOI: 10.1002/advs.77657
Resource: RRID:Addgene_112093
Curator: @scibot
SciCrunch record: RRID:Addgene_112093
Addgene_171695
DOI: 10.1002/advs.77657
Resource: RRID:Addgene_171695
Curator: @scibot
SciCrunch record: RRID:Addgene_171695
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Resource: (CLS Cat# 300342/p657_SK-OV-3, RRID:CVCL_0532)
Curator: @scibot
SciCrunch record: RRID:CVCL_0532
AB_477629
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Curator: @scibot
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DOI: 10.1002/1878-0261.70333
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Curator: @scibot
SciCrunch record: RRID:AB_143157
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DOI: 10.1002/1878-0261.70333
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Curator: @scibot
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DOI: 10.1002/1878-0261.70333
Resource: (BD Biosciences Cat# 610620, RRID:AB_397952)
Curator: @scibot
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Curator: @scibot
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Curator: @scibot
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Curator: @scibot
SciCrunch record: RRID:AB_627695
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DOI: 10.1002/1878-0261.70333
Resource: RRID:AB_2610739
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SciCrunch record: RRID:AB_2610739
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DOI: 10.1002/1878-0261.70333
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Curator: @scibot
SciCrunch record: RRID:AB_2734686
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DOI: 10.1002/1878-0261.70333
Resource: (Sigma-Aldrich Cat# P2983, RRID:AB_439685)
Curator: @scibot
SciCrunch record: RRID:AB_439685
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DOI: 10.1002/1878-0261.70333
Resource: (Sigma-Aldrich Cat# M4401, RRID:AB_477192)
Curator: @scibot
SciCrunch record: RRID:AB_477192
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DOI: 10.1002/1878-0261.70333
Resource: (Sigma-Aldrich Cat# T7451, RRID:AB_609894)
Curator: @scibot
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DOI: 10.1002/1878-0261.70333
Resource: (Abcam Cat# ab155785, RRID:AB_2818944)
Curator: @scibot
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DOI: 10.1002/1878-0261.70333
Resource: (Cell Signaling Technology Cat# 3674, RRID:AB_2147464)
Curator: @scibot
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DOI: 10.1002/1878-0261.70333
Resource: (Cell Signaling Technology Cat# 3671, RRID:AB_330248)
Curator: @scibot
SciCrunch record: RRID:AB_330248
RRID:MMRRC_071761-JAX
DOI: 10.3389/fgeed.2026.1910209
Resource: RRID:MMRRC_071761-JAX
Curator: @AleksanderDrozdz
SciCrunch record: RRID:MMRRC_071761-JAX
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DOI: 10.3389/fgeed.2026.1910209
Resource: RRID:MMRRC_071760-JAX
Curator: @AleksanderDrozdz
SciCrunch record: RRID:MMRRC_071760-JAX
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DOI: 10.3389/fgeed.2026.1910209
Resource: RRID:MMRRC_071299-MU
Curator: @AleksanderDrozdz
SciCrunch record: RRID:MMRRC_071299-MU
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DOI: 10.3389/fgeed.2026.1910209
Resource: RRID:MMRRC_071298-MU
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SciCrunch record: RRID:MMRRC_071298-MU
Addgene_250565
DOI: 10.1371/journal.ppat.1014540
Resource: RRID:Addgene_250565
Curator: @bandrow
SciCrunch record: RRID:Addgene_250565
In 1960, Los Angeles was still a decidedly white city. By 1980, it no longerwas. What happened in Los Angeles was not uncommon elsewhere, as whiteresidents fled city centers in the latter half of the twentieth century to pursuevisions of the suburban good life.
I find this opening sentence to be very interesting because what we usually see now or from the general consensus that LA is made up of is that it is a very diverse city. There are parts that are heavily Latinx, heavily Korean, majority, White, etc. The history of this city is very important as it serves as one of the biggest cities in the U.S. And engraved in that history can be many roots of what society or education system is built up of today. It has many districts and major colleges and universities of higher education.
Synthèse d'Information : Résultats de PISA 2025 et Leçons pour le Système Éducatif
Ce document de synthèse analyse les résultats du Programme international pour le suivi des acquis des élèves (PISA) 2025 de l'OCDE, présentés conjointement par le National Center on Education and the Economy (NCEE) et l'OCDE.
L'évaluation PISA 2025 a examiné les performances de plus de 760 000 élèves de 15 ans répartis dans 91 pays et économies en sciences, mathématiques, lecture, résolution informatique de problèmes, ainsi que leur utilisation de l'intelligence artificielle (IA) et leurs compétences psycho-sociales (curiosité, persévérance, sentiment d'efficacité personnelle).
Les données PISA révèlent que le déclin des résultats scolaires à l'échelle mondiale a débuté autour de 2012-2013.
L'amélioration relative des États-Unis dans certains classements s'explique par la détérioration plus rapide des performances des autres pays, et non par une hausse nette des compétences des élèves américains.
Toutefois, 22 % des élèves américains de 15 ans ne maîtrisent pas les compétences de base (niveau 2).
Le milieu socio-économique détermine fortement les résultats : les élèves américains défavorisés obtiennent des scores comparables à la moyenne du Kazakhstan, alors que les élèves défavorisés d'Asie de l'Est dépassent les élèves américains les plus riches.
L'utilisation des appareils numériques pour les loisirs (consommation passive sur les réseaux sociaux) détériore les capacités de concentration et d'apprentissage, tandis qu'un usage pédagogique encadré (jusqu'à 3 heures par jour) peut s'avérer bénéfique.
Cependant, cet engagement familial est en déclin mondial entre 2015 et 2025.
L'évaluation PISA mesure la capacité des élèves de 15 ans à appliquer leurs connaissances dans des situations réelles et complexes.
Échantillon : Plus de 760 000 élèves évalués.
Couverture : 91 pays et économies.
Domaines évalués : Sciences, Mathématiques, Compétences en lecture, Résolution informatique de problèmes, Usages scolaires de l'IA, Curiosité, Persévérance et Sentiment d'efficacité personnelle.
L'Asie de l'Est domine les premières places du classement mondial en sciences.
Une différence de 20 points au PISA équivaut environ à une année de scolarité pour un élève de 15 ans.
| Rang / Région | Juridiction / Pays | Écart par rapport à la moyenne OCDE / Remarques | | --- | --- | --- | | 1er | Chine continentale (Pékin, Shanghai, Jiangsu, Zhejiang) | Plus de 100 points au-dessus de la moyenne OCDE (soit 5 ans de scolarité d'avance). | | 2e à 5e | Singapour, Macao, Taipei chinois, Japon | Systèmes d'Asie de l'Est occupant l'ensemble du Top 5. | | Premier non-asiatique | Estonie | Suivie par la Corée du Sud, le Royaume-Uni, le Canada, la Nouvelle-Zélande, l'Australie, la Finlande, puis les États-Unis. |
Le PIB par habitant n'explique qu'environ 50 % des variations de performance entre les nations.
L'autre moitié dépend des politiques éducatives et de l'efficience de l'allocation des ressources.
Progrès rapides dans des pays à ressources limitées :
Turquie : Progression de 18 points depuis 2018, et de 65 points sur dix ans (équivalent à 3 ans de scolarité).
Costa Rica : Gain de 13 points sur les 3 dernières années.
Royaume-Uni : Progression de 12 points.
Efficience des dépenses : Les États-Unis dépensent plus de quatre fois plus par élève que la Turquie, mais obtiennent des résultats similaires.
Au-delà d'un certain niveau de revenu national, l'augmentation du financement ne garantit plus de meilleurs résultats scolaires.
Les États-Unis se caractérisent par une forte polarisation de leurs résultats académiques :
Ces élèves sont capables de manipuler des concepts scientifiques complexes, d'évaluer des preuves et d'identifier les failles d'un raisonnement.
Échec des compétences de base : 22 % des élèves américains de 15 ans n'atteignent pas le niveau 2, le seuil minimal de compétences fondamentales.
Inégalités socio-économiques : Les élèves américains issus des milieux les plus favorisés atteignent des scores proches de la moyenne générale de Taipei chinois.
À l'inverse, les élèves américains défavorisés obtiennent des résultats similaires à la moyenne nationale du Kazakhstan.
En comparaison, les élèves défavorisés au Royaume-Uni ou dans les régions chinoises réussissent mieux que les élèves américains les plus riches.
Malgré des écarts de performance globaux, les États-Unis possèdent des forces structurelles majeures en matière d'engagement et d'aspirations scolaires :
Plusieurs systèmes asiatiques de premier plan (Japon, Chine, Corée du Sud) perdent du terrain car leurs élèves les plus performants n'apprécient pas la science et ne s'y projettent pas.
Aux États-Unis, la tendance est inverse : les élèves associent la science à leur avenir professionnel grâce à un enseignement qui suscite leur plaisir d'apprendre.
Mindset scientifique et esprit critique face à l'IA :
Ce chiffre varie de 10 % en Ouzbékistan à 52 % aux États-Unis, et dépasse 60 % en Italie, en Corée du Sud et en Chine continentale.
Inquiétude majeure : 43 % des élèves dans le monde déclarent que lorsque les preuves scientifiques contredisent le bon sens, ils préfèrent se fier au bon sens.
Dans un monde dominé par des algorithmes d'IA produisant des réponses fluides et fondées sur la plausibilité, cette tendance constitue une vulnérabilité critique.
Le déclin constaté en lecture ne concerne pas seulement la maîtrise de base, mais touche prioritairement les compétences requises dans un environnement numérique saturé d'IA :
Triangulation d'informations provenant de sources multiples.
Distinction explicite entre faits et opinions.
Évaluation critique et réflexion sur l'information.
Mémorisation et intégration de données complexes.
Les taux de réussite aux épreuves PISA ont chuté le plus rapidement dans les tâches nécessitant d'interroger la validité des contenus et de naviguer dans l'incertitude.
Les données PISA révèlent une corrélation inattendue entre la lecture traditionnelle et la maîtrise de la lecture numérique :
Les élèves qui réussissent le mieux la lecture de documents numériques complexes ne sont pas ceux qui passent le plus de temps sur les écrans, mais ceux qui lisent régulièrement des livres imprimés de plus de 100 pages.
Mécanisme cognitif : La lecture de livres longs contraint le cerveau à élaborer une représentation mentale complexe, structurée et continue.
Cette capacité cognitive construite dans le monde analogique est indispensable pour naviguer et faire du sens dans le monde numérique.
L'impact de la technologie sur les performances scolaires dépend fondamentalement de l'intention pédagogique et de la nature de son utilisation :
IMPACT SUR LES PERFORMANCES SCOLAIRES
Élevé ^ | /---\ (Utilisation pour l'APPRENTISSAGE) | / \ Optimale jusqu'à 3h/jour | / --------------------------> Déclin | / |--------+------------------------------------> | \ | \ (Utilisation pour les LOISIRS) | \ Chute dramatique et continue Faible +--------------------------------------------> 0h 1h 2h 3h 4h+ Temps d'écran
Au-delà, l'effet devient négatif.
Cette courbe s'est fortement détériorée par rapport à 2022.
La consommation passive entraîne une atrophie des connexions neuronales, tandis que l'usage créatif et l'effort cognitif stimulent le développement cérébral.
Environ 30 % des élèves américains (et une proportion plus élevée en Amérique du Sud) déclarent ne plus pouvoir se concentrer en classe en raison de l'utilisation d'appareils numériques par leurs camarades.
La proportion d'écoles appliquant des interdictions de téléphones portables est passée de 34 % à plus de 50 %.
Les établissements appliquant une interdiction formelle ou des directives strictes enregistrent une baisse significative des distractions et une amélioration du climat d'apprentissage.
Statistiques d'utilisation aux États-Unis : 15 % des élèves de 15 ans utilisent l'IA quotidiennement pour leurs devoirs ; 22 % supplémentaires l'utilisent une à deux fois par semaine.
Corrélation globale : À l'échelle internationale, plus la fréquence d'utilisation de l'IA par les élèves est élevée dans un pays, plus les scores globaux au PISA tendent à être faibles.
De même, au niveau individuel, les élèves américains non-utilisateurs d'IA obtiennent de meilleurs résultats que les utilisateurs fréquents.
La différence réside dans l'architecture des outils :
Elle sépare la réalisation de la tâche (task performance) de l'apprentissage réel (learning).
L'analyse PISA montre que le soutien familial est l'un des prédicteurs les plus puissants de la réussite scolaire, surpassant les facteurs socio-économiques traditionnels.
« Qu'as-tu fait à l'école aujourd'hui ? ».
Entre 2015 et 2025, les données PISA mettent en évidence une diminution quasi généralisée de l'intérêt porté par les familles à la vie scolaire de leurs enfants.
+-----------------------------------------------------------------------+
| ÉVOLUTION DU RÔLE DE L'ÉDUCATION ET FACTEURS DE DÉCLIN |
+-----------------------------------------------------------------------+
| Modèle Traditionnel (Co-production) -> Modèle Marchand (Consommation) |
| |
| * Parent : Co-producteur de l'éducation -> Client de l'école |
| * Enseignant : Mentor / Guide -> Prestataire de services |
| * Élève : Apprenant actif -> Consommateur de contenus |
| * Distraction technologique : Captation de l'attention des parents |
+-----------------------------------------------------------------------+
Seuls certains systèmes d'Asie de l'Est réussissent à maintenir un niveau très élevé d'engagement des familles dans le processus éducatif.
La perception qu'ont les élèves du soutien apporté par leurs enseignants constitue un prédictur robuste de leurs performances académiques.
Lorsque les enseignants s'intéressent à l'identité de l'élève, à ses aspirations et lui donnent la parole, les résultats scolaires progressent.
Dans plusieurs pays, ce sentiment de soutien enseignant s'est amélioré depuis la dernière évaluation.
À la lumière des données PISA 2025, la table ronde des experts de NCEE et de l'OCDE suggère trois leviers d'action majeurs :
Les dirigeants éducatifs doivent dépasser la simple mesure de la réalisation des devoirs ou des tâches scolaires.
Dans un contexte où l'IA réalise instantanément les devoirs, la valeur de l'éducation réside dans le processus d'apprentissage et l'effort cognitif.
Développer l'autonomie (agency) des élèves et les compétences métacognitives.
Prioriser la capacité à faire du sens à partir d'informations complexes plutôt que l'accès passif aux données.
Le modèle britannique offre des pistes pour réduire la fracture sociale sans sacrifier l'excellence :
Mise en place de financements basés sur des formules équitables (ex: le Pupil Premium au Royaume-Uni).
Transfert d'expertise entre établissements très performants et écoles en difficulté à travers des réseaux organisés (Academy Trusts).
Attribution de temps et de ressources aux enseignants pour agir en tant que mentors et travailleurs sociaux auprès des élèves défavorisés.
Éviter le simple rejet ou la banale distribution d'outils : L'exemple de l'Estonie (AI Leap) montre la nécessité d'un plan national de littératie en IA impliquant élèves, enseignants et dirigeants.
Curations d'outils : Sélectionner des applications IA conçues spécifiquement pour la pédagogie et l'effort cognitif, plutôt que de laisser se généraliser l'usage d'agents conversationnels génériques.
Impliquer les enseignants dans le design : Accorder aux enseignants du temps dédié à la recherche et à l'expérimentation pédagogique des technologies numériques.
En conclusion, bien que les scores académiques PISA révèlent des fragilités structurelles, les données transversales sur la jeunesse mettent en évidence des indicateurs de bien-être et de maturité sociale très favorables :
Hausse de l'empathie, de l'ouverture d'esprit et de l'inclusivité.
Baisse de la violence juvénile, des grossesses précoces et de la consommation de drogues.
Augmentation de la patience, de l'autocontrôle et des scores de QI global.
六大原则
SOILD: - S for Single, every class must take 1 role. - O for Open, open for extension, close for modification - I for Interface, it should be small and specific - L for Liskov, subclass object can replace all parent class object - D for Dependency, Dependency Inverse.
哪几个方面
Polymorphism in Java: 1. overload 2. override 3. interface 4. up-casting and down-casting
编译时多态(重载)
Still, Orange County conservatism has meant, in the built environment, a penchant for privatization. OrangeCounty contains America’s first walled community, Rossmoor; first age-segregated community, LeisureWorld; and first Home Owners Association, in Huntington Beach. Irvine is so privatized that it has one of thelargest HOAs in the nation, the Woodbridge neighborhood with 10,000 homes, 35 parks, 48 pools, and 2manmade lakes. This, too, is connected to the history of race and space. In Orange County, Mondays used111213
It’s interesting to learn about how Orange County’s walled communities and large HOAs grew out of resistance to integrating public pools. It's sad to hear that towns replaced segregated "Mexican Days" at swimming pools, but instead communities simply built private, expensive facilities to keep the same people out. This passage reminds me of the place I used to live when I was younger. We had apartments with other Mexican families there, but when we moved and I wanted to visit again, everything was gone, and it was now a bank. It was very upsetting to see because, although I had made new friends and felt like family there, it felt like it had been taken away from me.
Often, the process begins with a single motive, such as market expansion (on the part of a corporation) or increased access to healthcare (on the part of a nonprofit organization). But usually there is a snowball effect, and globalization becomes a mixed bag of economic, philanthropic, entrepreneurial, and cultural efforts.
This part made me think about how we make financial decisions in real life. People usually think economics is just cold numbers and math, but this shows how much human behavior and emotions actually drive choices. It changed my way of thinking because it proves that logic isn't the only thing moving the market.
What you do not want done to yourself, do not do to others.’”
The idea of the golden rule is something that I feel almost everyone has been told at least once. I dont think anyone would disagree with this idea but in reality many people, especially online is completely ignored, whether its users harassing each other, or companies farming users data and selling it.
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The ends justify the means.
Especially in todays social media world, Consequentialism and the idea that the ends justify the means, I feel could cause more problems like feelings of exclusion or discrimination. To get to point where most people are happy, there will end up being another group that feels unhappy or feels like they did not get heard. this could also lead to that group being discriminated against by the majority and ultimately make a more hostile place to be.
Being able to categorize a source helps you understand the kind of information it contains, which is a big clue to (1) whether might meet one or more of your information needs and (2) where to look for it and similar sources.
Categorizing sources before you even have them could also help with deciding where you want to take your research, if you want it to be more fact- or opinion-heavy.
With so many sources available, the question usually is not whether sources exist for your project but which ones will best meet your information needs.
You could find many related sources and narrow it down as you continue to develop your research. Not all the sources need to work out on the first try.
Once you have your research question, you’ll need information sources to answer it and meet the other information needs of your research project.
First, you need to identify your research question to even start looking for sources
Perhaps if there were an obvious relationship between collegecourses and her career goals,
This is Liz's whole arguement, because she only sees college as worth it if it directly connects / fast tracks her to her career. If it doesn't its a waste of money, which is one way to look at it, education just being a tool to get a job. I get it, especially when money is tight (and it is), but it makes me wonder what we lose in terms of classes that seem useful and leave the other ones out.
I’m this ancient person that’s desperate for an end, or some sortof goal.
She's only 21, but feels ancient. That says alot about how rushed college can feel, because the pressure to have a goal and finish fast makes people feel behind more than they are already especially when they're young.
Even so, she hadtaken “quite a few” psychology classes and found the subject tobe “plain” and “too boring.”
A few boring classes made her reconsider her whole major, thats a lot of pressure just from intro courses. I hope I don't have a class like that but it makes me wonder if its the subject itself or the way its being taught.
only two ex-plained their attendance in terms other than career goals. Thesetwo students were Ruth, a retired accountant, who was takingEn glish courses for plea sure, and Natalie, a recent high schoolgraduate.
It's suprising that only two students weren't in for career related reasons. That shows how dominant the economic motive is, like natalie saying she's there because her mom told ehr to do so, which is relatable but sad. Shes just there because she has to, not because she wants to be.
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